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	# coding=utf-8
	# Copyright 2020 The Google AI Language Team Authors and The 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.
	"""PyTorch BERT model specific for generation."""

	import math
	from typing import Optional, Tuple, Union

	import torch
	import torch.utils.checkpoint
	from torch import nn
	from torch.nn import CrossEntropyLoss

	from ...activations import ACT2FN
	from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
	from ...modeling_utils import PreTrainedModel
	from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
	from ...utils import (
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	logging,
	replace_return_docstrings,
	)
	from .configuration_bert_generation import BertGenerationConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "google/bert_for_seq_generation_L-24_bbc_encoder"
	_CONFIG_FOR_DOC = "BertGenerationConfig"


	# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->BertGeneration
	class BertGenerationSelfOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->BertGeneration
	class BertGenerationSelfAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
	raise ValueError(
	f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
	f"heads ({config.num_attention_heads})"
	)

	self.num_attention_heads = config.num_attention_heads
	self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
	self.all_head_size = self.num_attention_heads * self.attention_head_size

	self.query = nn.Linear(config.hidden_size, self.all_head_size)
	self.key = nn.Linear(config.hidden_size, self.all_head_size)
	self.value = nn.Linear(config.hidden_size, self.all_head_size)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
	self.position_embedding_type = position_embedding_type or getattr(
	config, "position_embedding_type", "absolute"
	)
	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	self.max_position_embeddings = config.max_position_embeddings
	self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

	self.is_decoder = config.is_decoder

	def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
	new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
	x = x.view(new_x_shape)
	return x.permute(0, 2, 1, 3)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	mixed_query_layer = self.query(hidden_states)

	# If this is instantiated as a cross-attention module, the keys
	# and values come from an encoder; the attention mask needs to be
	# such that the encoder's padding tokens are not attended to.
	is_cross_attention = encoder_hidden_states is not None

	if is_cross_attention and past_key_value is not None:
	# reuse k,v, cross_attentions
	key_layer = past_key_value[0]
	value_layer = past_key_value[1]
	attention_mask = encoder_attention_mask
	elif is_cross_attention:
	key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
	value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
	attention_mask = encoder_attention_mask
	elif past_key_value is not None:
	key_layer = self.transpose_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))
	key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
	value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
	else:
	key_layer = self.transpose_for_scores(self.key(hidden_states))
	value_layer = self.transpose_for_scores(self.value(hidden_states))

	query_layer = self.transpose_for_scores(mixed_query_layer)

	use_cache = past_key_value is not None
	if self.is_decoder:
	# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
	# Further calls to cross_attention layer can then reuse all cross-attention
	# key/value_states (first "if" case)
	# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
	# all previous decoder key/value_states. Further calls to uni-directional self-attention
	# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
	# if encoder bi-directional self-attention `past_key_value` is always `None`
	past_key_value = (key_layer, value_layer)

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
	query_length, key_length = query_layer.shape[2], key_layer.shape[2]
	if use_cache:
	position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
	-1, 1
	)
	else:
	position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
	position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
	distance = position_ids_l - position_ids_r

	positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
	positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility

	if self.position_embedding_type == "relative_key":
	relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores
	elif self.position_embedding_type == "relative_key_query":
	relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
	relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
	attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

	attention_scores = attention_scores / math.sqrt(self.attention_head_size)
	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in BertGenerationModel forward() function)
	attention_scores = attention_scores + attention_mask

	# Normalize the attention scores to probabilities.
	attention_probs = nn.functional.softmax(attention_scores, dim=-1)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs = attention_probs * head_mask

	context_layer = torch.matmul(attention_probs, value_layer)

	context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
	new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
	context_layer = context_layer.view(new_context_layer_shape)

	outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

	if self.is_decoder:
	outputs = outputs + (past_key_value,)
	return outputs


	# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->BertGeneration
	class BertGenerationAttention(nn.Module):
	def __init__(self, config, position_embedding_type=None):
	super().__init__()
	self.self = BertGenerationSelfAttention(config, position_embedding_type=position_embedding_type)
	self.output = BertGenerationSelfOutput(config)
	self.pruned_heads = set()

	def prune_heads(self, heads):
	if len(heads) == 0:
	return
	heads, index = find_pruneable_heads_and_indices(
	heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
	)

