





import logging

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.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


SCREAMING_SNAKE_CASE_      :			Optional[int]		  =    logging.getLogger(__name__)

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    List[Any]				,			__lowerCamelCase       :    List[Any]				,			__lowerCamelCase       :    str=None				,			__lowerCamelCase       :    Optional[int]=None      )					->   str:

			'''simple docstring'''




			if current_layer == 0:
					__lowercase					=    self.embedding_hidden_mapping_in(__lowerCamelCase      )
			else:
					__lowercase					=    hidden_states[0]

			__lowercase					=    int(self.config.num_hidden_layers / self.config.num_hidden_groups      )

			# Index of the hidden group
			__lowercase					=    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)      )

			__lowercase					=    self.albert_layer_groups[group_idx](
			    __lowerCamelCase				,			__lowerCamelCase				,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]				,			)
			__lowercase					=    layer_group_output[0]

			return (hidden_states,)





@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''    ,    UpperCAmelCase_    ,    )
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''




	def __init__(	self       :    Dict				,			__lowerCamelCase       :    List[Any]      )					->   str:

			'''simple docstring'''




			super().__init__(__lowerCamelCase      )

			__lowercase					=    AlbertTransformerWithPabee(__lowerCamelCase      )

			self.init_weights()
			__lowercase					=    0
			__lowercase					=    0
			__lowercase					=    0

			__lowercase					=    0




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    Optional[Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    threshold




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    Optional[Any]      )					->   Dict:

			'''simple docstring'''




			__lowercase					=    patience




	def       UpperCAmelCase (	self       :    List[Any]      )					->   List[str]:

			'''simple docstring'''




			__lowercase					=    0
			__lowercase					=    0




	def       UpperCAmelCase (	self       :    List[str]      )					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    self.inference_layers_num / self.inference_instances_num
			__lowercase					=    (
			    F"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="
			    F" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
			)
			print(__lowerCamelCase      )






	@add_start_docstrings_to_model_forward(__lowerCamelCase      )
	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    Optional[int]=None				,			__lowerCamelCase       :    Union[str, Any]=None				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    Any=None				,			__lowerCamelCase       :    Union[str, Any]=None				,			__lowerCamelCase       :    List[Any]=False				,			)					->   int:

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

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

			if attention_mask is None:
					__lowercase					=    torch.ones(__lowerCamelCase				,			device=__lowerCamelCase      )
			if token_type_ids is None:
					__lowercase					=    torch.zeros(__lowerCamelCase				,			dtype=torch.long				,			device=__lowerCamelCase      )

			__lowercase					=    attention_mask.unsqueeze(1      ).unsqueeze(2      )
			__lowercase					=    extended_attention_mask.to(dtype=self.dtype      )  # fp16 compatibility
			__lowercase					=    (1.0 - extended_attention_mask) * -10000.0
			__lowercase					=    self.get_head_mask(__lowerCamelCase				,			self.config.num_hidden_layers      )

			__lowercase					=    self.embeddings(
			    __lowerCamelCase				,			position_ids=__lowerCamelCase				,			token_type_ids=__lowerCamelCase				,			inputs_embeds=__lowerCamelCase      )
			__lowercase					=    embedding_output

			if self.training:
					__lowercase					=    []
					for i in range(self.config.num_hidden_layers      ):
							__lowercase					=    self.encoder.adaptive_forward(
							    __lowerCamelCase				,			current_layer=__lowerCamelCase				,			attention_mask=__lowerCamelCase				,			head_mask=__lowerCamelCase				,			)

							__lowercase					=    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
							__lowercase					=    output_layers[i](output_dropout(__lowerCamelCase      )      )
							res.append(__lowerCamelCase      )
			elif self.patience == 0:  # Use all layers for inference
					__lowercase					=    self.encoder(__lowerCamelCase				,			__lowerCamelCase				,			head_mask=__lowerCamelCase      )
					__lowercase					=    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
					__lowercase					=    [output_layers[self.config.num_hidden_layers - 1](__lowerCamelCase      )]
			else:
					__lowercase					=    0
					__lowercase					=    None
					__lowercase					=    0
					for i in range(self.config.num_hidden_layers      ):
							calculated_layer_num += 1
							__lowercase					=    self.encoder.adaptive_forward(
							    __lowerCamelCase				,			current_layer=__lowerCamelCase				,			attention_mask=__lowerCamelCase				,			head_mask=__lowerCamelCase				,			)

							__lowercase					=    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]      )      )
							__lowercase					=    output_layers[i](__lowerCamelCase      )
							if regression:
									__lowercase					=    logits.detach()
									if patient_result is not None:
											__lowercase					=    patient_result.detach()
									if (patient_result is not None) and torch.abs(patient_result - labels      ) < self.regression_threshold:
											patient_counter += 1
									else:
											__lowercase					=    0
							else:
									__lowercase					=    logits.detach().argmax(dim=1      )
									if patient_result is not None:
											__lowercase					=    patient_result.detach().argmax(dim=1      )
									if (patient_result is not None) and torch.all(labels.eq(__lowerCamelCase      )      ):
											patient_counter += 1
									else:
											__lowercase					=    0

