





'''simple docstring'''






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,
)


A :			Optional[Any]							   =      logging.getLogger(__name__)







class       lowerCamelCase       (    __snake_case	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[str]							,  __snake_case						:	Union[str, Any]=None							,  __snake_case						:	List[str]=None							):





											'''simple docstring'''





											if current_layer == 0:
																	_snake_case: str	       =		self.embedding_hidden_mapping_in(lowerCamelCase_							)
											else:
																	_snake_case: Dict	       =		hidden_states[0]

											_snake_case: Dict	       =		int(self.config.num_hidden_layers / self.config.num_hidden_groups							)

											# Index of the hidden group
											_snake_case: Optional[Any]	       =		int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)							)

											_snake_case: Tuple	       =		self.albert_layer_groups[group_idx](
											    lowerCamelCase_							,  lowerCamelCase_							,  head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]							,  )
											_snake_case: Tuple	       =		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."  ,				__snake_case  ,				)
class       lowerCamelCase       (    __snake_case	):


					def __init__(       self						:	int							,  __snake_case						:	int							):





											'''simple docstring'''





											super().__init__(lowerCamelCase_							)

											_snake_case: Union[str, Any]	       =		AlbertTransformerWithPabee(lowerCamelCase_							)

											self.init_weights()
											_snake_case: List[str]	       =		0
											_snake_case: Tuple	       =		0
											_snake_case: int	       =		0

											_snake_case: Union[str, Any]	       =		0


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Dict							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		threshold


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: int	       =		patience


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		0
											_snake_case: Dict	       =		0


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.inference_layers_num / self.inference_instances_num
											_snake_case: List[Any]	       =		(
											    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  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	Dict=None							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	Union[str, Any]=None							,  __snake_case						:	Any=None							,  __snake_case						:	List[str]=None							,  __snake_case						:	int=None							,  __snake_case						:	List[str]=None							,  __snake_case						:	int=False							,  ):





											'''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:
																	_snake_case: Union[str, Any]	       =		input_ids.size()
											elif inputs_embeds is not None:
																	_snake_case: int	       =		inputs_embeds.size()[:-1]
											else:
																	raise ValueError('You have to specify either input_ids or inputs_embeds'							)

											_snake_case: Tuple	       =		input_ids.device if input_ids is not None else inputs_embeds.device

											if attention_mask is None:
																	_snake_case: List[Any]	       =		torch.ones(lowerCamelCase_							,  device=lowerCamelCase_							)
											if token_type_ids is None:
																	_snake_case: Any	       =		torch.zeros(lowerCamelCase_							,  dtype=torch.long							,  device=lowerCamelCase_							)

											_snake_case: Optional[Any]	       =		attention_mask.unsqueeze(1							).unsqueeze(2							)
											_snake_case: Dict	       =		extended_attention_mask.to(dtype=self.dtype							)  # fp16 compatibility
											_snake_case: List[str]	       =		(1.0 - extended_attention_mask) * -1_00_00.0
											_snake_case: List[Any]	       =		self.get_head_mask(lowerCamelCase_							,  self.config.num_hidden_layers							)

											_snake_case: Optional[int]	       =		self.embeddings(
											    lowerCamelCase_							,  position_ids=lowerCamelCase_							,  token_type_ids=lowerCamelCase_							,  inputs_embeds=lowerCamelCase_							)
											_snake_case: str	       =		embedding_output

											if self.training:
																	_snake_case: str	       =		[]
																	for i in range(self.config.num_hidden_layers							):
																							_snake_case: Tuple	       =		self.encoder.adaptive_forward(
																							    lowerCamelCase_							,  current_layer=lowerCamelCase_							,  attention_mask=lowerCamelCase_							,  head_mask=lowerCamelCase_							,  )

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

																							_snake_case: Union[str, Any]	       =		self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]							)							)
																							_snake_case: Optional[int]	       =		output_layers[i](lowerCamelCase_							)
																							if regression:
																													_snake_case: Any	       =		logits.detach()
																													if patient_result is not None:
																																			_snake_case: List[str]	       =		patient_result.detach()
																													if (patient_result is not None) and torch.abs(patient_result - labels							) < self.regression_threshold:
																																			patient_counter += 1
																													else:
																																			_snake_case: str	       =		0
																							else:
																													_snake_case: List[Any]	       =		logits.detach().argmax(dim=1							)
																													if patient_result is not None:
																																			_snake_case: Dict	       =		patient_result.detach().argmax(dim=1							)
																													if (patient_result is not None) and torch.all(labels.eq(lowerCamelCase_							)							):
																																			patient_counter += 1
																													else:
																																			_snake_case: Dict	       =		0

																							_snake_case: Optional[int]	       =		logits
																							if patient_counter == self.patience:
																													break
																	_snake_case: Union[str, Any]	       =		[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\n    the pooled output) e.g. for GLUE tasks. "  ,				__snake_case  ,				)
class       lowerCamelCase       (    __snake_case	):


					def __init__(       self						:	str							,  __snake_case						:	Optional[Any]							):





											'''simple docstring'''





											super().__init__(lowerCamelCase_							)
											_snake_case: List[str]	       =		config.num_labels

											_snake_case: Any	       =		AlbertModelWithPabee(lowerCamelCase_							)
											_snake_case: Union[str, Any]	       =		nn.Dropout(config.classifier_dropout_prob							)
											_snake_case: Dict	       =		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  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	Dict=None							,  __snake_case						:	str=None							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	Optional[Any]=None							,  __snake_case						:	int=None							,  __snake_case						:	List[str]=None							,  __snake_case						:	Any=None							,  ):





											'''simple docstring'''





											_snake_case: List[Any]	       =		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							,  )

											_snake_case: Any	       =		(logits[-1],)

											if labels is not None:
																	_snake_case: Optional[int]	       =		None
																	_snake_case: Union[str, Any]	       =		0
																	for ix, logits_item in enumerate(lowerCamelCase_							):
																							if self.num_labels == 1:
																													#  We are doing regression
																													_snake_case: Tuple	       =		MSELoss()
																													_snake_case: Optional[Any]	       =		loss_fct(logits_item.view(-1							)							,  labels.view(-1							)							)
																							else:
																													_snake_case: Tuple	       =		CrossEntropyLoss()
																													_snake_case: Dict	       =		loss_fct(logits_item.view(-1							,  self.num_labels							)							,  labels.view(-1							)							)
																							if total_loss is None:
																													_snake_case: int	       =		loss
																							else:
																													total_loss += loss * (ix + 1)
																							total_weights += ix + 1
																	_snake_case: Union[str, Any]	       =		(total_loss / total_weights,) + outputs

											return outputs










import itertools
from pathlib import Path







def       lowerCamelCase_  (    _a       :						list[str]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					[tuple(_a    ) for login in logins]

    UpperCAmelCase_     :       str  		=					{char for login in split_logins for char in login}

    for permutation in itertools.permutations(_a    ):
        UpperCAmelCase_     :       str  		=					True
        for login in logins:
            if not (
                permutation.index(login[0]    )
                < permutation.index(login[1]    )
                < permutation.index(login[2]    )
            ):
                UpperCAmelCase_     :       Optional[int]  		=					False
                break

        if satisfied:
            return int("""""".join(_a    )    )

    raise Exception("""Unable to find the secret passcode"""    )







def       lowerCamelCase_  (    _a       :						str = "keylog.txt"    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					Path(_a    ).parent.joinpath(_a    ).read_text().splitlines()

    return find_secret_passcode(_a    )


if __name__ == "__main__":
      print(F"{solution() = }")
