code: 
  '''simple docstring'''
  import collections
  import types
  
  import numpy as np
  
  from ..utils import (
      add_end_docstrings,
      is_tensorflow_probability_available,
      is_tf_available,
      is_torch_available,
      requires_backends,
  )
  from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException
  
  
  if is_torch_available():
  						import torch
  
  						from ..models.auto.modeling_auto import (
  						    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  						    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  						)
  
  if is_tf_available() and is_tensorflow_probability_available():
  						import tensorflow as tf
  						import tensorflow_probability as tfp
  
  						from ..models.auto.modeling_tf_auto import (
  						    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  						    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  						)
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __call__(			self							: List[str]     ,  UpperCamelCase__							: List[str]=None     ,  UpperCamelCase__							: Union[str, Any]=None     ,  **UpperCamelCase__							: Any			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				requires_backends(self     ,  '''pandas'''			)
  				import pandas as pd
  
  				if table is None:
  						raise ValueError('''Keyword argument `table` cannot be None.'''			)
  				elif query is None:
  						if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and table.get('''query'''			) is not None and table.get('''table'''			) is not None:
  								snake_case     :					Tuple     =	[table]
  						elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and len(UpperCamelCase__			) > 0:
  								if not all(isinstance(UpperCamelCase__     ,  UpperCamelCase__			) for d in table			):
  										raise ValueError(
  										    f'Keyword argument `table` should be a list of dict, but is {(type(UpperCamelCase__			) for d in table)}'			)
  
  								if table[0].get('''query'''			) is not None and table[0].get('''table'''			) is not None:
  										snake_case     :					Union[str, Any]     =	table
  								else:
  										raise ValueError(
  										    '''If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'''
  										    f' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'			)
  						elif Dataset is not None and isinstance(UpperCamelCase__     ,  UpperCamelCase__			) or isinstance(UpperCamelCase__     ,  types.GeneratorType			):
  								return table
  						else:
  								raise ValueError(
  								    '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
  								    f'is {type(UpperCamelCase__			)})'			)
  				else:
  						snake_case     :					str     =	[{'''table''': table, '''query''': query}]
  
  				for tqa_pipeline_input in tqa_pipeline_inputs:
  						if not isinstance(tqa_pipeline_input['''table''']     ,  pd.DataFrame			):
  								if tqa_pipeline_input["table"] is None:
  										raise ValueError('''Table cannot be None.'''			)
  
  								snake_case     :					str     =	pd.DataFrame(tqa_pipeline_input['''table''']			)
  
  				return tqa_pipeline_inputs
  
  
  
  
  
  
  
  @add_end_docstrings(__SCREAMING_SNAKE_CASE				)
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""table,query"""
  
  
  
  		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: Union[str, Any]=TableQuestionAnsweringArgumentHandler()     ,  *UpperCamelCase__							: int     ,  **UpperCamelCase__							: Dict			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__(*UpperCamelCase__     ,  **UpperCamelCase__			)
  				snake_case     :					Union[str, Any]     =	args_parser
  
  				self.check_model_type(
  				    dict(
  				        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
  				        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()			)
  				    if self.framework == '''tf'''
  				    else dict(
  				        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()			)			)
  
  				snake_case     :					List[str]     =	bool(getattr(self.model.config     ,  '''aggregation_labels'''     ,  UpperCamelCase__			)			) and bool(
  				    getattr(self.model.config     ,  '''num_aggregation_labels'''     ,  UpperCamelCase__			)			)
  				snake_case     :					Union[str, Any]     =	'''tapas''' if hasattr(self.model.config     ,  '''aggregation_labels'''			) else None
  
  
  
  		def 			lowerCAmelCase							(			self							: int     ,  **UpperCamelCase__							: List[str]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.model(**UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: str     ,  **UpperCamelCase__							: List[str]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self.framework == "pt":
  						snake_case     :					List[Any]     =	[]
  						snake_case     :					Optional[Any]     =	[]
  						snake_case     :					Tuple     =	None
  						snake_case     :					Tuple     =	inputs['''input_ids'''].shape[0]
  
  						snake_case     :					Optional[Any]     =	inputs['''input_ids'''].to(self.device			)
  						snake_case     :					int     =	inputs['''attention_mask'''].to(self.device			)
  						snake_case     :					Optional[Any]     =	inputs['''token_type_ids'''].to(self.device			)
  						snake_case     :					Any     =	None
  
