# coding=utf-8
# Copyright 2021 T5 Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Flax T5 model."""

import copy
from typing import Callable, Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from transformers.modeling_flax_outputs import (
    FlaxBaseModelOutput,
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    FlaxSeq2SeqLMOutput,
    FlaxSeq2SeqModelOutput,
)
from transformers.modeling_flax_utils import (
    ACT2FN,
    FlaxPreTrainedModel,
    append_call_sample_docstring,
    append_replace_return_docstrings,
    overwrite_call_docstring,
)
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from transformers import T5Config
# from dataclasses import dataclass, replace
from ml_collections import ConfigDict, FrozenConfigDict


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "google-t5/t5-small"
_CONFIG_FOR_DOC = "FrozenConfigDict"

remat = nn_partitioning.remat


# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = jnp.zeros_like(input_ids)
    shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
    shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)

    shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
    return shifted_input_ids


class FlaxT5LayerNorm(nn.Module):
    hidden_size: int
    dtype: jnp.dtype = jnp.float32
    eps: float = 1e-6
    weight_init: Callable[..., np.ndarray] = jax.nn.initializers.ones

    def setup(self):
        self.weight = self.param("weight", self.weight_init, (self.hidden_size,))

    def __call__(self, hidden_states):
        """
        Construct a layernorm module in the T5 style; No bias and no subtraction of mean.
        """
        # layer norm should always be calculated in float32
        variance = jnp.power(hidden_states.astype("f4"), 2).mean(axis=-1, keepdims=True)
        hidden_states = hidden_states / jnp.sqrt(variance + self.eps)

        return self.weight * hidden_states


class FlaxT5DenseActDense(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        wi_init_std = self.config.initializer_factor * (self.config.d_model**-0.5)
        wo_init_std = self.config.initializer_factor * (self.config.d_ff**-0.5)

        self.wi = nn.Dense(
            self.config.d_ff,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(wi_init_std),
            dtype=self.dtype,
        )
        self.wo = nn.Dense(
            self.config.d_model,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(wo_init_std),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(self.config.dropout_rate)
        self.act = ACT2FN[self.config.dense_act_fn]

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = self.wi(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.wo(hidden_states)
        return hidden_states


class FlaxT5DenseGatedActDense(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        wi_init_std = self.config.initializer_factor * (self.config.d_model**-0.5)
        wo_init_std = self.config.initializer_factor * (self.config.d_ff**-0.5)

        self.wi_0 = nn.Dense(
            self.config.d_ff,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(wi_init_std),
            dtype=self.dtype,
        )
        self.wi_1 = nn.Dense(
            self.config.d_ff,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(wi_init_std),
            dtype=self.dtype,
        )
        self.wo = nn.Dense(
            self.config.d_model,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(wo_init_std),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(self.config.dropout_rate)
        self.act = ACT2FN[self.config.dense_act_fn]

    def __call__(self, hidden_states, deterministic):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.wo(hidden_states)
        return hidden_states


class FlaxT5LayerFF(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        if self.config.is_gated_act:
            self.DenseReluDense = FlaxT5DenseGatedActDense(self.config, dtype=self.dtype)
        else:
            self.DenseReluDense = FlaxT5DenseActDense(self.config, dtype=self.dtype)

        self.layer_norm = FlaxT5LayerNorm(self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype)
        self.dropout = nn.Dropout(self.config.dropout_rate)

    def __call__(self, hidden_states, deterministic=True):
        forwarded_states = self.layer_norm(hidden_states)
        forwarded_states = self.DenseReluDense(forwarded_states, deterministic=deterministic)
        hidden_states = hidden_states + self.dropout(forwarded_states, deterministic=deterministic)
        return hidden_states


class FlaxT5Attention(nn.Module):
    config: FrozenConfigDict
    has_relative_attention_bias: bool = False
    causal: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.relative_attention_num_buckets = self.config.relative_attention_num_buckets
        self.relative_attention_max_distance = self.config.relative_attention_max_distance
        self.d_model = self.config.d_model
        self.key_value_proj_dim = self.config.d_kv
        self.n_heads = self.config.num_heads
        self.dropout = self.config.dropout_rate
        self.inner_dim = self.n_heads * self.key_value_proj_dim

        q_init_std = self.config.initializer_factor * ((self.inner_dim * self.key_value_proj_dim) ** -0.5)
        kv_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
        o_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)

