





"""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       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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





class        _lowerCamelCase      (       nn.Module ):


						def __init__(self   ,							__a		) ->   Optional[Any]:
								super().__init__()
								UpperCamelCase   =	config.output_attentions
								UpperCamelCase   =	config.output_hidden_states
								UpperCamelCase   =	nn.ModuleList([BertLayer(__a		) for _ in range(config.num_hidden_layers		)]		)
								UpperCamelCase   =	nn.ModuleList([BertHighway(__a		) for _ in range(config.num_hidden_layers		)]		)

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


						def     snake_case_   (self   ,							__a		) ->   Union[str, Any]:
								if (type(__a		) is float) or (type(__a		) is int):
										for i in range(len(self.early_exit_entropy		)		):
												UpperCamelCase   =	x
								else:
										UpperCamelCase   =	x


						def     snake_case_   (self   ,							__a		) ->   Any:
								UpperCamelCase   =	pooler.state_dict()
								for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
												param.copy_(loaded_model[name]		)




						def     snake_case_   (self   ,							__a   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							) ->   int:
								UpperCamelCase   =	()
								UpperCamelCase   =	()
								UpperCamelCase   =	()
								for i, layer_module in enumerate(self.layer		):
										if self.output_hidden_states:
												UpperCamelCase   =	all_hidden_states + (hidden_states,)

										UpperCamelCase   =	layer_module(
										    __a   ,							__a   ,							head_mask[i]   ,							__a   ,							__a		)
										UpperCamelCase   =	layer_outputs[0]

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

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

										UpperCamelCase   =	self.highway[i](__a		)
										# logits, pooled_output

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

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

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

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

								UpperCamelCase   =	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). "  ,			_lowercase  ,			)
class        _lowerCamelCase      (       _lowercase ):


						def __init__(self   ,							__a		) ->   int:
								super().__init__(__a		)
								UpperCamelCase   =	config

								UpperCamelCase   =	BertEmbeddings(__a		)
								UpperCamelCase   =	DeeBertEncoder(__a		)
								UpperCamelCase   =	BertPooler(__a		)

								self.init_weights()


						def     snake_case_   (self		) ->   Union[str, Any]:
								self.encoder.init_highway_pooler(self.pooler		)


						def     snake_case_   (self		) ->   Optional[int]:
								return self.embeddings.word_embeddings


						def     snake_case_   (self   ,							__a		) ->   Tuple:
								UpperCamelCase   =	value


						def     snake_case_   (self   ,							__a		) ->   Any:
								for layer, heads in heads_to_prune.items():
										self.encoder.layer[layer].attention.prune_heads(__a		)




						@add_start_docstrings_to_model_forward(__a		)
						def     snake_case_   (self   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							) ->   List[Any]:
								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:
										UpperCamelCase   =	input_ids.size()
								elif inputs_embeds is not None:
										UpperCamelCase   =	inputs_embeds.size()[:-1]
								else:
										raise ValueError("You have to specify either input_ids or inputs_embeds"		)

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

								if attention_mask is None:
										UpperCamelCase   =	torch.ones(__a   ,							device=__a		)
								if encoder_attention_mask is None:
										UpperCamelCase   =	torch.ones(__a   ,							device=__a		)
								if token_type_ids is None:
										UpperCamelCase   =	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.
								UpperCamelCase   =	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:
										UpperCamelCase   =	encoder_attention_mask[:, None, :, :]
								if encoder_attention_mask.dim() == 2:
										UpperCamelCase   =	encoder_attention_mask[:, None, None, :]

								UpperCamelCase   =	encoder_extended_attention_mask.to(
								    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
								UpperCamelCase   =	(1.0 - encoder_extended_attention_mask) * -10000.0

								# 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]
								UpperCamelCase   =	self.get_head_mask(__a   ,							self.config.num_hidden_layers		)

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

								UpperCamelCase   =	(
								    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        _lowerCamelCase      (       _lowercase ):


						def __init__(self   ,							__a   ,							__a		) ->   Any:
								UpperCamelCase   =	message
								UpperCamelCase   =	exit_layer  # start from 1!





class        _lowerCamelCase      (       nn.Module ):


						def __init__(self   ,							__a		) ->   Tuple:
								super().__init__()
								UpperCamelCase   =	BertPooler(__a		)
								UpperCamelCase   =	nn.Dropout(config.hidden_dropout_prob		)
								UpperCamelCase   =	nn.Linear(config.hidden_size   ,							config.num_labels		)




						def     snake_case_   (self   ,							__a		) ->   List[str]:
								# Pooler
								UpperCamelCase   =	encoder_outputs[0]
								UpperCamelCase   =	self.pooler(__a		)
								# "return" pooler_output

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

								# Dropout and classification
								UpperCamelCase   =	bmodel_output[1]

								UpperCamelCase   =	self.dropout(__a		)
								UpperCamelCase   =	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. "  ,			_lowercase  ,			)
class        _lowerCamelCase      (       _lowercase ):


						def __init__(self   ,							__a		) ->   int:
								super().__init__(__a		)
								UpperCamelCase   =	config.num_labels
								UpperCamelCase   =	config.num_hidden_layers

								UpperCamelCase   =	DeeBertModel(__a		)
								UpperCamelCase   =	nn.Dropout(config.hidden_dropout_prob		)
								UpperCamelCase   =	nn.Linear(config.hidden_size   ,							self.config.num_labels		)

								self.init_weights()




						@add_start_docstrings_to_model_forward(__a		)
						def     snake_case_   (self   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=None   ,							__a=-1   ,							__a=False   ,							) ->   str:
								UpperCamelCase   =	self.num_layers
								try:
										UpperCamelCase   =	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

										UpperCamelCase   =	outputs[1]

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

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

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

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


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in range(len(string.ascii_uppercase    )    ):
				UpperCamelCase   =	""
				for symbol in message:
						if symbol in string.ascii_uppercase:
								UpperCamelCase   =	string.ascii_uppercase.find(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	num - key
								if num < 0:
										UpperCamelCase   =	num + len(string.ascii_uppercase    )
								UpperCamelCase   =	translated + string.ascii_uppercase[num]
						else:
								UpperCamelCase   =	translated + symbol
				print(F"Decryption using Key #{key}: {translated}"    )


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	input("Encrypted message: "    )
		UpperCamelCase   =	message.upper()
		decrypt(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