  						for index in range(UpperCamelCase__			):
  								# If sequences have already been processed, the token type IDs will be created according to the previous
  								# answer.
  								if prev_answers is not None:
  										snake_case     :					Dict     =	token_type_ids_example[:, 3]  # shape (seq_len,)
  										snake_case     :					str     =	np.zeros_like(prev_labels_example.cpu().numpy()			)  # shape (seq_len,)
  
  										snake_case     :					str     =	token_type_ids[index]  # shape (seq_len, 7)
  										for i in range(model_labels.shape[0]			):
  												snake_case     :					Any     =	token_type_ids_example[:, 0].tolist()[i]
  												snake_case     :					List[str]     =	token_type_ids_example[:, 1].tolist()[i] - 1
  												snake_case     :					Union[str, Any]     =	token_type_ids_example[:, 2].tolist()[i] - 1
  
  												if row_id >= 0 and col_id >= 0 and segment_id == 1:
  														snake_case     :					Dict     =	int(prev_answers[(col_id, row_id)]			)
  
  										snake_case     :					Union[str, Any]     =	torch.from_numpy(UpperCamelCase__			).type(torch.long			).to(self.device			)
  
  								snake_case     :					List[Any]     =	input_ids[index]
  								snake_case     :					List[Any]     =	attention_mask[index]  # shape (seq_len,)
  								snake_case     :					Dict     =	token_type_ids[index]  # shape (seq_len, 7)
  								snake_case     :					int     =	self.model(
  								    input_ids=input_ids_example.unsqueeze(0			)     ,  attention_mask=attention_mask_example.unsqueeze(0			)     ,  token_type_ids=token_type_ids_example.unsqueeze(0			)     ,  )
  								snake_case     :					Dict     =	outputs.logits
  
  								if self.aggregate:
  										all_aggregations.append(outputs.logits_aggregation			)
  
  								all_logits.append(UpperCamelCase__			)
  
  								snake_case     :					List[str]     =	torch.distributions.Bernoulli(logits=UpperCamelCase__			)
  								snake_case     :					Dict     =	dist_per_token.probs * attention_mask_example.type(torch.floataa			).to(
  								    dist_per_token.probs.device			)
  
  								snake_case     :					str     =	collections.defaultdict(UpperCamelCase__			)
  								for i, p in enumerate(probabilities.squeeze().tolist()			):
  										snake_case     :					Dict     =	token_type_ids_example[:, 0].tolist()[i]
  										snake_case     :					List[Any]     =	token_type_ids_example[:, 1].tolist()[i] - 1
  										snake_case     :					int     =	token_type_ids_example[:, 2].tolist()[i] - 1
  										if col >= 0 and row >= 0 and segment_id == 1:
  												coords_to_probs[(col, row)].append(UpperCamelCase__			)
  
  								snake_case     :					Optional[Any]     =	{key: np.array(coords_to_probs[key]			).mean() > 0.5 for key in coords_to_probs}
  
  						snake_case     :					Any     =	torch.cat(tuple(UpperCamelCase__			)     ,  0			)
  
  						return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCamelCase__			)     ,  0			))
  				else:
  						snake_case     :					List[str]     =	[]
  						snake_case     :					Any     =	[]
  						snake_case     :					List[Any]     =	None
  						snake_case     :					Optional[int]     =	inputs['''input_ids'''].shape[0]
  
  						snake_case     :					Optional[int]     =	inputs['''input_ids''']
  						snake_case     :					List[Any]     =	inputs['''attention_mask''']
  						snake_case     :					Dict     =	inputs['''token_type_ids'''].numpy()
  						snake_case     :					Optional[int]     =	None
  
  						for index in range(UpperCamelCase__			):
  								# If sequences have already been processed, the token type IDs will be created according to the previous
  								# answer.
  								if prev_answers is not None:
  										snake_case     :					Optional[int]     =	token_type_ids_example[:, 3]  # shape (seq_len,)
  										snake_case     :					List[Any]     =	np.zeros_like(UpperCamelCase__     ,  dtype=np.intaa			)  # shape (seq_len,)
  