	# Prune linear layers
	self.self.query = prune_linear_layer(self.self.query, index)
	self.self.key = prune_linear_layer(self.self.key, index)
	self.self.value = prune_linear_layer(self.self.value, index)
	self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

	# Update hyper params and store pruned heads
	self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
	self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
	self.pruned_heads = self.pruned_heads.union(heads)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	self_outputs = self.self(
	hidden_states,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	)
	attention_output = self.output(self_outputs[0], hidden_states)
	outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
	return outputs


	# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->BertGeneration
	class BertGenerationIntermediate(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
	if isinstance(config.hidden_act, str):
	self.intermediate_act_fn = ACT2FN[config.hidden_act]
	else:
	self.intermediate_act_fn = config.hidden_act

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.intermediate_act_fn(hidden_states)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->BertGeneration
	class BertGenerationOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	hidden_states = self.dense(hidden_states)
	hidden_states = self.dropout(hidden_states)
	hidden_states = self.LayerNorm(hidden_states + input_tensor)
	return hidden_states


	# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->BertGeneration
	class BertGenerationLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.chunk_size_feed_forward = config.chunk_size_feed_forward
	self.seq_len_dim = 1
	self.attention = BertGenerationAttention(config)
	self.is_decoder = config.is_decoder
	self.add_cross_attention = config.add_cross_attention
	if self.add_cross_attention:
	if not self.is_decoder:
	raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
	self.crossattention = BertGenerationAttention(config, position_embedding_type="absolute")
	self.intermediate = BertGenerationIntermediate(config)
	self.output = BertGenerationOutput(config)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
	self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
	self_attention_outputs = self.attention(
	hidden_states,
	attention_mask,
	head_mask,
	output_attentions=output_attentions,
	past_key_value=self_attn_past_key_value,
	)
	attention_output = self_attention_outputs[0]

	# if decoder, the last output is tuple of self-attn cache
	if self.is_decoder:
	outputs = self_attention_outputs[1:-1]
	present_key_value = self_attention_outputs[-1]
	else:
	outputs = self_attention_outputs[1:] # add self attentions if we output attention weights

	cross_attn_present_key_value = None
	if self.is_decoder and encoder_hidden_states is not None:
	if not hasattr(self, "crossattention"):
	raise ValueError(
	f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
	" by setting `config.add_cross_attention=True`"
	)

	# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
	cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
	cross_attention_outputs = self.crossattention(
	attention_output,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	cross_attn_past_key_value,
	output_attentions,
	)
	attention_output = cross_attention_outputs[0]
	outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights

	# add cross-attn cache to positions 3,4 of present_key_value tuple
	cross_attn_present_key_value = cross_attention_outputs[-1]
	present_key_value = present_key_value + cross_attn_present_key_value

	layer_output = apply_chunking_to_forward(
	self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
	)
	outputs = (layer_output,) + outputs

	# if decoder, return the attn key/values as the last output
	if self.is_decoder:
	outputs = outputs + (present_key_value,)

	return outputs

	def feed_forward_chunk(self, attention_output):
	intermediate_output = self.intermediate(attention_output)
	layer_output = self.output(intermediate_output, attention_output)
	return layer_output


	# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->BertGeneration
	class BertEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layer = nn.ModuleList([BertGenerationLayer(config) for _ in range(config.num_hidden_layers)])
	self.gradient_checkpointing = False

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = False,
	output_hidden_states: Optional[bool] = False,
	return_dict: Optional[bool] = True,
	) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
	all_hidden_states = () if output_hidden_states else None
	all_self_attentions = () if output_attentions else None
	all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

	if self.gradient_checkpointing and self.training:
	if use_cache:
	logger.warning_once(
	"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
	)
	use_cache = False

	next_decoder_cache = () if use_cache else None
	for i, layer_module in enumerate(self.layer):
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	layer_head_mask = head_mask[i] if head_mask is not None else None
	past_key_value = past_key_values[i] if past_key_values is not None else None

