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Grounded-Segment-Anything / transformers_4_35_0 /models /blenderbot_small /modeling_flax_blenderbot_small.py

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	# coding=utf-8
	# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved.
	#
	# 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 BlenderbotSmall model."""


	import math
	import random
	from functools import partial
	from typing import Callable, Optional, Tuple

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

	from ...modeling_flax_outputs import (
	FlaxBaseModelOutput,
	FlaxBaseModelOutputWithPastAndCrossAttentions,
	FlaxCausalLMOutputWithCrossAttentions,
	FlaxSeq2SeqLMOutput,
	FlaxSeq2SeqModelOutput,
	)
	from ...modeling_flax_utils import (
	ACT2FN,
	FlaxPreTrainedModel,
	append_call_sample_docstring,
	append_replace_return_docstrings,
	overwrite_call_docstring,
	)
	from ...utils import add_start_docstrings, logging, replace_return_docstrings
	from .configuration_blenderbot_small import BlenderbotSmallConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "facebook/blenderbot_small-90M"
	_CONFIG_FOR_DOC = "BlenderbotSmallConfig"

	BLENDERBOT_SMALL_START_DOCSTRING = r"""
	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 ([`BlenderbotSmallConfig`]): 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`].
	"""

	BLENDERBOT_SMALL_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

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

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

	For translation and summarization training, `decoder_input_ids` should be provided. If no
	`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
	for denoising pre-training following the paper.
	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.
	position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.
	decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
	range `[0, config.max_position_embeddings - 1]`.
	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.
	"""


	BLENDERBOT_SMALL_ENCODE_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
	it.

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

	[What are input IDs?](../glossary#input-ids)
	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)
	position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.
	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.
	"""

	BLENDERBOT_SMALL_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 translation and summarization training, `decoder_input_ids` should be provided. If no
	`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
	for denoising pre-training following the paper.
	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.
	decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, optional):
	Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
	range `[0, config.max_position_embeddings - 1]`.
	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.
	"""


	# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
	def shift_tokens_right(input_ids: jnp.array, 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


	# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->BlenderbotSmall
	class FlaxBlenderbotSmallAttention(nn.Module):
	config: BlenderbotSmallConfig
	embed_dim: int
	num_heads: int
	dropout: float = 0.0
	causal: bool = False
	bias: bool = True
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

	def setup(self) -> None:
	self.head_dim = self.embed_dim // self.num_heads
	if self.head_dim * self.num_heads != self.embed_dim:
	raise ValueError(
	f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
	f" and `num_heads`: {self.num_heads})."
	)

	dense = partial(
	nn.Dense,
	self.embed_dim,
	use_bias=self.bias,
	dtype=self.dtype,
	kernel_init=jax.nn.initializers.normal(self.config.init_std),
	)

	self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
	self.out_proj = dense()

	self.dropout_layer = nn.Dropout(rate=self.dropout)

	if self.causal:
	self.causal_mask = make_causal_mask(
	jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
	)

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

	def _merge_heads(self, hidden_states):
	return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_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 = lax.dynamic_update_slice(cached_key.value, key, indices)
	value = 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 __call__(
	self,
	hidden_states: jnp.ndarray,
	key_value_states: Optional[jnp.ndarray] = None,
	attention_mask: Optional[jnp.ndarray] = None,
	init_cache: bool = False,
	deterministic: bool = True,
	) -> Tuple[jnp.ndarray]:
	"""Input shape: Batch x Time x Channel"""

	# if key_value_states are provided this layer is used as a cross-attention layer
	# for the decoder
	is_cross_attention = key_value_states is not None
	batch_size = hidden_states.shape[0]

	# get query proj
	query_states = self.q_proj(hidden_states)
	# get key, value proj
	if is_cross_attention:
	# cross_attentions
	key_states = self.k_proj(key_value_states)
	value_states = self.v_proj(key_value_states)
	else:
	# self_attention
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	query_states = self._split_heads(query_states)
	key_states = self._split_heads(key_states)
	value_states = self._split_heads(value_states)

	# handle cache prepare causal attention mask
	if self.causal:
	query_length, key_length = query_states.shape[1], key_states.shape[1]
	if self.has_variable("cache", "cached_key"):
	mask_shift = self.variables["cache"]["cache_index"]
	max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
	causal_mask = lax.dynamic_slice(
	self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
	)
	else:
	causal_mask = self.causal_mask[:, :, :query_length, :key_length]
	causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