  										snake_case     :					Dict     =	token_type_ids[index]  # shape (seq_len, 7)
  										for i in range(model_labels.shape[0]			):
  												snake_case     :					Union[str, Any]     =	token_type_ids_example[:, 0].tolist()[i]
  												snake_case     :					int     =	token_type_ids_example[:, 1].tolist()[i] - 1
  												snake_case     :					Union[str, Any]     =	token_type_ids_example[:, 2].tolist()[i] - 1
  
  												if row_id >= 0 and col_id >= 0 and segment_id == 1:
  														snake_case     :					List[str]     =	int(prev_answers[(col_id, row_id)]			)
  
  										snake_case     :					Any     =	model_labels
  
  								snake_case     :					Optional[int]     =	input_ids[index]
  								snake_case     :					str     =	attention_mask[index]  # shape (seq_len,)
  								snake_case     :					int     =	token_type_ids[index]  # shape (seq_len, 7)
  								snake_case     :					str     =	self.model(
  								    input_ids=np.expand_dims(UpperCamelCase__     ,  axis=0			)     ,  attention_mask=np.expand_dims(UpperCamelCase__     ,  axis=0			)     ,  token_type_ids=np.expand_dims(UpperCamelCase__     ,  axis=0			)     ,  )
  								snake_case     :					Tuple     =	outputs.logits
  
  								if self.aggregate:
  										all_aggregations.append(outputs.logits_aggregation			)
  
  								all_logits.append(UpperCamelCase__			)
  
  								snake_case     :					Tuple     =	tfp.distributions.Bernoulli(logits=UpperCamelCase__			)
  								snake_case     :					Any     =	dist_per_token.probs_parameter() * tf.cast(UpperCamelCase__     ,  tf.floataa			)
  
  								snake_case     :					int     =	collections.defaultdict(UpperCamelCase__			)
  								snake_case     :					Union[str, Any]     =	token_type_ids_example
  								for i, p in enumerate(tf.squeeze(UpperCamelCase__			).numpy().tolist()			):
  										snake_case     :					Optional[Any]     =	token_type_ids_example[:, 0].tolist()[i]
  										snake_case     :					Tuple     =	token_type_ids_example[:, 1].tolist()[i] - 1
  										snake_case     :					List[Any]     =	token_type_ids_example[:, 2].tolist()[i] - 1
  										if col >= 0 and row >= 0 and segment_id == 1:
  												coords_to_probs[(col, row)].append(UpperCamelCase__			)
  
  								snake_case     :					List[str]     =	{key: np.array(coords_to_probs[key]			).mean() > 0.5 for key in coords_to_probs}
  
  						snake_case     :					List[Any]     =	tf.concat(tuple(UpperCamelCase__			)     ,  0			)
  
  						return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(UpperCamelCase__			)     ,  0			))
  
  
  
  		def __call__(			self							: List[Any]     ,  *UpperCamelCase__							: Union[str, Any]     ,  **UpperCamelCase__							: List[Any]			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	self._args_parser(*UpperCamelCase__     ,  **UpperCamelCase__			)
  
  				snake_case     :					Any     =	super().__call__(UpperCamelCase__     ,  **UpperCamelCase__			)
  				if len(UpperCamelCase__			) == 1:
  						return results[0]
  				return results
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[int]=None     ,  UpperCamelCase__							: int=None     ,  UpperCamelCase__							: str=None     ,  **UpperCamelCase__							: Optional[int]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	{}
  				if padding is not None:
  						snake_case     :					List[str]     =	padding
  				if truncation is not None:
  						snake_case     :					Any     =	truncation
  
  				snake_case     :					Tuple     =	{}
  				if sequential is not None:
  						snake_case     :					Tuple     =	sequential
  				return preprocess_params, forward_params, {}
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Dict=None     ,  UpperCamelCase__							: Union[str, Any]=True     ,  UpperCamelCase__							: Optional[Any]=None			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if truncation is None:
  						if self.type == "tapas":
  								snake_case     :					Dict     =	'''drop_rows_to_fit'''
  						else:
  								snake_case     :					Tuple     =	'''do_not_truncate'''
  
  				snake_case     :					str     =	pipeline_input['''table'''], pipeline_input['''query''']
  				if table.empty:
  						raise ValueError('''table is empty'''			)
  				if query is None or query == "":
  						raise ValueError('''query is empty'''			)
  				snake_case     :					Tuple     =	self.tokenizer(UpperCamelCase__     ,  UpperCamelCase__     ,  return_tensors=self.framework     ,  truncation=UpperCamelCase__     ,  padding=UpperCamelCase__			)
  				snake_case     :					Tuple     =	table
  				return inputs
  
