


'''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]







'''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() = }''')