	# combine masks if needed
	if attention_mask is not None and self.causal:
	attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
	attention_mask = combine_masks(attention_mask, causal_mask)
	elif self.causal:
	attention_mask = causal_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
	)

	# Convert the boolean attention mask to an attention bias.
	if attention_mask is not None:
	# attention mask in the form of attention bias
	attention_bias = lax.select(
	attention_mask > 0,
	jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
	jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
	)
	else:
	attention_bias = None

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

	attn_weights = dot_product_attention_weights(
	query_states,
	key_states,
	bias=attention_bias,
	dropout_rng=dropout_rng,
	dropout_rate=self.dropout,
	broadcast_dropout=True,
	deterministic=deterministic,
	dtype=self.dtype,
	precision=None,
	)

	attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
	attn_output = self._merge_heads(attn_output)
	attn_output = self.out_proj(attn_output)

	return attn_output, attn_weights


	# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayer with Bart->BlenderbotSmall
	class FlaxBlenderbotSmallEncoderLayer(nn.Module):
	config: BlenderbotSmallConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self) -> None:
	self.embed_dim = self.config.d_model
	self.self_attn = FlaxBlenderbotSmallAttention(
	config=self.config,
	embed_dim=self.embed_dim,
	num_heads=self.config.encoder_attention_heads,
	dropout=self.config.attention_dropout,
	dtype=self.dtype,
	)
	self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
	self.dropout_layer = nn.Dropout(rate=self.config.dropout)
	self.activation_fn = ACT2FN[self.config.activation_function]
	self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
	self.fc1 = nn.Dense(
	self.config.encoder_ffn_dim,
	dtype=self.dtype,
	kernel_init=jax.nn.initializers.normal(self.config.init_std),
	)
	self.fc2 = nn.Dense(
	self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
	)
	self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

	def __call__(
	self,
	hidden_states: jnp.ndarray,
	attention_mask: jnp.ndarray,
	output_attentions: bool = True,
	deterministic: bool = True,
	) -> Tuple[jnp.ndarray]:
	residual = hidden_states
	hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)

	hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
	hidden_states = residual + hidden_states
	hidden_states = self.self_attn_layer_norm(hidden_states)

	residual = hidden_states
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
	hidden_states = self.fc2(hidden_states)
	hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
	hidden_states = residual + hidden_states
	hidden_states = self.final_layer_norm(hidden_states)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->BlenderbotSmall
	class FlaxBlenderbotSmallEncoderLayerCollection(nn.Module):
	config: BlenderbotSmallConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

	def setup(self):
	self.layers = [
	FlaxBlenderbotSmallEncoderLayer(self.config, name=str(i), dtype=self.dtype)
	for i in range(self.config.encoder_layers)
	]
	self.layerdrop = self.config.encoder_layerdrop

	def __call__(
	self,
	hidden_states,
	attention_mask,
	deterministic: bool = True,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	all_attentions = () if output_attentions else None
	all_hidden_states = () if output_hidden_states else None

	for encoder_layer in self.layers:
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)
	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	dropout_probability = random.uniform(0, 1)
	if not deterministic and (dropout_probability < self.layerdrop): # skip the layer
	layer_outputs = (None, None)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	output_attentions,
	deterministic,
	)
	hidden_states = layer_outputs[0]
	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	outputs = (hidden_states, all_hidden_states, all_attentions)

	if not return_dict:
	return tuple(v for v in outputs if v is not None)

	return FlaxBaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
	)


	# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayer with Bart->BlenderbotSmall
	class FlaxBlenderbotSmallDecoderLayer(nn.Module):
	config: BlenderbotSmallConfig
	dtype: jnp.dtype = jnp.float32

	def setup(self) -> None:
	self.embed_dim = self.config.d_model
	self.self_attn = FlaxBlenderbotSmallAttention(
	config=self.config,
	embed_dim=self.embed_dim,
	num_heads=self.config.decoder_attention_heads,
	dropout=self.config.attention_dropout,
	causal=True,
	dtype=self.dtype,
	)
	self.dropout_layer = nn.Dropout(rate=self.config.dropout)
	self.activation_fn = ACT2FN[self.config.activation_function]
	self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)

	self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
	self.encoder_attn = FlaxBlenderbotSmallAttention(
	config=self.config,
	embed_dim=self.embed_dim,
	num_heads=self.config.decoder_attention_heads,
	dropout=self.config.attention_dropout,
	dtype=self.dtype,
	)
	self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
	self.fc1 = nn.Dense(
	self.config.decoder_ffn_dim,
	dtype=self.dtype,
	kernel_init=jax.nn.initializers.normal(self.config.init_std),
	)
	self.fc2 = nn.Dense(
	self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
	)
	self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

	def __call__(
	self,
	hidden_states: jnp.ndarray,
	attention_mask: jnp.ndarray,
	encoder_hidden_states: Optional[jnp.ndarray] = None,
	encoder_attention_mask: Optional[jnp.ndarray] = None,
	init_cache: bool = False,
	output_attentions: bool = True,
	deterministic: bool = True,
	) -> Tuple[jnp.ndarray]:
	residual = hidden_states

	# Self Attention
	hidden_states, self_attn_weights = self.self_attn(
	hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
	)
	hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
	hidden_states = residual + hidden_states
	hidden_states = self.self_attn_layer_norm(hidden_states)

	# Cross-Attention Block
	cross_attn_weights = None
	if encoder_hidden_states is not None:
	residual = hidden_states

	hidden_states, cross_attn_weights = self.encoder_attn(
	hidden_states=hidden_states,
	key_value_states=encoder_hidden_states,
	attention_mask=encoder_attention_mask,
	)
	hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
	hidden_states = residual + hidden_states
	hidden_states = self.encoder_attn_layer_norm(hidden_states)

	# Fully Connected
	residual = hidden_states
	hidden_states = self.activation_fn(self.fc1(hidden_states))
	hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
	hidden_states = self.fc2(hidden_states)
	hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
	hidden_states = residual + hidden_states
	hidden_states = self.final_layer_norm(hidden_states)

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights, cross_attn_weights)

	return outputs


	# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->BlenderbotSmall
	class FlaxBlenderbotSmallDecoderLayerCollection(nn.Module):
	config: BlenderbotSmallConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

	def setup(self):
	self.layers = [
	FlaxBlenderbotSmallDecoderLayer(self.config, name=str(i), dtype=self.dtype)
	for i in range(self.config.decoder_layers)
	]
	self.layerdrop = self.config.decoder_layerdrop

	def __call__(
	self,
	hidden_states,
	attention_mask,
	encoder_hidden_states: Optional[jnp.ndarray] = None,
	encoder_attention_mask: Optional[jnp.ndarray] = None,
	deterministic: bool = True,
	init_cache: bool = False,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	):
	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

	for decoder_layer in self.layers:
	if output_hidden_states:
	all_hidden_states += (hidden_states,)
	# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
	dropout_probability = random.uniform(0, 1)
	if not deterministic and (dropout_probability < self.layerdrop):
	layer_outputs = (None, None, None)
	else:
	layer_outputs = decoder_layer(
	hidden_states,
	attention_mask=attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	init_cache=init_cache,
	output_attentions=output_attentions,
	deterministic=deterministic,
	)

	hidden_states = layer_outputs[0]
	if output_attentions:
	all_self_attns += (layer_outputs[1],)

	if encoder_hidden_states is not None:
	all_cross_attentions += (layer_outputs[2],)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]

	if not return_dict:
	return tuple(v for v in outputs if v is not None)

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


	class FlaxBlenderbotSmallEncoder(nn.Module):
	config: BlenderbotSmallConfig
	embed_tokens: nn.Embed
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

	def setup(self):
	self.dropout_layer = nn.Dropout(rate=self.config.dropout)

	embed_dim = self.config.d_model
	self.padding_idx = self.config.pad_token_id
	self.max_source_positions = self.config.max_position_embeddings
	self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0

	self.embed_positions = nn.Embed(
	self.config.max_position_embeddings,
	embed_dim,
	embedding_init=jax.nn.initializers.normal(self.config.init_std),
	)
	self.layers = FlaxBlenderbotSmallEncoderLayerCollection(self.config, self.dtype)
	self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

	def __call__(
	self,
	input_ids,
	attention_mask,
	position_ids,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	deterministic: bool = True,
	):
	input_shape = input_ids.shape
	input_ids = input_ids.reshape(-1, input_shape[-1])

