





"""simple docstring"""
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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]			):
 lowerCAmelCase    				= torch.exp(_UpperCAmelCase			)
 lowerCAmelCase    				= torch.sum(_UpperCAmelCase  ,     dim=1			)  # sum of exp(x_i)
 lowerCAmelCase    				= torch.sum(x * exp_x  ,     dim=1			)  # sum of x_i * exp(x_i)
 return torch.log(_UpperCAmelCase			) - B / A







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= config.output_attentions
  lowerCAmelCase    				= config.output_hidden_states
  lowerCAmelCase    				= nn.ModuleList([BertLayer(_snake_case     ) for _ in range(config.num_hidden_layers     )]     )
  lowerCAmelCase    				= nn.ModuleList([BertHighway(_snake_case     ) for _ in range(config.num_hidden_layers     )]     )

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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= 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				,       _snake_case				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






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

   lowerCAmelCase    				= layer_module(
       _snake_case				,       _snake_case				,       head_mask[i]				,       _snake_case				,       _snake_case     )
   lowerCAmelCase    				= layer_outputs[0]

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

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

   lowerCAmelCase    				= self.highway[i](_snake_case     )
   # logits, pooled_output

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

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

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

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

  lowerCAmelCase    				= 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). '''   , a__   , )
class       a							(   a__  ):



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  super().__init__(_snake_case     )
  lowerCAmelCase    				= config

  lowerCAmelCase    				= BertEmbeddings(_snake_case     )
  lowerCAmelCase    				= DeeBertEncoder(_snake_case     )
  lowerCAmelCase    				= BertPooler(_snake_case     )

  self.init_weights()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.encoder.init_highway_pooler(self.pooler     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self.embeddings.word_embeddings



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= value



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






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



 @add_start_docstrings_to_model_forward(_snake_case     )
 def    UpperCamelCase__  (     self				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       ):







  """simple docstring"""






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

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

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

  # 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.
  lowerCAmelCase    				= self.get_extended_attention_mask(_snake_case				,       _snake_case				,       _snake_case     )

  # 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:
   lowerCAmelCase    				= encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.dim() == 2:
   lowerCAmelCase    				= encoder_attention_mask[:, None, None, :]

  lowerCAmelCase    				= encoder_extended_attention_mask.to(
      dtype=next(self.parameters()     ).dtype     )  # fp16 compatibility
  lowerCAmelCase    				= (1.0 - encoder_extended_attention_mask) * -10_000.0

  # 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]
  lowerCAmelCase    				= self.get_head_mask(_snake_case				,       self.config.num_hidden_layers     )

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

  lowerCAmelCase    				= (
      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       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= message
  lowerCAmelCase    				= exit_layer  # start from 1!







class       a							(   nn.Module  ):



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  super().__init__()
  lowerCAmelCase    				= BertPooler(_snake_case     )
  lowerCAmelCase    				= nn.Dropout(config.hidden_dropout_prob     )
  lowerCAmelCase    				= nn.Linear(config.hidden_size				,       config.num_labels     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= encoder_outputs[0]
  lowerCAmelCase    				= self.pooler(_snake_case     )
  # "return" pooler_output

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

  # Dropout and classification
  lowerCAmelCase    				= bmodel_output[1]

  lowerCAmelCase    				= self.dropout(_snake_case     )
  lowerCAmelCase    				= self.classifier(_snake_case     )

  return logits, pooled_output







@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''   , a__   , )
class       a							(   a__  ):



 def __init__(     self				,       _snake_case     ):







  """simple docstring"""






  super().__init__(_snake_case     )
  lowerCAmelCase    				= config.num_labels
  lowerCAmelCase    				= config.num_hidden_layers

  lowerCAmelCase    				= DeeBertModel(_snake_case     )
  lowerCAmelCase    				= nn.Dropout(config.hidden_dropout_prob     )
  lowerCAmelCase    				= nn.Linear(config.hidden_size				,       self.config.num_labels     )

  self.init_weights()



 @add_start_docstrings_to_model_forward(_snake_case     )
 def    UpperCamelCase__  (     self				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=None				,       _snake_case=-1				,       _snake_case=False				,       ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_layers
  try:
   lowerCAmelCase    				= self.bert(
       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case				,       position_ids=_snake_case				,       head_mask=_snake_case				,       inputs_embeds=_snake_case				,       )
   # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

   lowerCAmelCase    				= outputs[1]

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

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

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

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







"""simple docstring"""





import argparse
import os
import re


UpperCamelCase_       :    Any						      =			'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_       :    Optional[int]						      =			re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
UpperCamelCase_       :    Tuple						      =			re.compile(R'''\s*\(\s*"(\S[^"]+)"''')
def 				A_  (__a					, __a = False					):

					'''simple docstring'''





					with open(__a					, "r"					, encoding="utf-8"					) as f:
										A_								=    f.read()

					A_								=    content.split("\n"					)
					A_								=    []
					A_								=    0
					while line_idx < len(__a					):
										if _re_intro_mapping.search(lines[line_idx]					) is not None:
															A_								=    len(re.search(R"^(\s*)\S"					, lines[line_idx]					).groups()[0]					) + 8
															# Start of a new mapping!
															while not lines[line_idx].startswith(" " * indent + "("					):
																				new_lines.append(lines[line_idx]					)
																				line_idx += 1

															A_								=    []
															while lines[line_idx].strip() != "]":
																				# Blocks either fit in one line or not
																				if lines[line_idx].strip() == "(":
																									A_								=    line_idx
																									while not lines[line_idx].startswith(" " * indent + ")"					):
																														line_idx += 1
																									blocks.append("\n".join(lines[start_idx : line_idx + 1]					)					)
																				else:
																									blocks.append(lines[line_idx]					)
																				line_idx += 1

															# Sort blocks by their identifiers
															A_								=    sorted(__a					, key=lambda __a					: _re_identifier.search(__a					).groups()[0]					)
															new_lines += blocks
										else:
															new_lines.append(lines[line_idx]					)
															line_idx += 1

					if overwrite:
										with open(__a					, "w"					, encoding="utf-8"					) as f:
															f.write("\n".join(__a					)					)
					elif "\n".join(__a					) != content:
										return True
def 				A_  (__a = False					):

					'''simple docstring'''





					A_								=    [os.path.join(__a					, __a					) for f in os.listdir(__a					) if f.endswith(".py"					)]
					A_								=    [sort_auto_mapping(__a					, overwrite=__a					) for fname in fnames]

					if not overwrite and any(__a					):
										A_								=    [f for f, d in zip(__a					, __a					) if d]
										raise ValueError(
										    f'The following files have auto mappings that need sorting: {", ".join(__a					)}. Run `make style` to fix'
										    " this."					)


if __name__ == "__main__":
		UpperCamelCase_       :    str						      =			argparse.ArgumentParser()
		parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
		UpperCamelCase_       :    List[Any]						      =			parser.parse_args()

		sort_all_auto_mappings(not args.check_only)