        self.q = nn.Dense(
            self.inner_dim,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(q_init_std),
            dtype=self.dtype,
        )
        self.k = nn.Dense(
            self.inner_dim,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(kv_init_std),
            dtype=self.dtype,
        )
        self.v = nn.Dense(
            self.inner_dim,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(kv_init_std),
            dtype=self.dtype,
        )
        self.o = nn.Dense(
            self.d_model,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(o_init_std),
            dtype=self.dtype,
        )

        if self.has_relative_attention_bias:
            self.relative_attention_bias = nn.Embed(
                self.relative_attention_num_buckets,
                self.n_heads,
                embedding_init=jax.nn.initializers.normal(kv_init_std),
                dtype=self.dtype,
            )

    @staticmethod
    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        """
        Adapted from Mesh Tensorflow:
        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593

        Translate relative position to a bucket number for relative attention. The relative position is defined as
        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
        This should allow for more graceful generalization to longer sequences than the model has been trained on
        """
        relative_buckets = 0
        if bidirectional:
            num_buckets //= 2
            relative_buckets += (relative_position > 0) * num_buckets
            relative_position = jnp.abs(relative_position)
        else:
            relative_position = -jnp.clip(relative_position, a_max=0)
        # now relative_position is in the range [0, inf)

        # half of the buckets are for exact increments in positions
        max_exact = num_buckets // 2
        is_small = relative_position < max_exact

        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
        relative_position_if_large = max_exact + (
            jnp.log(relative_position / max_exact) / jnp.log(max_distance / max_exact) * (num_buckets - max_exact)
        )
        relative_position_if_large = jnp.clip(relative_position_if_large, a_max=num_buckets - 1)

        relative_buckets += jnp.where(is_small, relative_position, relative_position_if_large)

        return relative_buckets.astype("i4")

    def compute_bias(self, query_length, key_length):
        """Compute binned relative position bias"""
        context_position = jnp.arange(query_length, dtype="i4")[:, None]
        memory_position = jnp.arange(key_length, dtype="i4")[None, :]

        relative_position = memory_position - context_position
        relative_position_bucket = self._relative_position_bucket(
            relative_position,
            bidirectional=(not self.causal),
            num_buckets=self.relative_attention_num_buckets,
            max_distance=self.relative_attention_max_distance,
        )

        values = self.relative_attention_bias(relative_position_bucket)
        values = values.transpose((2, 0, 1))[None, :, :, :]
        return values

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.n_heads, self.key_value_proj_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.inner_dim,))

    @nn.compact
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        """
        This function takes projected key, value states from a single input token and concatenates the states to cached
        states from previous steps. This function is slighly adapted from the official Flax repository:
        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
        """
        # detect if we're initializing by absence of existing cache data.
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            # update key, value caches with our new 1d spatial slices
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = jax.lax.dynamic_update_slice(cached_key.value, key, indices)
            value = jax.lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions
            # that have already been generated and cached, not the remaining zero elements.
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask

    def _create_position_bias(
        self, key_states, query_states, attention_mask, init_cache, seq_length, causal_attention_mask_shift
    ):
        cache_is_filled = self.causal and self.has_variable("cache", "cached_key") and (not init_cache)
        key_length = key_states.shape[1]
        query_length = key_length if cache_is_filled else query_states.shape[1]

        if self.has_relative_attention_bias:
            position_bias = self.compute_bias(query_length, key_length)
        elif attention_mask is not None:
            position_bias = jnp.zeros_like(attention_mask)
        else:
            position_bias = jnp.zeros((1, self.n_heads, query_length, key_length), dtype=self.dtype)

        # if key and values are already calculated, only the last query position bias should be taken
        if cache_is_filled:
            max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
            position_bias = jax.lax.dynamic_slice(
                position_bias,
                (0, 0, causal_attention_mask_shift, 0),
                (1, self.n_heads, seq_length, max_decoder_length),
            )
        return position_bias

    def __call__(
        self,
        hidden_states,
        attention_mask=None,
        key_value_states=None,
        position_bias=None,
        use_cache=False,
        output_attentions=False,
        deterministic=True,
        init_cache=False,
    ):
        """
        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
        """
        batch_size, seq_length = hidden_states.shape[:2]

