

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). '''			,						lowercase_			,						)
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    RobertaConfig
				__SCREAMING_SNAKE_CASE      :				str     =    '''roberta'''




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

											snake_case_           =					RobertaEmbeddings(snake_case )
											self.init_weights()







@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''			,						lowercase_			,						)
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    RobertaConfig
				__SCREAMING_SNAKE_CASE      :				Tuple     =    '''roberta'''




				def __init__(      self   ,     snake_case ):
											super().__init__(snake_case )
											snake_case_           =					config.num_labels
											snake_case_           =					config.num_hidden_layers

											snake_case_           =					DeeRobertaModel(snake_case )
											snake_case_           =					nn.Dropout(config.hidden_dropout_prob )
											snake_case_           =					nn.Linear(config.hidden_size   ,     self.config.num_labels )




				@add_start_docstrings_to_model_forward(snake_case )
				def 					a							(      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   ,     ):
											snake_case_           =					self.num_layers
											try:
																		snake_case_           =					self.roberta(
																		    snake_case   ,     attention_mask=snake_case   ,     token_type_ids=snake_case   ,     position_ids=snake_case   ,     head_mask=snake_case   ,     inputs_embeds=snake_case   ,     )

																		snake_case_           =					outputs[1]

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

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

																		# work with highway exits
																		snake_case_           =					[]
																		for highway_exit in outputs[-1]:
																									snake_case_           =					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
																																snake_case_           =					MSELoss()
																																snake_case_           =					loss_fct(highway_logits.view(-1 )   ,     labels.view(-1 ) )
																									else:
																																snake_case_           =					CrossEntropyLoss()
																																snake_case_           =					loss_fct(highway_logits.view(-1   ,     self.num_labels )   ,     labels.view(-1 ) )
																									highway_losses.append(snake_case )

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

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







import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

_UpperCAmelCase	:     Optional[int]		       = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_UpperCAmelCase	:     Dict		       = [file for file in filepaths if file != file.lower()]
if upper_files:
					print(F'''{len(upper_files)} files contain uppercase characters:''')
					print("""\n""".join(upper_files) + """\n""")

_UpperCAmelCase	:     Tuple		       = [file for file in filepaths if """ """ in file]
if space_files:
					print(F'''{len(space_files)} files contain space characters:''')
					print("""\n""".join(space_files) + """\n""")

_UpperCAmelCase	:     Union[str, Any]		       = [file for file in filepaths if """-""" in file]
if hyphen_files:
					print(F'''{len(hyphen_files)} files contain hyphen characters:''')
					print("""\n""".join(hyphen_files) + """\n""")

_UpperCAmelCase	:     List[Any]		       = [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(F'''{len(nodir_files)} files are not in a directory:''')
					print("""\n""".join(nodir_files) + """\n""")

_UpperCAmelCase	:     List[str]		       = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)




