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 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def __call__(     self		,       __a=None		,       __a=None		,       **__a							):
  
  						'''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(__a		,       __a							) and table.get('query'							) is not None and table.get('table'							) is not None:
  																__a    : Optional[int]      					=						[table]
  											elif isinstance(__a		,       __a							) and len(__a							) > 0:
  																if not all(isinstance(__a		,       __a							) for d in table							):
  																					raise ValueError(
  																					    f"""Keyword argument `table` should be a list of dict, but is {(type(__a							) for d in table)}"""							)
  
  																if table[0].get('query'							) is not None and table[0].get('table'							) is not None:
  																					__a    : Dict      					=						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(__a		,       __a							) or isinstance(__a		,       types.GeneratorType							):
  																return table
  											else:
  																raise ValueError(
  																    'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
  																    f"""is {type(__a							)})"""							)
  						else:
  											__a    : List[Any]      					=						[{'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.'							)
  
  																__a    : int      					=						pd.DataFrame(tqa_pipeline_input['table']							)
  
  						return tqa_pipeline_inputs
  
  
  
  
  
  @add_end_docstrings(lowerCAmelCase_      )
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	A_			    =					"table,query"
  
  
  
  
  	def __init__(     self		,       __a=TableQuestionAnsweringArgumentHandler()		,       *__a		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__(*__a		,       **__a							)
  						__a    : int      					=						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()							)							)
  
  						__a    : int      					=						bool(getattr(self.model.config		,       'aggregation_labels'		,       __a							)							) and bool(
  						    getattr(self.model.config		,       'num_aggregation_labels'		,       __a							)							)
  						__a    : List[str]      					=						'tapas' if hasattr(self.model.config		,       'aggregation_labels'							) else None
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return self.model(**__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if self.framework == "pt":
  											__a    : List[str]      					=						[]
  											__a    : Dict      					=						[]
  											__a    : List[str]      					=						None
  											__a    : int      					=						inputs['input_ids'].shape[0]
  
  											__a    : Any      					=						inputs['input_ids'].to(self.device							)
  											__a    : List[Any]      					=						inputs['attention_mask'].to(self.device							)
  											__a    : Union[str, Any]      					=						inputs['token_type_ids'].to(self.device							)
  											__a    : Any      					=						None
  
  											for index in range(__a							):
  																# If sequences have already been processed, the token type IDs will be created according to the previous
  																# answer.
  																if prev_answers is not None:
  																					__a    : str      					=						token_type_ids_example[:, 3]  # shape (seq_len,)
  																					__a    : Optional[Any]      					=						np.zeros_like(prev_labels_example.cpu().numpy()							)  # shape (seq_len,)
  
  																					__a    : List[str]      					=						token_type_ids[index]  # shape (seq_len, 7)
  																					for i in range(model_labels.shape[0]							):
  																										__a    : Tuple      					=						token_type_ids_example[:, 0].tolist()[i]
  																										__a    : int      					=						token_type_ids_example[:, 1].tolist()[i] - 1
  																										__a    : Any      					=						token_type_ids_example[:, 2].tolist()[i] - 1
  
  																										if row_id >= 0 and col_id >= 0 and segment_id == 1:
  																															__a    : int      					=						int(prev_answers[(col_id, row_id)]							)
  
  																					__a    : Dict      					=						torch.from_numpy(__a							).type(torch.long							).to(self.device							)
  
  																__a    : Union[str, Any]      					=						input_ids[index]
  																__a    : Optional[int]      					=						attention_mask[index]  # shape (seq_len,)
  																__a    : Optional[Any]      					=						token_type_ids[index]  # shape (seq_len, 7)
  																__a    : List[Any]      					=						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							)		,       )
  																__a    : List[str]      					=						outputs.logits
  
  																if self.aggregate:
  																					all_aggregations.append(outputs.logits_aggregation							)
  
  																all_logits.append(__a							)
  