        # q, k, v projections
        query_states = self.q(hidden_states)  # (batch_size, n_heads, seq_length, dim_per_head)
        key_states = self.k(hidden_states) if key_value_states is None else self.k(key_value_states)
        value_states = self.v(hidden_states) if key_value_states is None else self.v(key_value_states)

        # reshape to (batch_size, seq_length, n_heads, head_dim)
        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        # counter-act scaling in dot_product_attention_weights function
        query_states *= jnp.sqrt(query_states.shape[-1])

        # for fast decoding causal attention mask should be shifted
        causal_attention_mask_shift = (
            self.variables["cache"]["cache_index"] if (self.has_variable("cache", "cached_key") and self.causal) else 0
        )
        # create causal attention_mask; attention_mask has to be defined when model is causal
        if self.causal:
            causal_attention_mask = make_causal_mask(attention_mask, dtype="bool")

            # fast decoding for generate requires special attention_mask
            if self.has_variable("cache", "cached_key"):
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_attention_mask = jax.lax.dynamic_slice(
                    causal_attention_mask,
                    (0, 0, causal_attention_mask_shift, 0),
                    (1, 1, seq_length, max_decoder_length),
                )

            # broadcast causal attention mask & attention mask to fit for merge
            causal_attention_mask = jnp.broadcast_to(
                causal_attention_mask, (batch_size,) + causal_attention_mask.shape[1:]
            )
            attention_mask = jnp.broadcast_to(
                jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_attention_mask.shape
            )
            attention_mask = combine_masks(attention_mask, causal_attention_mask)
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        # replace masked positions with -10_000
        if attention_mask is not None:
            mask_value = jnp.finfo(self.dtype).min
            attention_mask = jax.lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, mask_value).astype(self.dtype),
            )

        if position_bias is None:
            # compute position bias (only for first layer)
            position_bias = self._create_position_bias(
                key_states, query_states, attention_mask, init_cache, seq_length, causal_attention_mask_shift
            )

            if attention_mask is not None:
                position_bias = position_bias + attention_mask

        # create dropout rng
        dropout_rng = None
        if not deterministic and self.dropout > 0.0:
            dropout_rng = self.make_rng("dropout")

        # Softmax(QK^T)
        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=position_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.dropout,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
        )

        # multiply with value states
        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)

        # bring back to (batch_size, seq_length, d_model)
        attn_output = self._merge_heads(attn_output)

        # apply output matrix
        attn_output = self.o(attn_output)

        outputs = (attn_output, position_bias)

        if output_attentions:
            outputs = outputs + (attn_weights,)

        return outputs


class FlaxT5LayerSelfAttention(nn.Module):
    config: FrozenConfigDict
    has_relative_attention_bias: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.SelfAttention = FlaxT5Attention(
            self.config,
            has_relative_attention_bias=self.has_relative_attention_bias,
            causal=self.config.causal,
            dtype=self.dtype,
        )
        self.layer_norm = FlaxT5LayerNorm(self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype)
        self.dropout = nn.Dropout(self.config.dropout_rate)

    def __call__(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        output_attentions=False,
        deterministic=True,
        init_cache=False,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(
            normed_hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            output_attentions=output_attentions,
            deterministic=deterministic,
            init_cache=init_cache,
        )
        hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
        return outputs


class FlaxT5LayerCrossAttention(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.EncDecAttention = FlaxT5Attention(
            self.config, has_relative_attention_bias=False, causal=False, dtype=self.dtype
        )
        self.layer_norm = FlaxT5LayerNorm(self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype)
        self.dropout = nn.Dropout(self.config.dropout_rate)

    def __call__(
        self,
        hidden_states,
        key_value_states,
        attention_mask=None,
        position_bias=None,
        output_attentions=False,
        deterministic=True,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(
            normed_hidden_states,
            attention_mask=attention_mask,
            key_value_states=key_value_states,
            position_bias=position_bias,
            output_attentions=output_attentions,
        )
        hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
        return outputs


class FlaxT5Block(nn.Module):
    config: FrozenConfigDict
    has_relative_attention_bias: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.causal = self.config.causal
        self.layer = (
            FlaxT5LayerSelfAttention(
                self.config,
                has_relative_attention_bias=self.has_relative_attention_bias,
                name=str(0),
                dtype=self.dtype,
            ),
        )
        feed_forward_index = 1
        if self.causal:
            self.layer += (FlaxT5LayerCrossAttention(self.config, name=str(1), dtype=self.dtype),)
            feed_forward_index += 1