	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, past_key_value, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer_module),
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	)

	hidden_states = layer_outputs[0]
	if use_cache:
	next_decoder_cache += (layer_outputs[-1],)
	if output_attentions:
	all_self_attentions = all_self_attentions + (layer_outputs[1],)
	if self.config.add_cross_attention:
	all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	if not return_dict:
	return tuple(
	v
	for v in [
	hidden_states,
	next_decoder_cache,
	all_hidden_states,
	all_self_attentions,
	all_cross_attentions,
	]
	if v is not None
	)
	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=hidden_states,
	past_key_values=next_decoder_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attentions,
	cross_attentions=all_cross_attentions,
	)


	def load_tf_weights_in_bert_generation(
	model, tf_hub_path, model_class, is_encoder_named_decoder=False, is_encoder=False
	):
	try:
	import numpy as np
	import tensorflow.compat.v1 as tf
	import tensorflow_hub as hub
	import tensorflow_text # noqa: F401

	tf.disable_eager_execution()
	except ImportError:
	logger.error(
	"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
	"https://www.tensorflow.org/install/ for installation instructions."
	)
	raise
	tf_model = hub.Module(tf_hub_path)
	init = tf.global_variables_initializer()
	with tf.Session() as sess:
	init.run()
	all_variables = tf_model.variable_map
	keep_track_variables = all_variables.copy()
	for key in list(all_variables.keys()):
	if "global" in key:
	logger.info(f"Skipping {key}...")
	continue
	if not is_encoder:
	model_pointer = getattr(model, model_class)
	else:
	model_pointer = model
	is_embedding = False
	logger.info(f"Trying to match {key}...")
	# remove start_string = "module/bert/"
	sub_layers = key.split("/")[2:]
	if is_encoder_named_decoder and sub_layers[0] == "encoder":
	logger.info(f"Skipping encoder layer {key} for decoder")
	continue
	if is_encoder and sub_layers[0] == "decoder":
	logger.info(f"Skipping decoder layer {key} for encoder")
	continue
	for i, sub_layer in enumerate(sub_layers):
	if sub_layer == "embeddings":
	is_embedding = True
	elif sub_layer == "LayerNorm":
	is_embedding = False
	if "layer" in sub_layer:
	model_pointer = model_pointer.layer[int(sub_layer.split("_")[-1])]
	elif sub_layer in ["kernel", "gamma"]:
	model_pointer = model_pointer.weight
	elif sub_layer == "beta":
	model_pointer = model_pointer.bias
	elif sub_layer == "encdec":
	model_pointer = model_pointer.crossattention.self
	elif sub_layer == "encdec_output":
	model_pointer = model_pointer.crossattention.output
	elif is_encoder_named_decoder and sub_layer == "decoder":
	model_pointer = model_pointer.encoder
	else:
	if sub_layer == "attention" and "encdec" in sub_layers[i + 1]:
	continue
	try:
	model_pointer = getattr(model_pointer, sub_layer)
	except AttributeError:
	logger.info(f"Skipping to initialize {key} at {sub_layer}...")
	raise AttributeError

	array = np.asarray(sess.run(all_variables[key]))
	if not is_embedding:
	logger.info(f"Transposing numpy weight of shape {array.shape} for {key}")
	array = np.transpose(array)
	else:
	model_pointer = model_pointer.weight

	if model_pointer.shape != array.shape:
	raise ValueError(f"Pointer shape {model_pointer.shape} and array shape {array.shape} mismatched")
	logger.info(f"Initialize PyTorch weight {key}")

	model_pointer.data = torch.from_numpy(array.astype(np.float32))
	keep_track_variables.pop(key, None)

	logger.info(f"Weights not copied to PyTorch model: {', '.join(keep_track_variables.keys())}")
	return model


	class BertGenerationEmbeddings(nn.Module):
	"""Construct the embeddings from word and position embeddings."""

