


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 							a						(unittest.TestCase ):




							"""simple docstring"""


							@slow
							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[Any]:
														__snake_case						:     List[str]									=  FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
														__snake_case						:     Tuple									=  AutoTokenizer.from_pretrained("google/mt5-small"							)

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

														__snake_case						:     Union[str, Any]									=  shift_tokens_right(lowerCamelCase       ,      model.config.pad_token_id       ,      model.config.decoder_start_token_id							)

														__snake_case						:     List[str]									=  model(lowerCamelCase       ,      decoder_input_ids=lowerCamelCase							).logits
														__snake_case						:     List[str]									=  optax.softmax_cross_entropy(lowerCamelCase       ,      onehot(lowerCamelCase       ,      logits.shape[-1]							)							).mean()

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

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








'''simple docstring'''

import os
from math import logaa

def     __magic_name__							(				__UpperCAmelCase = "base_exp.txt"   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0
   __SCREAMING_SNAKE_CASE   				=							0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__UpperCAmelCase   )				,						__UpperCAmelCase   )   )   ):
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							list(map(__UpperCAmelCase				,						line.split(""","""   )   )   )
      if x * logaa(__UpperCAmelCase   ) > largest:
         __SCREAMING_SNAKE_CASE   				=							x * logaa(__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							i + 1
   return result


if __name__ == "__main__":
    print(solution())