	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	embed_pos = self.embed_positions(position_ids)

	hidden_states = inputs_embeds + embed_pos
	hidden_states = self.layernorm_embedding(hidden_states)
	hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)

	outputs = self.layers(
	hidden_states,
	attention_mask,
	deterministic=deterministic,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	if not return_dict:
	return outputs

	return FlaxBaseModelOutput(
	last_hidden_state=outputs.last_hidden_state,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)


	class FlaxBlenderbotSmallDecoder(nn.Module):
	config: BlenderbotSmallConfig
	embed_tokens: nn.Embed
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

	def setup(self):
	self.dropout_layer = nn.Dropout(rate=self.config.dropout)

	embed_dim = self.config.d_model
	self.padding_idx = self.config.pad_token_id
	self.max_target_positions = self.config.max_position_embeddings
	self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0

	self.embed_positions = nn.Embed(
	self.config.max_position_embeddings,
	embed_dim,
	embedding_init=jax.nn.initializers.normal(self.config.init_std),
	)

	self.layers = FlaxBlenderbotSmallDecoderLayerCollection(self.config, self.dtype)
	self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

	def __call__(
	self,
	input_ids,
	attention_mask,
	position_ids,
	encoder_hidden_states: Optional[jnp.ndarray] = None,
	encoder_attention_mask: Optional[jnp.ndarray] = None,
	init_cache: bool = False,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	deterministic: bool = True,
	):
	input_shape = input_ids.shape
	input_ids = input_ids.reshape(-1, input_shape[-1])

	inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

	# embed positions
	positions = self.embed_positions(position_ids)

	# BlenderbotSmall applies layer norm on inputs_embeds in decoder
	inputs_embeds = self.layernorm_embedding(inputs_embeds)
	hidden_states = inputs_embeds + positions

	hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)

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

	if not return_dict:
	return outputs

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


	# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModule with Bart->BlenderbotSmall
	class FlaxBlenderbotSmallModule(nn.Module):
	config: BlenderbotSmallConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation

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

	self.encoder = FlaxBlenderbotSmallEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
	self.decoder = FlaxBlenderbotSmallDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared)

	def _get_encoder_module(self):
	return self.encoder

	def _get_decoder_module(self):
	return self.decoder

	def __call__(
	self,
	input_ids,
	attention_mask,
	decoder_input_ids,
	decoder_attention_mask,
	position_ids,
	decoder_position_ids,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	deterministic: bool = True,
	):
	encoder_outputs = self.encoder(
	input_ids=input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	deterministic=deterministic,
	)

	decoder_outputs = self.decoder(
	input_ids=decoder_input_ids,
	attention_mask=decoder_attention_mask,
	position_ids=decoder_position_ids,
	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,
	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 FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
	config_class = BlenderbotSmallConfig
	base_model_prefix: str = "model"
	module_class: nn.Module = None

	def __init__(
	self,
	config: BlenderbotSmallConfig,
	input_shape: Tuple[int] = (1, 1),
	seed: int = 0,
	dtype: jnp.dtype = jnp.float32,
	_do_init: bool = True,
	**kwargs,
	):
	module = self.module_class(config=config, dtype=dtype, **kwargs)
	super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

	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")
	# make sure initialization pass will work for FlaxBlenderbotSmallForSequenceClassificationModule
	input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
	attention_mask = jnp.ones_like(input_ids)
	decoder_input_ids = input_ids
	decoder_attention_mask = jnp.ones_like(input_ids)

	batch_size, sequence_length = input_ids.shape
	position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
	decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

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

	random_params = self.module.init(
	rngs,
	input_ids,
	attention_mask,
	decoder_input_ids,
	decoder_attention_mask,
	position_ids,
	decoder_position_ids,
	)["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

	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)
	decoder_position_ids = jnp.broadcast_to(
	jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
	)

