
"""simple docstring"""







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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   RobertaConfig
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   'roberta'





					def __init__(				self   :							Any							,						snake_case_   :							Any    ):
												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,\n    also takes care of multi-layer training. '      ,		lowercase__      ,		)
class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   RobertaConfig
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   'roberta'





					def __init__(				self   :							Tuple							,						snake_case_   :							Dict    ):
												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 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str=None							,						snake_case_   :							Optional[Any]=None							,						snake_case_   :							Optional[Any]=None							,						snake_case_   :							Dict=None							,						snake_case_   :							Optional[int]=None							,						snake_case_   :							List[Any]=None							,						snake_case_   :							Any=None							,						snake_case_   :							Optional[int]=-1							,						snake_case_   :							Union[str, Any]=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


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = 10							,    SCREAMING_SNAKE_CASE = 10_00							,    SCREAMING_SNAKE_CASE = True )  ->     int:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError("Invalid value for min_val or max_val (min_value < max_value)" )
							return min_val if option else max_val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							return int((number_a + number_a) / 2 )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     None:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError("argument value for lower and higher must be(lower > higher)" )

							if not lower < to_guess < higher:
														raise ValueError(
														    "guess value must be within the range of lower and higher value" )

							def answer(SCREAMING_SNAKE_CASE ) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print("started..." )

							__snake_case     			=       lower
							__snake_case     			=       higher

							__snake_case     			=       []

							while True:
														__snake_case     			=       get_avg(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														last_numbers.append(SCREAMING_SNAKE_CASE )

														if answer(SCREAMING_SNAKE_CASE ) == "low":
																					__snake_case     			=       number
														elif answer(SCREAMING_SNAKE_CASE ) == "high":
																					__snake_case     			=       number
														else:
																					break

							print(F'''guess the number : {last_numbers[-1]}''' )
							print(F'''details : {last_numbers!s}''' )
def   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							__snake_case     			=       int(input("Enter lower value : " ).strip() )
							__snake_case     			=       int(input("Enter high value : " ).strip() )
							__snake_case     			=       int(input("Enter value to guess : " ).strip() )
							guess_the_number(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()
