





from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). "       ,				__SCREAMING_SNAKE_CASE       ,				)
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     RobertaConfig
 lowercase_						=     "roberta"

 def __init__(self							:  List[Any]			,					UpperCAmelCase_							:  Union[str, Any])			->Union[str, Any]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)

   lowerCamelCase__: Any         =RobertaEmbeddings(UpperCAmelCase_)
   self.init_weights()



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







 '''simple docstring'''
 lowercase_						=     RobertaConfig
 lowercase_						=     "roberta"

 def __init__(self							:  Optional[Any]			,					UpperCAmelCase_							:  List[str])			->str:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =config.num_labels
   lowerCamelCase__: Union[str, Any]         =config.num_hidden_layers

   lowerCamelCase__: Dict         =DeeRobertaModel(UpperCAmelCase_)
   lowerCamelCase__: Optional[Any]         =nn.Dropout(config.hidden_dropout_prob)
   lowerCamelCase__: Tuple         =nn.Linear(config.hidden_size			,					self.config.num_labels)







 @add_start_docstrings_to_model_forward(UpperCAmelCase_)
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Dict=None			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  Dict=None			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  int=None			,					UpperCAmelCase_							:  Any=None			,					UpperCAmelCase_							:  Dict=-1			,					UpperCAmelCase_							:  List[Any]=False			,					)			->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.num_layers
   try:
     lowerCamelCase__: Any         =self.roberta(
         UpperCAmelCase_			,					attention_mask=UpperCAmelCase_			,					token_type_ids=UpperCAmelCase_			,					position_ids=UpperCAmelCase_			,					head_mask=UpperCAmelCase_			,					inputs_embeds=UpperCAmelCase_			,					)

     lowerCamelCase__: int         =outputs[1]

     lowerCamelCase__: int         =self.dropout(UpperCAmelCase_)
     lowerCamelCase__: Optional[Any]         =self.classifier(UpperCAmelCase_)
     lowerCamelCase__: List[Any]         =(logits,) + outputs[2:]  # add hidden states and attention if they are here
   except HighwayException as e:
     lowerCamelCase__: List[str]         =e.message
     lowerCamelCase__: str         =e.exit_layer
     lowerCamelCase__: Dict         =outputs[0]

   if not self.training:
     lowerCamelCase__: Dict         =entropy(UpperCAmelCase_)
     lowerCamelCase__: Tuple         =[]
     lowerCamelCase__: Optional[int]         =[]
   if labels is not None:
     if self.num_labels == 1:
       #  We are doing regression
       lowerCamelCase__: Optional[int]         =MSELoss()
       lowerCamelCase__: Tuple         =loss_fct(logits.view(-1)			,					labels.view(-1))
     else:
       lowerCamelCase__: str         =CrossEntropyLoss()
       lowerCamelCase__: Optional[int]         =loss_fct(logits.view(-1			,					self.num_labels)			,					labels.view(-1))

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

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

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








def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		set()

       # Replace all the whitespace in our sentence
       A__		   =		input_str.replace(''' '''					,	''''''    )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()    )
       return len(UpperCamelCase__    ) == 26


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		[False] * 26
       for char in input_str:
              if char.islower():
                     A__		   =		True
              elif char.isupper():
                     A__		   =		True
       return all(UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       return len({char for char in input_str.lower() if char.isalpha()}    ) == 26


def       UpperCamelCase__(			)->None:
       from timeit import timeit

       A__		   =		'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
       print(timeit('''is_pangram()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_faster()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_fastest()'''					,	setup=UpperCamelCase__    )    )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()