  																__a    : List[str]      					=						torch.distributions.Bernoulli(logits=__a							)
  																__a    : int      					=						dist_per_token.probs * attention_mask_example.type(torch.floataa							).to(
  																    dist_per_token.probs.device							)
  
  																__a    : Dict      					=						collections.defaultdict(__a							)
  																for i, p in enumerate(probabilities.squeeze().tolist()							):
  																					__a    : Optional[int]      					=						token_type_ids_example[:, 0].tolist()[i]
  																					__a    : Union[str, Any]      					=						token_type_ids_example[:, 1].tolist()[i] - 1
  																					__a    : List[str]      					=						token_type_ids_example[:, 2].tolist()[i] - 1
  																					if col >= 0 and row >= 0 and segment_id == 1:
  																										coords_to_probs[(col, row)].append(__a							)
  
  																__a    : str      					=						{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}
  
  											__a    : Tuple      					=						torch.cat(tuple(__a							)		,       0							)
  
  											return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__a							)		,       0							))
  						else:
  											__a    : Any      					=						[]
  											__a    : str      					=						[]
  											__a    : int      					=						None
  											__a    : int      					=						inputs['input_ids'].shape[0]
  
  											__a    : Optional[int]      					=						inputs['input_ids']
  											__a    : List[str]      					=						inputs['attention_mask']
  											__a    : str      					=						inputs['token_type_ids'].numpy()
  											__a    : Dict      					=						None
  
  											for index in range(__a							):
  																# If sequences have already been processed, the token type IDs will be created according to the previous
  																# answer.
  																if prev_answers is not None:
  																					__a    : Any      					=						token_type_ids_example[:, 3]  # shape (seq_len,)
  																					__a    : Tuple      					=						np.zeros_like(__a		,       dtype=np.intaa							)  # shape (seq_len,)
  
  																					__a    : str      					=						token_type_ids[index]  # shape (seq_len, 7)
  																					for i in range(model_labels.shape[0]							):
  																										__a    : List[str]      					=						token_type_ids_example[:, 0].tolist()[i]
  																										__a    : str      					=						token_type_ids_example[:, 1].tolist()[i] - 1
  																										__a    : int      					=						token_type_ids_example[:, 2].tolist()[i] - 1
  
  																										if row_id >= 0 and col_id >= 0 and segment_id == 1:
  																															__a    : Optional[Any]      					=						int(prev_answers[(col_id, row_id)]							)
  
  																					__a    : Union[str, Any]      					=						model_labels
  
  																__a    : str      					=						input_ids[index]
  																__a    : Any      					=						attention_mask[index]  # shape (seq_len,)
  																__a    : List[str]      					=						token_type_ids[index]  # shape (seq_len, 7)
  																__a    : Dict      					=						self.model(
  																    input_ids=np.expand_dims(__a		,       axis=0							)		,       attention_mask=np.expand_dims(__a		,       axis=0							)		,       token_type_ids=np.expand_dims(__a		,       axis=0							)		,       )
  																__a    : Dict      					=						outputs.logits
  
  																if self.aggregate:
  																					all_aggregations.append(outputs.logits_aggregation							)
  
  																all_logits.append(__a							)
  
  																__a    : Tuple      					=						tfp.distributions.Bernoulli(logits=__a							)
  																__a    : int      					=						dist_per_token.probs_parameter() * tf.cast(__a		,       tf.floataa							)
  
  																__a    : Optional[Any]      					=						collections.defaultdict(__a							)
  																__a    : List[str]      					=						token_type_ids_example
  																for i, p in enumerate(tf.squeeze(__a							).numpy().tolist()							):
  																					__a    : List[str]      					=						token_type_ids_example[:, 0].tolist()[i]
  																					__a    : Optional[int]      					=						token_type_ids_example[:, 1].tolist()[i] - 1
  																					__a    : Tuple      					=						token_type_ids_example[:, 2].tolist()[i] - 1
  																					if col >= 0 and row >= 0 and segment_id == 1:
  																										coords_to_probs[(col, row)].append(__a							)
  