        self.layer += (FlaxT5LayerFF(self.config, name=str(feed_forward_index), dtype=self.dtype),)

    def __call__(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        encoder_decoder_position_bias=None,
        output_attentions=False,
        return_dict=True,
        deterministic=True,
        init_cache=False,
    ):
        self_attention_outputs = self.layer[0](
            hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            output_attentions=output_attentions,
            deterministic=deterministic,
            init_cache=init_cache,
        )
        hidden_states = self_attention_outputs[0]
        attention_outputs = self_attention_outputs[1:]  # Keep self-attention outputs and relative position weights

        do_cross_attention = self.causal and encoder_hidden_states is not None
        if do_cross_attention:
            cross_attention_outputs = self.layer[1](
                hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
                position_bias=encoder_decoder_position_bias,
                output_attentions=output_attentions,
                deterministic=deterministic,
            )
            hidden_states = cross_attention_outputs[0]

            # Keep cross-attention outputs and relative position weights
            attention_outputs = attention_outputs + cross_attention_outputs[1:]

        # Apply Feed Forward layer
        hidden_states = self.layer[-1](hidden_states, deterministic=deterministic)

        outputs = (hidden_states,)

        outputs = outputs + attention_outputs

        # returns hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights),
        # (cross-attention position bias), (cross-attention weights)
        return outputs


class FlaxT5LayerCollection(nn.Module):
    config: FrozenConfigDict
    has_relative_attention_bias: bool
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.layer = FlaxT5Block(
            self.config, has_relative_attention_bias=self.has_relative_attention_bias, dtype=self.dtype
        )

    def __call__(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        encoder_decoder_position_bias=None,
        output_attentions=False,
        deterministic=True,
        init_cache=False,
    ):
        return self.layer(
            hidden_states,
            attention_mask=attention_mask,
            position_bias=position_bias,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            encoder_decoder_position_bias=encoder_decoder_position_bias,
            output_attentions=output_attentions,
            deterministic=deterministic,
            init_cache=init_cache,
        )


class FlaxT5BlockCollection(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.causal = self.config.causal
        if self.gradient_checkpointing:
            FlaxT5CheckpointLayer = remat(FlaxT5LayerCollection, static_argnums=(6, 7, 8))
            self.blocks = [
                FlaxT5CheckpointLayer(
                    self.config,
                    has_relative_attention_bias=(i == 0),
                    dtype=self.dtype,
                    name=str(i),
                )
                for i in range(self.config.num_layers)
            ]
        else:
            self.blocks = [
                FlaxT5LayerCollection(
                    self.config,
                    has_relative_attention_bias=(i == 0),
                    dtype=self.dtype,
                    name=str(i),
                )
                for i in range(self.config.num_layers)
            ]

    def __call__(
        self,
        hidden_states=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        deterministic: bool = True,
        init_cache: bool = False,
    ):
        # Prepare head mask if needed
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and self.causal) else None
        position_bias = None
        encoder_decoder_position_bias = None

        for i, layer_module in enumerate(self.blocks):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(
                hidden_states,
                attention_mask,
                position_bias,
                encoder_hidden_states,
                encoder_attention_mask,
                encoder_decoder_position_bias,
                output_attentions,
                deterministic,
                init_cache,
            )

            hidden_states = layer_outputs[0]

            # We share the position biases between the layers - the first layer store them
            # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
            # (cross-attention position bias), (cross-attention weights)
            position_bias = layer_outputs[1]

            if self.causal and encoder_hidden_states is not None:
                encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[2],)
                if self.causal:
                    all_cross_attentions = all_cross_attentions + (layer_outputs[4],)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


class FlaxT5Stack(nn.Module):
    config: FrozenConfigDict
    embed_tokens: nn.Embed
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.causal = self.config.causal

        self.block = FlaxT5BlockCollection(
            self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.final_layer_norm = FlaxT5LayerNorm(
            self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
        )
        self.dropout = nn.Dropout(self.config.dropout_rate)

    def __call__(
        self,
        input_ids=None,
        attention_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        deterministic: bool = True,
        init_cache: bool = False,
    ):
        hidden_states = self.embed_tokens(input_ids)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)

        outputs = self.block(
            hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            deterministic=deterministic,
            init_cache=init_cache,
        )

        hidden_states = outputs[0]

        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)

        # Add last layer
        all_hidden_states = None

        if output_hidden_states:
            all_hidden_states = outputs.hidden_states
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            if output_hidden_states:
                return (
                    hidden_states,
                    all_hidden_states,
                ) + outputs[2:]
            return (hidden_states,) + outputs[1:]

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


T5_ENCODE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
            should be able to pad the inputs on both the right and the left.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for detail.