	def __init__(self, config):
	super().__init__()
	self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
	# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
	# any TensorFlow checkpoint file
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	# position_ids (1, len position emb) is contiguous in memory and exported when serialized
	self.register_buffer(
	"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
	)

	def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
	if input_ids is not None:
	input_shape = input_ids.size()
	else:
	input_shape = inputs_embeds.size()[:-1]

	seq_length = input_shape[1]

	if position_ids is None:
	position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]

	if inputs_embeds is None:
	inputs_embeds = self.word_embeddings(input_ids)
	position_embeddings = self.position_embeddings(position_ids)

	embeddings = inputs_embeds + position_embeddings
	embeddings = self.LayerNorm(embeddings)
	embeddings = self.dropout(embeddings)
	return embeddings


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

	config_class = BertGenerationConfig
	base_model_prefix = "bert"
	supports_gradient_checkpointing = True

	def _init_weights(self, module):
	"""Initialize the weights"""
	if isinstance(module, nn.Linear):
	# Slightly different from the TF version which uses truncated_normal for initialization
	# cf https://github.com/pytorch/pytorch/pull/5617
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()
	elif isinstance(module, nn.LayerNorm):
	module.bias.data.zero_()
	module.weight.data.fill_(1.0)

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, BertEncoder):
	module.gradient_checkpointing = value


	BERT_GENERATION_START_DOCSTRING = r"""

	This model inherits from [`PreTrainedModel`]. 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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.

	Parameters:
	config ([`BertGenerationConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
	"""

	BERT_GENERATION_INPUTS_DOCSTRING = r"""
	Args:
	input_ids (`torch.LongTensor` of shape `({0})`):
	Indices of input sequence tokens in the vocabulary.

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

	[What are input IDs?](../glossary#input-ids)
	attention_mask (`torch.FloatTensor` of shape `({0})`, 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 (`torch.LongTensor` of shape `({0})`, optional):
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
	config.max_position_embeddings - 1]`.

	[What are position IDs?](../glossary#position-ids)
	head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional):
	Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

	- 1 indicates the head is not masked,
	- 0 indicates the head is masked.

	inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, optional):
	Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
	model's internal embedding lookup matrix.
	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.
	"""


	@add_start_docstrings(
	"The bare BertGeneration model transformer outputting raw hidden-states without any specific head on top.",
	BERT_GENERATION_START_DOCSTRING,
	)
	class BertGenerationEncoder(BertGenerationPreTrainedModel):
	"""

	The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
	cross-attention is added between the self-attention layers, following the architecture described in [Attention is
	all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
	Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

	This model should be used when leveraging Bert or Roberta checkpoints for the [`EncoderDecoderModel`] class as
	described in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461)
	by Sascha Rothe, Shashi Narayan, and Aliaksei Severyn.

	To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
	to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
	`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
	"""

	def __init__(self, config):
	super().__init__(config)
	self.config = config

	self.embeddings = BertGenerationEmbeddings(config)
	self.encoder = BertEncoder(config)

	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.embeddings.word_embeddings

	def set_input_embeddings(self, value):
	self.embeddings.word_embeddings = value

	def _prune_heads(self, heads_to_prune):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
	class PreTrainedModel
	"""
	for layer, heads in heads_to_prune.items():
	self.encoder.layer[layer].attention.prune_heads(heads)

	@add_start_docstrings_to_model_forward(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=BaseModelOutputWithPastAndCrossAttentions,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
	r"""
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: `1` for
	tokens that are NOT MASKED, `0` for MASKED tokens.
	past_key_values (`tuple(tuple(torch.FloatTensor))` 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)`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).
	"""
	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.use_return_dict

	if self.config.is_decoder:
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	else:
	use_cache = False

	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
	elif input_ids is not None:
	self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
	input_shape = input_ids.size()
	elif inputs_embeds is not None:
	input_shape = inputs_embeds.size()[:-1]
	else:
	raise ValueError("You have to specify either input_ids or inputs_embeds")

	batch_size, seq_length = input_shape
	device = input_ids.device if input_ids is not None else inputs_embeds.device

	# past_key_values_length
	past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

	if attention_mask is None:
	attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)

	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	extended_attention_mask = None
	if not use_cache:
	extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if self.config.is_decoder and encoder_hidden_states is not None:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
	if encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = None