  																__a    : List[str]      					=						{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}
  
  											__a    : List[Any]      					=						tf.concat(tuple(__a							)		,       0							)
  
  											return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__a							)		,       0							))
  
  
  
  
  	def __call__(     self		,       *__a		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						self._args_parser(*__a		,       **__a							)
  
  						__a    : Dict      					=						super().__call__(__a		,       **__a							)
  						if len(__a							) == 1:
  											return results[0]
  						return results
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=None		,       __a=None		,       __a=None		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						{}
  						if padding is not None:
  											__a    : Dict      					=						padding
  						if truncation is not None:
  											__a    : Dict      					=						truncation
  
  						__a    : List[Any]      					=						{}
  						if sequential is not None:
  											__a    : Dict      					=						sequential
  						return preprocess_params, forward_params, {}
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a=None		,       __a=True		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if truncation is None:
  											if self.type == "tapas":
  																__a    : List[str]      					=						'drop_rows_to_fit'
  											else:
  																__a    : Union[str, Any]      					=						'do_not_truncate'
  
  						__a    : Optional[Any]      					=						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'							)
  						__a    : List[Any]      					=						self.tokenizer(__a		,       __a		,       return_tensors=self.framework		,       truncation=__a		,       padding=__a							)
  						__a    : List[Any]      					=						table
  						return inputs
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a=False							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						model_inputs.pop('table'							)
  
  						if self.type == "tapas":
  											if sequential:
  																__a    : Optional[int]      					=						self.sequential_inference(**__a							)
  											else:
  																__a    : List[Any]      					=						self.batch_inference(**__a							)
  						else:
  											__a    : Optional[Any]      					=						self.model.generate(**__a							)
  						__a    : int      					=						{'model_inputs': model_inputs, 'table': table, 'outputs': outputs}
  						return model_outputs
  
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						model_outputs['model_inputs']
  						__a    : Optional[int]      					=						model_outputs['table']
  						__a    : str      					=						model_outputs['outputs']
  						if self.type == "tapas":
  											if self.aggregate:
  																__a    : int      					=						outputs[:2]
  																__a    : List[str]      					=						self.tokenizer.convert_logits_to_predictions(__a		,       __a		,       __a							)
  																__a    : Tuple      					=						predictions
  																__a    : Tuple      					=						{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__a							)}
  
  																__a    : List[Any]      					=						self.model.config.no_aggregation_label_index
  																__a    : Optional[Any]      					=						{
  																    i: aggregators[i] + ' > ' for i, pred in enumerate(__a							) if pred != no_agg_label_index
  																}
  											else:
  																__a    : Union[str, Any]      					=						outputs[0]
  																__a    : str      					=						self.tokenizer.convert_logits_to_predictions(__a		,       __a							)
  																__a    : Tuple      					=						predictions[0]
  																__a    : Tuple      					=						{}
  																__a    : Dict      					=						{}
  											__a    : Any      					=						[]
  											for index, coordinates in enumerate(__a							):
  																__a    : Optional[int]      					=						[table.iat[coordinate] for coordinate in coordinates]
  																__a    : Optional[int]      					=						aggregators.get(__a		,       ''							)
  																__a    : str      					=						aggregators_prefix.get(__a		,       ''							)
  																__a    : Optional[int]      					=						{
  																    'answer': aggregator_prefix + ', '.join(__a							),
  																    'coordinates': coordinates,
  																    'cells': [table.iat[coordinate] for coordinate in coordinates],
  																}
  																if aggregator:
  																					__a    : int      					=						aggregator
  
  																answers.append(__a							)
  											if len(__a							) == 0:
  																raise PipelineException('Empty answer'							)
  						else:
  											__a    : Tuple      					=						[{'answer': answer} for answer in self.tokenizer.batch_decode(__a		,       skip_special_tokens=__a							)]
  
  						return answers if len(__a							) > 1 else answers[0]
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
  					__a    : Dict      					=						exponent + 2
  					__a    : List[Any]      					=						(1 + 5**0.5) / 2
  					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
  					return int(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	print(f'''{solution() = }''')
  
  
  
  