            To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
        attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

T5_DECODE_INPUTS_DOCSTRING = r"""
    Args:
        decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            For training, `decoder_input_ids` should be provided.
        encoder_outputs (`tuple(tuple(jnp.ndarray)`):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.

            If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
            paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
        past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
            Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
            auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


T5_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
            should be able to pad the inputs on both the right and the left.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for detail.

            [What are input IDs?](../glossary#input-ids)

            To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
        attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
            is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
            Training](./t5#training).
        decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        encoder_outputs (`tuple(tuple(jnp.ndarray)`, *optional*):
            Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
            the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(jnp.ndarray))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.


        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


class FlaxT5PreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = T5Config
    base_model_prefix = "transformer"
    module_class: nn.Module = None

    def __init__(
        self,
        config: T5Config,
        input_shape: Tuple[int] = (1, 1),
        seed: int = 0,
        dtype: jnp.dtype = jnp.bfloat16,
        _do_init: bool = True,
        gradient_checkpointing: bool = False,
        **kwargs,
    ):
        # we convert the T5Config mutable object into a FrozenConfigDict
        # we pop these 2 things because we know they are troublesome
        # from manual testing making a ConfigDict out of the config
        config_dict = copy.deepcopy(config.to_dict())
        config_dict.pop('architectures')
        config_dict.pop('id2label')
        config_dict = FrozenConfigDict(config_dict)
        # all modules take in a FrozenConfigDict
        module = self.module_class(config=config_dict, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        # but FlaxPreTrainedModel still takes the T5Config object
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    def enable_gradient_checkpointing(self):
        self._module = self.module_class(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=True,
        )

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")

        attention_mask = jnp.ones_like(input_ids)
        args = [input_ids, attention_mask]
        if self.module_class not in [FlaxT5EncoderModule]:
            decoder_input_ids = jnp.ones_like(input_ids)
            decoder_attention_mask = jnp.ones_like(input_ids)
            args.extend([decoder_input_ids, decoder_attention_mask])

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        random_params = self.module.init(
            rngs,
            *args,
        )["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
    def __call__(
        self,
        input_ids: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        decoder_input_ids: jnp.ndarray = None,
        decoder_attention_mask: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if decoder_input_ids is None:
            raise ValueError(
                "Make sure to provide both `input_ids` and `decoder_input_ids`. `decoder_input_ids` is not passed"
                " here."
            )

        # prepare encoder inputs
        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        # prepare decoder inputs
        if decoder_attention_mask is None:
            decoder_attention_mask = jnp.ones_like(decoder_input_ids)

        # Handle any PRNG if needed
        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}

        return self.module.apply(
            {"params": params or self.params},
            input_ids=jnp.array(input_ids, dtype="i4"),
            attention_mask=jnp.array(attention_mask, dtype="i4"),
            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
        )

    def init_cache(self, batch_size, max_length, encoder_outputs):
        r"""
        Args:
            batch_size (`int`):
                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
            max_length (`int`):
                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                cache.
            encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
                `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
                `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
                is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
                cross-attention of the decoder.
        """
        # init input variables to retrieve cache
        decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        decoder_attention_mask = jnp.ones_like(decoder_input_ids)

        def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
            decoder_module = module._get_decoder_module()
            return decoder_module(
                decoder_input_ids,
                decoder_attention_mask,
                **kwargs,
            )

        init_variables = self.module.init(
            jax.random.PRNGKey(0),
            decoder_input_ids=decoder_input_ids,
            decoder_attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            init_cache=True,
            method=_decoder_forward,  # we only need to call the decoder to init the cache
        )
        return unfreeze(init_variables["cache"])

    @add_start_docstrings(T5_ENCODE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=FrozenConfigDict)
    def encode(
        self,
        input_ids: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration

        >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
        >>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")

