"""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__(      __A			):





						def __call__(							self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase  )       ->					str:
													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:
																											A__						  =				[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:
																																		A__						  =				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:
																				A__						  =				[{'''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__						  =				pd.DataFrame(tqa_pipeline_input['table']  )

													return tqa_pipeline_inputs






@add_end_docstrings(__A			)
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    int    				=		'''table,query'''





						def __init__(							self		,__UpperCAmelCase=TableQuestionAnsweringArgumentHandler()		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					List[str]:
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase  )
													A__						  =				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__						  =				bool(getattr(self.model.config		,'aggregation_labels'		,__UpperCAmelCase  )  ) and bool(
													    getattr(self.model.config		,'num_aggregation_labels'		,__UpperCAmelCase  )  )
													A__						  =				'''tapas''' if hasattr(self.model.config		,'aggregation_labels'  ) else None





						def 					snake_case__						(							self		,**__UpperCAmelCase  )       ->					List[str]:
													return self.model(**__UpperCAmelCase  )





						def 					snake_case__						(							self		,**__UpperCAmelCase  )       ->					Optional[Any]:







													if self.framework == "pt":
																				A__						  =				[]
																				A__						  =				[]
																				A__						  =				None
																				A__						  =				inputs['''input_ids'''].shape[0]

																				A__						  =				inputs['''input_ids'''].to(self.device  )
																				A__						  =				inputs['''attention_mask'''].to(self.device  )
																				A__						  =				inputs['''token_type_ids'''].to(self.device  )
																				A__						  =				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:
																																		A__						  =				token_type_ids_example[:, 3]  # shape (seq_len,)
																																		A__						  =				np.zeros_like(prev_labels_example.cpu().numpy()  )  # shape (seq_len,)

																																		A__						  =				token_type_ids[index]  # shape (seq_len, 7)
																																		for i in range(model_labels.shape[0]  ):
																																									A__						  =				token_type_ids_example[:, 0].tolist()[i]
																																									A__						  =				token_type_ids_example[:, 1].tolist()[i] - 1
																																									A__						  =				token_type_ids_example[:, 2].tolist()[i] - 1

																																									if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																																A__						  =				int(prev_answers[(col_id, row_id)]  )

																																		A__						  =				torch.from_numpy(__UpperCAmelCase  ).type(torch.long  ).to(self.device  )

																											A__						  =				input_ids[index]
																											A__						  =				attention_mask[index]  # shape (seq_len,)
																											A__						  =				token_type_ids[index]  # shape (seq_len, 7)
																											A__						  =				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__						  =				outputs.logits

																											if self.aggregate:
																																		all_aggregations.append(outputs.logits_aggregation  )

																											all_logits.append(__UpperCAmelCase  )

																											A__						  =				torch.distributions.Bernoulli(logits=__UpperCAmelCase  )
																											A__						  =				dist_per_token.probs * attention_mask_example.type(torch.floataa  ).to(
																											    dist_per_token.probs.device  )

																											A__						  =				collections.defaultdict(__UpperCAmelCase  )
																											for i, p in enumerate(probabilities.squeeze().tolist()  ):
																																		A__						  =				token_type_ids_example[:, 0].tolist()[i]
																																		A__						  =				token_type_ids_example[:, 1].tolist()[i] - 1
																																		A__						  =				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  )

																											A__						  =				{key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

																				A__						  =				torch.cat(tuple(__UpperCAmelCase  )		,0  )

																				return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__UpperCAmelCase  )		,0  ))
													else:
																				A__						  =				[]
																				A__						  =				[]
																				A__						  =				None
																				A__						  =				inputs['''input_ids'''].shape[0]

																				A__						  =				inputs['''input_ids''']
																				A__						  =				inputs['''attention_mask''']
																				A__						  =				inputs['''token_type_ids'''].numpy()
																				A__						  =				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:
																																		A__						  =				token_type_ids_example[:, 3]  # shape (seq_len,)
																																		A__						  =				np.zeros_like(__UpperCAmelCase		,dtype=np.intaa  )  # shape (seq_len,)

																																		A__						  =				token_type_ids[index]  # shape (seq_len, 7)
																																		for i in range(model_labels.shape[0]  ):
																																									A__						  =				token_type_ids_example[:, 0].tolist()[i]
																																									A__						  =				token_type_ids_example[:, 1].tolist()[i] - 1
																																									A__						  =				token_type_ids_example[:, 2].tolist()[i] - 1

																																									if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																																A__						  =				int(prev_answers[(col_id, row_id)]  )

																																		A__						  =				model_labels

																											A__						  =				input_ids[index]
																											A__						  =				attention_mask[index]  # shape (seq_len,)
																											A__						  =				token_type_ids[index]  # shape (seq_len, 7)
																											A__						  =				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  )		,)
																											A__						  =				outputs.logits

																											if self.aggregate:
																																		all_aggregations.append(outputs.logits_aggregation  )

																											all_logits.append(__UpperCAmelCase  )