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

	embedding_output = self.embeddings(
	input_ids=input_ids,
	position_ids=position_ids,
	inputs_embeds=inputs_embeds,
	past_key_values_length=past_key_values_length,
	)

	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_extended_attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	sequence_output = encoder_outputs[0]

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

	return BaseModelOutputWithPastAndCrossAttentions(
	last_hidden_state=sequence_output,
	past_key_values=encoder_outputs.past_key_values,
	hidden_states=encoder_outputs.hidden_states,
	attentions=encoder_outputs.attentions,
	cross_attentions=encoder_outputs.cross_attentions,
	)


	class BertGenerationOnlyLMHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
	self.bias = nn.Parameter(torch.zeros(config.vocab_size))
	self.decoder.bias = self.bias

	def forward(self, hidden_states):
	logits = self.decoder(hidden_states)
	return logits

	def _tie_weights(self):
	# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
	self.bias = self.decoder.bias


	@add_start_docstrings(
	"""BertGeneration Model with a `language modeling` head on top for CLM fine-tuning.""",
	BERT_GENERATION_START_DOCSTRING,
	)
	class BertGenerationDecoder(BertGenerationPreTrainedModel):
	_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]

	def __init__(self, config):
	super().__init__(config)

	if not config.is_decoder:
	logger.warning("If you want to use `BertGenerationDecoder` as a standalone, add `is_decoder=True.`")

	self.bert = BertGenerationEncoder(config)
	self.lm_head = BertGenerationOnlyLMHead(config)

	# Initialize weights and apply final processing
	self.post_init()

	def get_output_embeddings(self):
	return self.lm_head.decoder

	def set_output_embeddings(self, new_embeddings):
	self.lm_head.decoder = new_embeddings

	@add_start_docstrings_to_model_forward(BERT_GENERATION_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.Tensor] = None,
	head_mask: Optional[torch.Tensor] = None,
	inputs_embeds: Optional[torch.Tensor] = None,
	encoder_hidden_states: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	labels: Optional[torch.Tensor] = None,
	past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
	r"""
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional):
	Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
	`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
	ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
	past_key_values (`tuple(tuple(torch.FloatTensor))` 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)`.
	use_cache (`bool`, optional):
	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
	`past_key_values`).

	Returns:

	Example:

	```python
	>>> from transformers import AutoTokenizer, BertGenerationDecoder, BertGenerationConfig
	>>> import torch

	>>> tokenizer = AutoTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
	>>> config = BertGenerationConfig.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
	>>> config.is_decoder = True
	>>> model = BertGenerationDecoder.from_pretrained(
	... "google/bert_for_seq_generation_L-24_bbc_encoder", config=config
	... )

	>>> inputs = tokenizer("Hello, my dog is cute", return_token_type_ids=False, return_tensors="pt")
	>>> outputs = model(**inputs)

	>>> prediction_logits = outputs.logits
	```"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict
	if labels is not None:
	use_cache = False

	outputs = self.bert(
	input_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	head_mask=head_mask,
	inputs_embeds=inputs_embeds,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	past_key_values=past_key_values,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	sequence_output = outputs[0]
	prediction_scores = self.lm_head(sequence_output)

	lm_loss = None
	if labels is not None:
	# we are doing next-token prediction; shift prediction scores and input ids by one
	shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
	labels = labels[:, 1:].contiguous()
	loss_fct = CrossEntropyLoss()
	lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

	if not return_dict:
	output = (prediction_scores,) + outputs[1:]
	return ((lm_loss,) + output) if lm_loss is not None else output

	return CausalLMOutputWithCrossAttentions(
	loss=lm_loss,
	logits=prediction_scores,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	cross_attentions=outputs.cross_attentions,
	)

	def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs):
	input_shape = input_ids.shape
	# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
	if attention_mask is None:
	attention_mask = input_ids.new_ones(input_shape)

	# cut decoder_input_ids if past is used
	if past_key_values is not None:
	input_ids = input_ids[:, -1:]

	return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}

	def _reorder_cache(self, past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
	)
	return reordered_past