

from __future__ import annotations

import numpy as np

def 						UpperCamelCase (lowercase_:			np.ndarray )					->				tuple[np.ndarray, np.ndarray]:
	A__       ,       A__							:   Union[str, Any]					       =		np.shape(lowercase_ )
	if rows != columns:
		A__							:   str					       =		(
		    """'table' has to be of square shaped array but got a """
		    f"""{rows}x{columns} array:\n{table}"""
		)
		raise ValueError(lowercase_ )

	A__							:   List[Any]					       =		np.zeros((rows, columns) )
	A__							:   Union[str, Any]					       =		np.zeros((rows, columns) )
	for i in range(lowercase_ ):
		for j in range(lowercase_ ):
			A__							:   List[str]					       =		sum(lower[i][k] * upper[k][j] for k in range(lowercase_ ) )
			if upper[j][j] == 0:
				raise ArithmeticError("""No LU decomposition exists""" )
			A__							:   str					       =		(table[i][j] - total) / upper[j][j]
		A__							:   int					       =		1
		for j in range(lowercase_   ,				lowercase_ ):
			A__							:   str					       =		sum(lower[i][k] * upper[k][j] for k in range(lowercase_ ) )
			A__							:   Union[str, Any]					       =		table[i][j] - total
	return lower, upper


if __name__ == "__main__":
	import doctest

	doctest.testmod()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