							__lowercase					=    logits
							if patient_counter == self.patience:
									break
					__lowercase					=    [patient_result]
					self.inference_layers_num += calculated_layer_num
					self.inference_instances_num += 1

			return res





@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''    ,    UpperCAmelCase_    ,    )
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''




	def __init__(	self       :    Optional[int]				,			__lowerCamelCase       :    int      )					->   int:

			'''simple docstring'''




			super().__init__(__lowerCamelCase      )
			__lowercase					=    config.num_labels

			__lowercase					=    AlbertModelWithPabee(__lowerCamelCase      )
			__lowercase					=    nn.Dropout(config.classifier_dropout_prob      )
			__lowercase					=    nn.ModuleList(
			    [nn.Linear(config.hidden_size				,			self.config.num_labels      ) for _ in range(config.num_hidden_layers      )]      )

			self.init_weights()






	@add_start_docstrings_to_model_forward(__lowerCamelCase      )
	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    Optional[int]=None				,			__lowerCamelCase       :    int=None				,			__lowerCamelCase       :    List[Any]=None				,			__lowerCamelCase       :    Tuple=None				,			__lowerCamelCase       :    Tuple=None				,			__lowerCamelCase       :    str=None				,			__lowerCamelCase       :    Optional[Any]=None				,			)					->   Dict:

			'''simple docstring'''




			__lowercase					=    self.albert(
			    input_ids=__lowerCamelCase				,			attention_mask=__lowerCamelCase				,			token_type_ids=__lowerCamelCase				,			position_ids=__lowerCamelCase				,			head_mask=__lowerCamelCase				,			inputs_embeds=__lowerCamelCase				,			output_dropout=self.dropout				,			output_layers=self.classifiers				,			regression=self.num_labels == 1				,			)

			__lowercase					=    (logits[-1],)

			if labels is not None:
					__lowercase					=    None
					__lowercase					=    0
					for ix, logits_item in enumerate(__lowerCamelCase      ):
							if self.num_labels == 1:
									#  We are doing regression
									__lowercase					=    MSELoss()
									__lowercase					=    loss_fct(logits_item.view(-1      )				,			labels.view(-1      )      )
							else:
									__lowercase					=    CrossEntropyLoss()
									__lowercase					=    loss_fct(logits_item.view(-1				,			self.num_labels      )				,			labels.view(-1      )      )
							if total_loss is None:
									__lowercase					=    loss
							else:
									total_loss += loss * (ix + 1)
							total_weights += ix + 1
					__lowercase					=    (total_loss / total_weights,) + outputs

			return outputs











SCREAMING_SNAKE_CASE_      :			str		  =      [chr(i) for i in range(32, 126)]
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			List[str]		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Dict		  =      list(reversed(range(len(alphabets))))
SCREAMING_SNAKE_CASE_      :			int		  =      []
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      0



def    SCREAMING_SNAKE_CASE							(			)		->						int:
		global gear_one_pos
		global gear_two_pos
		global gear_three_pos
		__lowercase					=    gear_one[0]
		gear_one.append(snake_case       )
		del gear_one[0]
		gear_one_pos += 1
		if gear_one_pos % int(len(snake_case       )       ) == 0:
				__lowercase					=    gear_two[0]
				gear_two.append(snake_case       )
				del gear_two[0]
				gear_two_pos += 1
				if gear_two_pos % int(len(snake_case       )       ) == 0:
						__lowercase					=    gear_three[0]
						gear_three.append(snake_case       )
						del gear_three[0]
						gear_three_pos += 1



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    alphabets.index(snake_case       )
		__lowercase					=    gear_one[target]
		__lowercase					=    gear_two[target]
		__lowercase					=    gear_three[target]
		__lowercase					=    reflector[target]
		__lowercase					=    gear_three.index(snake_case       )
		__lowercase					=    gear_two.index(snake_case       )
		__lowercase					=    gear_one.index(snake_case       )
		code.append(alphabets[target]       )
		rotator()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      list(input('''Type your message:\n'''))
		while True:
				try:
						SCREAMING_SNAKE_CASE_      :			List[str]		  =      int(input('''Please set token:(must be only digits)\n'''))
						break
				except Exception as error:
						print(error)
		for _ in range(token):
				rotator()
		for j in decode:
				engine(j)
		print('''\n''' + ''''''.join(code))
		print(
		    F"""\nYour Token is {token} please write it down.\nIf you want to decode """
		    '''this message again you should input same digits as token!'''
		)




