
from __future__ import annotations

import numpy as np


def     __lowerCAmelCase			(			a__  )       ->  tuple[np.ndarray, np.ndarray]:
				__a							,     __a										=    np.shape(a__  )
				if rows != columns:
								__a										=    (
								    '''\'table\' has to be of square shaped array but got a '''
								    F"""{rows}x{columns} array:\n{table}"""
								)
								raise ValueError(a__  )

				__a										=    np.zeros((rows, columns)  )
				__a										=    np.zeros((rows, columns)  )
				for i in range(a__  ):
								for j in range(a__  ):
												__a										=    sum(lower[i][k] * upper[k][j] for k in range(a__  )  )
												if upper[j][j] == 0:
																raise ArithmeticError('''No LU decomposition exists'''  )
												__a										=    (table[i][j] - total) / upper[j][j]
								__a										=    1
								for j in range(a__						,    a__  ):
												__a										=    sum(lower[i][k] * upper[k][j] for k in range(a__  )  )
												__a										=    table[i][j] - total
				return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()

from typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)