        >>> text = "My friends are cool but they eat too many carbs."
        >>> inputs = tokenizer(text, return_tensors="np")
        >>> encoder_outputs = model.encode(**inputs)
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        def _encoder_forward(module, input_ids, attention_mask, **kwargs):
            encode_module = module._get_encoder_module()
            return encode_module(input_ids, attention_mask, **kwargs)

        return self.module.apply(
            {"params": params or self.params},
            input_ids=jnp.array(input_ids, dtype="i4"),
            attention_mask=jnp.array(attention_mask, dtype="i4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
            method=_encoder_forward,
        )

    @add_start_docstrings(T5_DECODE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=FrozenConfigDict)
    def decode(
        self,
        decoder_input_ids,
        encoder_outputs,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        decoder_attention_mask: Optional[jnp.ndarray] = None,
        past_key_values: dict = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration
        >>> import jax.numpy as jnp

        >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
        >>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")

        >>> text = "My friends are cool but they eat too many carbs."
        >>> inputs = tokenizer(text, return_tensors="np")
        >>> encoder_outputs = model.encode(**inputs)

        >>> decoder_start_token_id = model.config.decoder_start_token_id
        >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

        >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
        >>> logits = outputs.logits
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        encoder_hidden_states = encoder_outputs[0]
        if encoder_attention_mask is None:
            batch_size, sequence_length = encoder_hidden_states.shape[:2]
            encoder_attention_mask = jnp.ones((batch_size, sequence_length))

        batch_size, sequence_length = decoder_input_ids.shape
        if decoder_attention_mask is None:
            decoder_attention_mask = jnp.ones((batch_size, sequence_length))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
        # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
        # it can be changed by FlaxT5Attention module
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else:
            mutable = False

        def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
            decoder_module = module._get_decoder_module()
            return decoder_module(
                decoder_input_ids,
                decoder_attention_mask,
                **kwargs,
            )

        outputs = self.module.apply(
            inputs,
            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
            mutable=mutable,
            method=_decoder_forward,
        )

        # add updated cache to model output
        if past_key_values is not None and return_dict:
            outputs, past = outputs
            outputs["past_key_values"] = unfreeze(past["cache"])
            return outputs
        elif past_key_values is not None and not return_dict:
            outputs, past = outputs
            outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]

        return outputs


T5_START_DOCSTRING = r"""
    The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
    Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
    Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
    text-to-text denoising generative setting.

    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a Flax Linen
    [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
    regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        config ([`FrozenConfigDict`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
            `jax.numpy.bfloat16` (on TPUs).

            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
            specified all the computation will be performed with the given `dtype`.

            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
            parameters.**

            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
            [`~FlaxPreTrainedModel.to_bf16`].
"""


@add_start_docstrings(
    "The bare T5 Model transformer outputting raw hidden-stateswithout any specific head on top.",
    T5_START_DOCSTRING,
)
class FlaxT5Module(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def _get_encoder_module(self):
        return self.encoder

    def _get_decoder_module(self):
        return self.decoder

    def setup(self):
        self.shared = nn.Embed(
            self.config.vocab_size,
            self.config.d_model,
            embedding_init=jax.nn.initializers.normal(self.config.initializer_factor * 1.0),
            dtype=self.dtype,
        )

        encoder_config = copy.deepcopy(self.config)
        # unfreeze
        encoder_config = ConfigDict(encoder_config)
        encoder_config.causal = False
        # freeze
        encoder_config = FrozenConfigDict(encoder_config)
        self.encoder = FlaxT5Stack(
            encoder_config,
            embed_tokens=self.shared,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

        decoder_config = copy.deepcopy(self.config)
        # unfreeze
        decoder_config = ConfigDict(encoder_config)
        decoder_config.causal = True
        decoder_config.num_layers = self.config.num_decoder_layers
        # freeze
        decoder_config = FrozenConfigDict(encoder_config)
        self.decoder = FlaxT5Stack(
            decoder_config,
            embed_tokens=self.shared,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

    def __call__(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        encoder_outputs=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        deterministic: bool = True,
    ):
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # Encode if needed (training, first prediction pass)
        encoder_outputs = self.encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return FlaxSeq2SeqModelOutput(
            last_hidden_state=decoder_outputs.last_hidden_state,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )


class FlaxT5Model(FlaxT5PreTrainedModel):
    module_class = FlaxT5Module


append_call_sample_docstring(FlaxT5Model, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)

FLAX_T5_MODEL_DOCSTRING = """
    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, FlaxT5Model

    >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
    >>> model = FlaxT5Model.from_pretrained("google-t5/t5-small")