																											A__						  =				tfp.distributions.Bernoulli(logits=__UpperCAmelCase  )
																											A__						  =				dist_per_token.probs_parameter() * tf.cast(__UpperCAmelCase		,tf.floataa  )

																											A__						  =				collections.defaultdict(__UpperCAmelCase  )
																											A__						  =				token_type_ids_example
																											for i, p in enumerate(tf.squeeze(__UpperCAmelCase  ).numpy().tolist()  ):
																																		A__						  =				token_type_ids_example[:, 0].tolist()[i]
																																		A__						  =				token_type_ids_example[:, 1].tolist()[i] - 1
																																		A__						  =				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  )

																											A__						  =				{key: np.array(coords_to_probs[key]  ).mean() > 0.5 for key in coords_to_probs}

																				A__						  =				tf.concat(tuple(__UpperCAmelCase  )		,0  )

																				return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__UpperCAmelCase  )		,0  ))





						def __call__(							self		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					Tuple:
													A__						  =				self._args_parser(*__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				super().__call__(__UpperCAmelCase		,**__UpperCAmelCase  )
													if len(__UpperCAmelCase  ) == 1:
																				return results[0]
													return results





						def 					snake_case__						(							self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase  )       ->					Dict:
													A__						  =				{}
													if padding is not None:
																				A__						  =				padding
													if truncation is not None:
																				A__						  =				truncation

													A__						  =				{}
													if sequential is not None:
																				A__						  =				sequential
													return preprocess_params, forward_params, {}





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=None  )       ->					Tuple:







													if truncation is None:
																				if self.type == "tapas":
																											A__						  =				'''drop_rows_to_fit'''
																				else:
																											A__						  =				'''do_not_truncate'''

													A__						  =				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__						  =				self.tokenizer(__UpperCAmelCase		,__UpperCAmelCase		,return_tensors=self.framework		,truncation=__UpperCAmelCase		,padding=__UpperCAmelCase  )
													A__						  =				table
													return inputs





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=False  )       ->					int:
													A__						  =				model_inputs.pop('table'  )

													if self.type == "tapas":
																				if sequential:
																											A__						  =				self.sequential_inference(**__UpperCAmelCase  )
																				else:
																											A__						  =				self.batch_inference(**__UpperCAmelCase  )
													else:
																				A__						  =				self.model.generate(**__UpperCAmelCase  )
													A__						  =				{'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
													return model_outputs





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				model_outputs['''model_inputs''']
													A__						  =				model_outputs['''table''']
													A__						  =				model_outputs['''outputs''']
													if self.type == "tapas":
																				if self.aggregate:
																											A__						  =				outputs[:2]
																											A__						  =				self.tokenizer.convert_logits_to_predictions(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )
																											A__						  =				predictions
																											A__						  =				{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__UpperCAmelCase  )}

																											A__						  =				self.model.config.no_aggregation_label_index
																											A__						  =				{
																											    i: aggregators[i] + ''' > ''' for i, pred in enumerate(__UpperCAmelCase  ) if pred != no_agg_label_index
																											}
																				else:
																											A__						  =				outputs[0]
																											A__						  =				self.tokenizer.convert_logits_to_predictions(__UpperCAmelCase		,__UpperCAmelCase  )
																											A__						  =				predictions[0]
																											A__						  =				{}
																											A__						  =				{}
																				A__						  =				[]
																				for index, coordinates in enumerate(__UpperCAmelCase  ):
																											A__						  =				[table.iat[coordinate] for coordinate in coordinates]
																											A__						  =				aggregators.get(__UpperCAmelCase		,''  )
																											A__						  =				aggregators_prefix.get(__UpperCAmelCase		,''  )
																											A__						  =				{
																											    '''answer''': aggregator_prefix + ''', '''.join(__UpperCAmelCase  ),
																											    '''coordinates''': coordinates,
																											    '''cells''': [table.iat[coordinate] for coordinate in coordinates],
																											}
																											if aggregator:
																																		A__						  =				aggregator

																											answers.append(__UpperCAmelCase  )
																				if len(__UpperCAmelCase  ) == 0:
																											raise PipelineException('Empty answer'  )
													else:
																				A__						  =				[{'''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"""





from itertools import combinations, product



def      __lowercase       (     a			:					int							,		a			:					int							,		a			:					int							,		a			:					int			)       ->       bool:
      if xa == ya == 0 or xa == ya == 0:
            return False
      __snake_case   :     Any	      =xa * xa + ya * ya
      __snake_case   :     Tuple	      =xa * xa + ya * ya
      __snake_case   :     List[Any]	      =(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
      return (
          a_square + b_square == c_square
          or a_square + c_square == b_square
          or b_square + c_square == a_square
      )



def      __lowercase       (     a			:					int = 50			)       ->       int:
      return sum(
          1
          for pta, pta in combinations(product(range(limit + 1			)							,		repeat=2			)							,		2			)
          if is_right(*a							,		*a			)			)


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


