



from __future__ import annotations

import numpy as np


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

			A__       =    np.zeros((rows, columns)					)
			A__       =    np.zeros((rows, columns)					)
			for i in range(__UpperCamelCase					):
						for j in range(__UpperCamelCase					):
									A__       =    sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase					)					)
									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(__UpperCamelCase ,	__UpperCamelCase					):
									A__       =    sum(lower[i][k] * upper[k][j] for k in range(__UpperCamelCase					)					)
									A__       =    table[i][j] - total
			return lower, upper


if __name__ == "__main__":
							import doctest

							doctest.testmod()









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




