
'''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 ,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 ,snake_case     :					int     =	outputs[:2]
								snake_case     :					Optional[int]     =	self.tokenizer.convert_logits_to_predictions(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
								snake_case ,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]




'''simple docstring'''
def       _UpperCamelCase	(					)     ->       int:


		'''simple docstring'''


		for a in range(300 ):
				for b in range(a + 1   ,					400 ):
						for c in range(b + 1   ,					500 ):
								if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
										return a * b * c

		return -1




def       _UpperCamelCase	(					)     ->       int:


		'''simple docstring'''


		for a in range(300 ):
				for b in range(400 ):
						snake_case     :					Dict     =	1000 - a - b
						if a < b < c and (a**2) + (b**2) == (c**2):
								return a * b * c

		return -1




def       _UpperCamelCase	(					)     ->       None:


		'''simple docstring'''


		import timeit

		print(
		    timeit.timeit('''solution()'''   ,					setup='''from __main__ import solution'''   ,					number=1000 ) )
		print(
		    timeit.timeit(
		        '''solution_fast()'''   ,					setup='''from __main__ import solution_fast'''   ,					number=1000 ) )


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