    >>> input_ids = tokenizer(
    ...     "Studies have been shown that owning a dog is good for you", return_tensors="np"
    ... ).input_ids
    >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="np").input_ids

    >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
    >>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
    >>> decoder_input_ids = model._shift_right(decoder_input_ids)

    >>> # forward pass
    >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
    >>> last_hidden_states = outputs.last_hidden_state
    ```
"""


overwrite_call_docstring(FlaxT5Model, T5_INPUTS_DOCSTRING + FLAX_T5_MODEL_DOCSTRING)
append_replace_return_docstrings(FlaxT5Model, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)


@add_start_docstrings(
    "The bare T5 Model transformer outputting encoder's raw hidden-states without any specific head on top.",
    T5_START_DOCSTRING,
)
class FlaxT5EncoderModule(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.shared = nn.Embed(
            self.config.vocab_size,
            self.config.d_model,
            embedding_init=jax.nn.initializers.normal(self.config.initializer_factor * 1.0),
            dtype=self.dtype,
        )

        encoder_config = copy.deepcopy(self.config)
        # unfreeze
        encoder_config = ConfigDict(encoder_config)
        encoder_config.is_decoder = False
        encoder_config.is_encoder_decoder = False
        encoder_config.causal = False
        # freeze
        encoder_config = FrozenConfigDict(encoder_config)

        self.encoder = FlaxT5Stack(
            encoder_config,
            embed_tokens=self.shared,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

    def __call__(
        self,
        input_ids=None,
        attention_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict: bool = True,
        deterministic: bool = True,
    ):
        # Encode if needed (training, first prediction pass)
        encoder_outputs = self.encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        return encoder_outputs


class FlaxT5EncoderModel(FlaxT5PreTrainedModel):
    module_class = FlaxT5EncoderModule

    @add_start_docstrings_to_model_forward(T5_ENCODE_INPUTS_DOCSTRING)
    def __call__(
        self,
        input_ids: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # prepare encoder inputs
        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        # Handle any PRNG if needed
        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}

        return self.module.apply(
            {"params": params or self.params},
            input_ids=jnp.array(input_ids, dtype="i4"),
            attention_mask=jnp.array(attention_mask, dtype="i4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
        )


@add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING)
class FlaxT5ForConditionalGenerationModule(nn.Module):
    config: FrozenConfigDict
    dtype: jnp.dtype = jnp.bfloat16  # the dtype of the computation
    gradient_checkpointing: bool = False

    def _get_encoder_module(self):
        return self.encoder

    def _get_decoder_module(self):
        return self.decoder

    def setup(self):
        self.model_dim = self.config.d_model

        self.shared = nn.Embed(
            self.config.vocab_size,
            self.config.d_model,
            embedding_init=jax.nn.initializers.normal(self.config.initializer_factor),
            dtype=self.dtype,
        )

        encoder_config = copy.deepcopy(self.config)
        # unfreeze
        encoder_config = ConfigDict(encoder_config)
        encoder_config.causal = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        # freeze
        encoder_config = FrozenConfigDict(encoder_config)

        self.encoder = FlaxT5Stack(
            encoder_config, self.shared, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )

        decoder_config = copy.deepcopy(self.config)
        # unfreeze
        decoder_config = ConfigDict(decoder_config)
        decoder_config.causal = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = self.config.num_decoder_layers
        # freeze
        decoder_config = FrozenConfigDict(decoder_config)

        self.decoder = FlaxT5Stack(
            decoder_config, self.shared, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )

        self.lm_head = nn.Dense(
            self.config.vocab_size,
            use_bias=False,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_factor),
            dtype=self.dtype,
        )

    def __call__(
        self,
        input_ids=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        encoder_outputs=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        deterministic: bool = True,
    ):
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # Encode
        encoder_outputs = self.encoder(
            input_ids=input_ids,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        hidden_states = encoder_outputs[0]

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=deterministic,
        )

        sequence_output = decoder_outputs[0]

        if self.config.tie_word_embeddings:
            # Rescale output before projecting on vocab
            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
            sequence_output = sequence_output * (self.model_dim**-0.5)

        if self.config.tie_word_embeddings:
            shared_embedding = self.shared.variables["params"]["embedding"]
            lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, sequence_output)
        else:
            lm_logits = self.lm_head(sequence_output)

        if not return_dict:
            return (lm_logits,) + decoder_outputs[1:] + encoder_outputs

        return FlaxSeq2SeqLMOutput(
            logits=lm_logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )


class FlaxT5ForConditionalGeneration(FlaxT5PreTrainedModel):
    module_class = FlaxT5ForConditionalGenerationModule

