
"""simple docstring"""




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 			__magic_name__       (       unittest.TestCase ):
       @slow
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			List[Any]       =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''       )
         _lowercase:			Optional[int]       =     AutoTokenizer.from_pretrained('''google/mt5-small'''       )

         _lowercase:			List[Any]       =     tokenizer('''Hello there''' ,				return_tensors='''np'''       ).input_ids
         _lowercase:			Optional[int]       =     tokenizer('''Hi I am''' ,				return_tensors='''np'''       ).input_ids

         _lowercase:			List[Any]       =     shift_tokens_right(A_ ,				model.config.pad_token_id ,				model.config.decoder_start_token_id       )

         _lowercase:			Tuple       =     model(A_ ,				decoder_input_ids=A_       ).logits
         _lowercase:			List[Any]       =     optax.softmax_cross_entropy(A_ ,				onehot(A_ ,				logits.shape[-1]       )       ).mean()

         _lowercase:			Optional[int]       =     -(labels.shape[-1] * loss.item())

         _lowercase:			List[Any]       =     -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )


"""simple docstring"""




from __future__ import annotations

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase ,				_lowercase:			List[str]       =     np.shape(_UpperCamelCase						)
  if rows != columns:
    _lowercase:			int       =     (
        '''\'table\' has to be of square shaped array but got a '''
        f'''{rows}x{columns} array:\n{table}'''
    )
    raise ValueError(_UpperCamelCase						)

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()
