
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def 		UpperCAmelCase     (      a_		)						->      str:







				"""simple docstring"""



				__A       =					torch.exp(A__		)
				__A       =					torch.sum(A__						,  dim=1		)  # sum of exp(x_i)
				__A       =					torch.sum(x * exp_x						,  dim=1		)  # sum of x_i * exp(x_i)
				return torch.log(A__		) - B / A





class 				UpperCAmelCase (    nn.Module    ):




	'''simple docstring'''





	def __init__(			self   :					Union[str, Any]				,A   :					Optional[int]					):
					super().__init__()
					__A       =					config.output_attentions
					__A       =					config.output_hidden_states
					__A       =					nn.ModuleList([BertLayer(A					) for _ in range(config.num_hidden_layers					)]					)
					__A       =					nn.ModuleList([BertHighway(A					) for _ in range(config.num_hidden_layers					)]					)

					__A       =					[-1 for _ in range(config.num_hidden_layers					)]






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					Optional[int]					):

					if (type(A					) is float) or (type(A					) is int):
									for i in range(len(self.early_exit_entropy					)					):
													__A       =					x
					else:
									__A       =					x






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					Any					):
					__A       =					pooler.state_dict()
					for highway in self.highway:
									for name, param in highway.pooler.state_dict().items():
													param.copy_(loaded_model[name]					)







	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					Tuple				,A   :					Union[str, Any]=None				,A   :					Optional[Any]=None				,A   :					Optional[Any]=None				,A   :					Union[str, Any]=None				,):
					__A       =					()
					__A       =					()
					__A       =					()
					for i, layer_module in enumerate(self.layer					):
									if self.output_hidden_states:
													__A       =					all_hidden_states + (hidden_states,)

									__A       =					layer_module(
									    A				,A				,head_mask[i]				,A				,A					)
									__A       =					layer_outputs[0]

									if self.output_attentions:
													__A       =					all_attentions + (layer_outputs[1],)

									__A       =					(hidden_states,)
									if self.output_hidden_states:
													__A       =					current_outputs + (all_hidden_states,)
									if self.output_attentions:
													__A       =					current_outputs + (all_attentions,)

									__A       =					self.highway[i](A					)
									# logits, pooled_output

									if not self.training:
													__A       =					highway_exit[0]
													__A       =					entropy(A					)
													__A       =					highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
													__A       =					all_highway_exits + (highway_exit,)

													if highway_entropy < self.early_exit_entropy[i]:
																	__A       =					(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																	raise HighwayException(A				,i + 1					)
									else:
													__A       =					all_highway_exits + (highway_exit,)

        # Add last layer
					if self.output_hidden_states:
									__A       =					all_hidden_states + (hidden_states,)

					__A       =					(hidden_states,)
					if self.output_hidden_states:
									__A       =					outputs + (all_hidden_states,)
					if self.output_attentions:
									__A       =					outputs + (all_attentions,)

					__A       =					outputs + (all_highway_exits,)
					return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "     ,     __SCREAMING_SNAKE_CASE     ,     )
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def __init__(			self   :					List[str]				,A   :					Optional[Any]					):
					super().__init__(A					)
					__A       =					config

					__A       =					BertEmbeddings(A					)
					__A       =					DeeBertEncoder(A					)
					__A       =					BertPooler(A					)

					self.init_weights()






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					self.encoder.init_highway_pooler(self.pooler					)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					return self.embeddings.word_embeddings






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					List[Any]					):
					__A       =					value






	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					int					):

					for layer, heads in heads_to_prune.items():
									self.encoder.layer[layer].attention.prune_heads(A					)







	@add_start_docstrings_to_model_forward(A					)
	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					str=None				,A   :					Dict=None				,A   :					Dict=None				,A   :					Optional[int]=None				,A   :					List[str]=None				,A   :					Dict=None				,A   :					Optional[int]=None				,A   :					Optional[int]=None				,):

					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:
									__A       =					input_ids.size()
					elif inputs_embeds is not None:
									__A       =					inputs_embeds.size()[:-1]
					else:
									raise ValueError("You have to specify either input_ids or inputs_embeds"					)

					__A       =					input_ids.device if input_ids is not None else inputs_embeds.device

					if attention_mask is None:
									__A       =					torch.ones(A				,device=A					)
					if encoder_attention_mask is None:
									__A       =					torch.ones(A				,device=A					)
					if token_type_ids is None:
									__A       =					torch.zeros(A				,dtype=torch.long				,device=A					)

					# 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.
					__A       =					self.get_extended_attention_mask(A				,A				,A					)

					# If a 2D ou 3D attention mask is provided for the cross-attention
					# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
					if encoder_attention_mask.dim() == 3:
									__A       =					encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.dim() == 2:
									__A       =					encoder_attention_mask[:, None, None, :]

					__A       =					encoder_extended_attention_mask.to(
					    dtype=next(self.parameters()					).dtype					)  # fp16 compatibility
					__A       =					(1.0 - encoder_extended_attention_mask) * -1_00_00.0

					# 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]
					__A       =					self.get_head_mask(A				,self.config.num_hidden_layers					)

					__A       =					self.embeddings(
					    input_ids=A				,position_ids=A				,token_type_ids=A				,inputs_embeds=A					)
					__A       =					self.encoder(
					    A				,attention_mask=A				,head_mask=A				,encoder_hidden_states=A				,encoder_attention_mask=A				,)
					__A       =					encoder_outputs[0]
					__A       =					self.pooler(A					)