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

	init_variables = self.module.init(
	jax.random.PRNGKey(0),
	decoder_input_ids=decoder_input_ids,
	decoder_attention_mask=decoder_attention_mask,
	decoder_position_ids=decoder_position_ids,
	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(BLENDERBOT_SMALL_ENCODE_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=BlenderbotSmallConfig)
	def encode(
	self,
	input_ids: jnp.ndarray,
	attention_mask: Optional[jnp.ndarray] = None,
	position_ids: 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, FlaxBlenderbotSmallForConditionalGeneration

	>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

	>>> text = "My friends are cool but they eat too many carbs."
	>>> inputs = tokenizer(text, max_length=1024, 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)
	if position_ids is None:
	batch_size, sequence_length = input_ids.shape
	position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

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

	def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
	encode_module = module._get_encoder_module()
	return encode_module(input_ids, attention_mask, position_ids, **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"),
	position_ids=jnp.array(position_ids, 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(BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING)
	@replace_return_docstrings(
	output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=BlenderbotSmallConfig
	)
	def decode(
	self,
	decoder_input_ids,
	encoder_outputs,
	encoder_attention_mask: Optional[jnp.ndarray] = None,
	decoder_attention_mask: Optional[jnp.ndarray] = None,
	decoder_position_ids: 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
	>>> import jax.numpy as jnp
	>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