    @add_start_docstrings(T5_DECODE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=FrozenConfigDict)
    def decode(
        self,
        decoder_input_ids,
        encoder_outputs,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        decoder_attention_mask: Optional[jnp.ndarray] = None,
        past_key_values: dict = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration
        >>> import jax.numpy as jnp

        >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
        >>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")

        >>> text = "summarize: My friends are cool but they eat too many carbs."
        >>> inputs = tokenizer(text, return_tensors="np")
        >>> encoder_outputs = model.encode(**inputs)

        >>> decoder_start_token_id = model.config.decoder_start_token_id
        >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

        >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
        >>> logits = outputs.logits
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        encoder_hidden_states = encoder_outputs[0]
        if encoder_attention_mask is None:
            batch_size, sequence_length = encoder_hidden_states.shape[:2]
            encoder_attention_mask = jnp.ones((batch_size, sequence_length))

        batch_size, sequence_length = decoder_input_ids.shape
        if decoder_attention_mask is None:
            decoder_attention_mask = jnp.ones((batch_size, sequence_length))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
        # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
        # it can be changed by FlaxT5Attention module
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else:
            mutable = False

        def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
            decoder_module = module._get_decoder_module()
            decoder_outputs = decoder_module(
                decoder_input_ids,
                decoder_attention_mask,
                **kwargs,
            )

            sequence_output = decoder_outputs[0]

            if self.config.tie_word_embeddings:
                # Rescale output before projecting on vocab
                # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
                sequence_output = sequence_output * (self.config.d_model**-0.5)

            if self.config.tie_word_embeddings:
                shared_embedding = module.shared.variables["params"]["embedding"]
                lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, sequence_output)
            else:
                lm_logits = module.lm_head(sequence_output)

            return lm_logits, decoder_outputs

        outputs = self.module.apply(
            inputs,
            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
            mutable=mutable,
            method=_decoder_forward,
        )

        if past_key_values is None:
            lm_logits, decoder_outputs = outputs
        else:
            (lm_logits, decoder_outputs), past = outputs

        if return_dict:
            outputs = FlaxCausalLMOutputWithCrossAttentions(
                logits=lm_logits,
                hidden_states=decoder_outputs.hidden_states,
                attentions=decoder_outputs.attentions,
                cross_attentions=decoder_outputs.cross_attentions,
            )
        else:
            outputs = (lm_logits,) + decoder_outputs[1:]

        # add updated cache to model output
        if past_key_values is not None and return_dict:
            outputs["past_key_values"] = unfreeze(past["cache"])
            return outputs
        elif past_key_values is not None and not return_dict:
            outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]

        return outputs

    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        max_length,
        attention_mask: Optional[jax.Array] = None,
        decoder_attention_mask: Optional[jax.Array] = None,
        encoder_outputs=None,
        **kwargs,
    ):
        # initializing the cache
        batch_size, seq_length = decoder_input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
        # But since the decoder uses a causal mask, those positions are masked anyways.
        # Thus we can create a single static attention_mask here, which is more efficient for compilation
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if decoder_attention_mask is not None:
            extended_attention_mask = jax.lax.dynamic_update_slice(
                extended_attention_mask, decoder_attention_mask, (0, 0)
            )

        return {
            "past_key_values": past_key_values,
            "encoder_outputs": encoder_outputs,
            "encoder_attention_mask": attention_mask,
            "decoder_attention_mask": extended_attention_mask,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        return model_kwargs


FLAX_T5_CONDITIONAL_GENERATION_DOCSTRING = """
    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration

    >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
    >>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")

    >>> ARTICLE_TO_SUMMARIZE = "summarize: My friends are cool but they eat too many carbs."
    >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], return_tensors="np")

    >>> # Generate Summary
    >>> summary_ids = model.generate(inputs["input_ids"]).sequences
    >>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False))
    ```
"""


overwrite_call_docstring(
    FlaxT5ForConditionalGeneration, T5_INPUTS_DOCSTRING + FLAX_T5_CONDITIONAL_GENERATION_DOCSTRING
)
append_replace_return_docstrings(
    FlaxT5ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
)