  
  
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Dict=False			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	model_inputs.pop('''table'''			)
  
  				if self.type == "tapas":
  						if sequential:
  								snake_case     :					str     =	self.sequential_inference(**UpperCamelCase__			)
  						else:
  								snake_case     :					Optional[int]     =	self.batch_inference(**UpperCamelCase__			)
  				else:
  						snake_case     :					int     =	self.model.generate(**UpperCamelCase__			)
  				snake_case     :					Any     =	{'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
  				return model_outputs
  
  
  
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Dict			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	model_outputs['''model_inputs''']
  				snake_case     :					Any     =	model_outputs['''table''']
  				snake_case     :					Optional[int]     =	model_outputs['''outputs''']
  				if self.type == "tapas":
  						if self.aggregate:
  								snake_case     :					int     =	outputs[:2]
  								snake_case     :					Optional[int]     =	self.tokenizer.convert_logits_to_predictions(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
  								snake_case     :					Union[str, Any]     =	predictions
  								snake_case     :					str     =	{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(UpperCamelCase__			)}
  
  								snake_case     :					List[str]     =	self.model.config.no_aggregation_label_index
  								snake_case     :					List[str]     =	{
  								    i: aggregators[i] + ''' > ''' for i, pred in enumerate(UpperCamelCase__			) if pred != no_agg_label_index
  								}
  						else:
  								snake_case     :					Union[str, Any]     =	outputs[0]
  								snake_case     :					str     =	self.tokenizer.convert_logits_to_predictions(UpperCamelCase__     ,  UpperCamelCase__			)
  								snake_case     :					List[Any]     =	predictions[0]
  								snake_case     :					Any     =	{}
  								snake_case     :					str     =	{}
  						snake_case     :					Union[str, Any]     =	[]
  						for index, coordinates in enumerate(UpperCamelCase__			):
  								snake_case     :					Dict     =	[table.iat[coordinate] for coordinate in coordinates]
  								snake_case     :					Dict     =	aggregators.get(UpperCamelCase__     ,  ''''''			)
  								snake_case     :					Dict     =	aggregators_prefix.get(UpperCamelCase__     ,  ''''''			)
  								snake_case     :					Optional[Any]     =	{
  								    '''answer''': aggregator_prefix + ''', '''.join(UpperCamelCase__			),
  								    '''coordinates''': coordinates,
  								    '''cells''': [table.iat[coordinate] for coordinate in coordinates],
  								}
  								if aggregator:
  										snake_case     :					Tuple     =	aggregator
  
  								answers.append(UpperCamelCase__			)
  						if len(UpperCamelCase__			) == 0:
  								raise PipelineException('''Empty answer'''			)
  				else:
  						snake_case     :					str     =	[{'''answer''': answer} for answer in self.tokenizer.batch_decode(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__			)]
  
  				return answers if len(UpperCamelCase__			) > 1 else answers[0]
  
  
  
style_context: 
  '''simple docstring'''
  import itertools
  from pathlib import Path
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	[tuple(SCREAMING_SNAKE_CASE__ ) for login in logins]
  
  		snake_case     :					Dict     =	{char for login in split_logins for char in login}
  
  		for permutation in itertools.permutations(SCREAMING_SNAKE_CASE__ ):
  				snake_case     :					Optional[int]     =	True
  				for login in logins:
  						if not (
  						    permutation.index(login[0] )
  						    < permutation.index(login[1] )
  						    < permutation.index(login[2] )
  						):
  								snake_case     :					int     =	False
  								break
  
  				if satisfied:
  						return int(''''''.join(SCREAMING_SNAKE_CASE__ ) )
  
  		raise Exception('''Unable to find the secret passcode''' )
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ = "keylog.txt" )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	Path(SCREAMING_SNAKE_CASE__ ).parent.joinpath(SCREAMING_SNAKE_CASE__ ).read_text().splitlines()
  
  		return find_secret_passcode(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						print(f"{solution() = }")
  
  
  