					__A       =					(
					    sequence_output,
					    pooled_output,
					) + encoder_outputs[
					    1:
					]  # add hidden_states and attentions if they are here
					return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def __init__(			self   :					Optional[Any]				,A   :					Optional[Any]				,A   :					Any					):
					__A       =					message
					__A       =					exit_layer  # start from 1!





class 				UpperCAmelCase (    nn.Module    ):




	'''simple docstring'''





	def __init__(			self   :					int				,A   :					List[str]					):
					super().__init__()
					__A       =					BertPooler(A					)
					__A       =					nn.Dropout(config.hidden_dropout_prob					)
					__A       =					nn.Linear(config.hidden_size				,config.num_labels					)







	def 							UpperCamelCase_    (			self   :					int				,A   :					str					):
					__A       =					encoder_outputs[0]
					__A       =					self.pooler(A					)
					# "return" pooler_output

					# BertModel
					__A       =					(pooler_input, pooler_output) + encoder_outputs[1:]
					# "return" bmodel_output

					# Dropout and classification
					__A       =					bmodel_output[1]

					__A       =					self.dropout(A					)
					__A       =					self.classifier(A					)

					return logits, pooled_output





@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "     ,     __SCREAMING_SNAKE_CASE     ,     )
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def __init__(			self   :					Dict				,A   :					Tuple					):
					super().__init__(A					)
					__A       =					config.num_labels
					__A       =					config.num_hidden_layers

					__A       =					DeeBertModel(A					)
					__A       =					nn.Dropout(config.hidden_dropout_prob					)
					__A       =					nn.Linear(config.hidden_size				,self.config.num_labels					)

					self.init_weights()







	@add_start_docstrings_to_model_forward(A					)
	def 							UpperCamelCase_    (			self   :					List[Any]				,A   :					Optional[Any]=None				,A   :					Optional[Any]=None				,A   :					List[Any]=None				,A   :					Dict=None				,A   :					List[str]=None				,A   :					List[Any]=None				,A   :					List[Any]=None				,A   :					Union[str, Any]=-1				,A   :					List[str]=False				,):
					__A       =					self.num_layers
					try:
									__A       =					self.bert(
									    A				,attention_mask=A				,token_type_ids=A				,position_ids=A				,head_mask=A				,inputs_embeds=A				,)
									# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

									__A       =					outputs[1]

									__A       =					self.dropout(A					)
									__A       =					self.classifier(A					)
									__A       =					(logits,) + outputs[2:]  # add hidden states and attention if they are here
					except HighwayException as e:
									__A       =					e.message
									__A       =					e.exit_layer
									__A       =					outputs[0]

					if not self.training:
									__A       =					entropy(A					)
									__A       =					[]
									__A       =					[]
					if labels is not None:
									if self.num_labels == 1:
													#  We are doing regression
													__A       =					MSELoss()
													__A       =					loss_fct(logits.view(-1					)				,labels.view(-1					)					)
									else:
													__A       =					CrossEntropyLoss()
													__A       =					loss_fct(logits.view(-1				,self.num_labels					)				,labels.view(-1					)					)

									# work with highway exits
									__A       =					[]
									for highway_exit in outputs[-1]:
													__A       =					highway_exit[0]
													if not self.training:
																	highway_logits_all.append(A					)
																	highway_entropy.append(highway_exit[2]					)
													if self.num_labels == 1:
																	#  We are doing regression
																	__A       =					MSELoss()
																	__A       =					loss_fct(highway_logits.view(-1					)				,labels.view(-1					)					)
													else:
																	__A       =					CrossEntropyLoss()
																	__A       =					loss_fct(highway_logits.view(-1				,self.num_labels					)				,labels.view(-1					)					)
													highway_losses.append(A					)

									if train_highway:
													__A       =					(sum(highway_losses[:-1]					),) + outputs
													# exclude the final highway, of course
									else:
													__A       =					(loss,) + outputs
					if not self.training:
									__A       =					outputs + ((original_entropy, highway_entropy), exit_layer)
									if output_layer >= 0:
													__A       =					(
													    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
													)  # use the highway of the last layer

					return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)





# Copyright 2021 The HuggingFace 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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	_lowerCAmelCase (    A__=None						):
      if subparsers is not None:
            lowercase__  =	subparsers.add_parser('test'						)
      else:
            lowercase__  =	argparse.ArgumentParser('Accelerate test command'						)

      parser.add_argument(
          '--config_file'    ,	default=A__    ,	help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )    ,	)

      if subparsers is not None:
            parser.set_defaults(func=A__						)
      return parser

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ['test_utils', 'scripts', 'test_script.py']						)

      if args.config_file is None:
            lowercase__  =	script_name
      else:
            lowercase__  =	F'''--config_file={args.config_file} {script_name}'''

      lowercase__  =	['accelerate-launch'] + test_args.split()
      lowercase__  =	execute_subprocess_async(A__    ,	env=os.environ.copy()						)
      if result.returncode == 0:
            print('Test is a success! You are ready for your distributed training!'						)

def 	_lowerCAmelCase (    ):
      lowercase__  =	test_command_parser()
      lowercase__  =	parser.parse_args()
      test_command(A__						)


if __name__ == "__main__":
       main()
