




'''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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
      _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained("google/mt5-small"   )

      _SCREAMING_SNAKE_CASE	=				tokenizer("Hello there"  ,				return_tensors="np"   ).input_ids
      _SCREAMING_SNAKE_CASE	=				tokenizer("Hi I am"  ,				return_tensors="np"   ).input_ids

      _SCREAMING_SNAKE_CASE	=				shift_tokens_right(__lowerCamelCase  ,				model.config.pad_token_id  ,				model.config.decoder_start_token_id   )

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

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

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







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