	>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

	>>> text = "My friends are cool but they eat too many carbs."
	>>> inputs = tokenizer(text, max_length=1024, 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)
	>>> last_decoder_hidden_states = outputs.last_hidden_state
	```"""
	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))

	if decoder_position_ids is None:
	if past_key_values is not None:
	raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")

	decoder_position_ids = jnp.broadcast_to(
	jnp.arange(sequence_length)[None, :], (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 FlaxBlenderbotSmallAttention 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, decoder_position_ids, **kwargs):
	decoder_module = module._get_decoder_module()
	return decoder_module(
	decoder_input_ids,
	decoder_attention_mask,
	decoder_position_ids,
	**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"),
	decoder_position_ids=jnp.array(decoder_position_ids, 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

	def __call__(
	self,
	input_ids: jnp.ndarray,
	attention_mask: Optional[jnp.ndarray] = None,
	decoder_input_ids: Optional[jnp.ndarray] = None,
	decoder_attention_mask: Optional[jnp.ndarray] = None,
	position_ids: Optional[jnp.ndarray] = None,
	decoder_position_ids: 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)
	if position_ids is None:
	batch_size, sequence_length = input_ids.shape
	position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

	# prepare decoder inputs
	if decoder_input_ids is None:
	decoder_input_ids = shift_tokens_right(
	input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
	)
	if decoder_attention_mask is None:
	decoder_attention_mask = jnp.ones_like(decoder_input_ids)
	if decoder_position_ids is None:
	batch_size, sequence_length = decoder_input_ids.shape
	decoder_position_ids = jnp.broadcast_to(
	jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
	)

	# 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"),
	position_ids=jnp.array(position_ids, dtype="i4"),
	decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
	decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
	decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	deterministic=not train,
	rngs=rngs,
	)


	@add_start_docstrings(
	"The bare BlenderbotSmall Model transformer outputting raw hidden-states without any specific head on top.",
	BLENDERBOT_SMALL_START_DOCSTRING,
	)
	class FlaxBlenderbotSmallModel(FlaxBlenderbotSmallPreTrainedModel):
	config: BlenderbotSmallConfig
	dtype: jnp.dtype = jnp.float32 # the dtype of the computation
	module_class = FlaxBlenderbotSmallModule


	append_call_sample_docstring(FlaxBlenderbotSmallModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)


	# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForConditionalGenerationModule with Bart->BlenderbotSmall
	class FlaxBlenderbotSmallForConditionalGenerationModule(nn.Module):
	config: BlenderbotSmallConfig
	dtype: jnp.dtype = jnp.float32
	bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros

	def setup(self):
	self.model = FlaxBlenderbotSmallModule(config=self.config, dtype=self.dtype)
	self.lm_head = nn.Dense(
	self.model.shared.num_embeddings,
	use_bias=False,
	dtype=self.dtype,
	kernel_init=jax.nn.initializers.normal(self.config.init_std),
	)
	self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings))

	def _get_encoder_module(self):
	return self.model.encoder

	def _get_decoder_module(self):
	return self.model.decoder

	def __call__(
	self,
	input_ids,
	attention_mask,
	decoder_input_ids,
	decoder_attention_mask,
	position_ids,
	decoder_position_ids,
	output_attentions: bool = False,
	output_hidden_states: bool = False,
	return_dict: bool = True,
	deterministic: bool = True,
	):
	outputs = self.model(
	input_ids=input_ids,
	attention_mask=attention_mask,
	decoder_input_ids=decoder_input_ids,
	decoder_attention_mask=decoder_attention_mask,
	position_ids=position_ids,
	decoder_position_ids=decoder_position_ids,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	deterministic=deterministic,
	)

	hidden_states = outputs[0]

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

	lm_logits += jax.lax.stop_gradient(self.final_logits_bias.astype(self.dtype))

	if not return_dict:
	output = (lm_logits,) + outputs[1:]
	return output

	return FlaxSeq2SeqLMOutput(
	logits=lm_logits,
	decoder_hidden_states=outputs.decoder_hidden_states,
	decoder_attentions=outputs.decoder_attentions,
	cross_attentions=outputs.cross_attentions,
	encoder_last_hidden_state=outputs.encoder_last_hidden_state,
	encoder_hidden_states=outputs.encoder_hidden_states,
	encoder_attentions=outputs.encoder_attentions,
	)


	@add_start_docstrings(
	"The BLENDERBOT_SMALL Model with a language modeling head. Can be used for summarization.",
	BLENDERBOT_SMALL_START_DOCSTRING,
	)
	class FlaxBlenderbotSmallForConditionalGeneration(FlaxBlenderbotSmallPreTrainedModel):
	module_class = FlaxBlenderbotSmallForConditionalGenerationModule
	dtype: jnp.dtype = jnp.float32

	@add_start_docstrings(BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING)
	@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=BlenderbotSmallConfig)
	def decode(
	self,
	decoder_input_ids,
	encoder_outputs,
	encoder_attention_mask: Optional[jnp.ndarray] = None,
	decoder_attention_mask: Optional[jnp.ndarray] = None,
	decoder_position_ids: 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,
	deterministic: bool = True,
	params: dict = None,
	dropout_rng: PRNGKey = None,
	):
	r"""
	Returns:

	Example:

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

	>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

	>>> text = "My friends are cool but they eat too many carbs."
	>>> inputs = tokenizer(text, max_length=1024, 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))

	if decoder_position_ids is None:
	if past_key_values is not None:
	raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")

	decoder_position_ids = jnp.broadcast_to(
	jnp.arange(sequence_length)[None, :], (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 FlaxBlenderbotSmallAttention 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, decoder_position_ids, **kwargs):
	decoder_module = module._get_decoder_module()
	outputs = decoder_module(
	decoder_input_ids,
	decoder_attention_mask,
	decoder_position_ids,
	**kwargs,
	)
	hidden_states = outputs[0]

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

	lm_logits += module.final_logits_bias.astype(self.dtype)
	return lm_logits, 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"),
	decoder_position_ids=jnp.array(decoder_position_ids, 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=deterministic,
	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:
	position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
	extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
	else:
	position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

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

	def update_inputs_for_generation(self, model_outputs, model_kwargs):
	model_kwargs["past_key_values"] = model_outputs.past_key_values
	model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
	return model_kwargs


	FLAX_BLENDERBOT_SMALL_CONDITIONAL_GENERATION_DOCSTRING = """
	Returns:

	Summarization example:

	```py
	>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

	>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")

	>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
	>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np")

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

	Mask filling example:

	```py
	>>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration

	>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
	>>> TXT = "My friends are <mask> but they eat too many carbs."

	>>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
	>>> input_ids = tokenizer([TXT], return_tensors="np")["input_ids"]
	>>> logits = model(input_ids).logits

	>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
	>>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
	>>> values, predictions = jax.lax.top_k(probs)

	>>> tokenizer.decode(predictions).split()
	```
	"""

	overwrite_call_docstring(
	FlaxBlenderbotSmallForConditionalGeneration,
	BLENDERBOT_SMALL_INPUTS_DOCSTRING + FLAX_BLENDERBOT_SMALL_CONDITIONAL_GENERATION_DOCSTRING,
	)
	append_replace_return_docstrings(
	FlaxBlenderbotSmallForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
	)