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 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			int    	=       FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small' )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       AutoTokenizer.from_pretrained('google/mt5-small' )

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

         SCREAMING_SNAKE_CASE					:			List[str]    	=       shift_tokens_right(lowerCAmelCase_    ,							model.config.pad_token_id    ,							model.config.decoder_start_token_id )

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

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

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )




'''simple docstring'''





def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number < 0 or shift_amount < 0:
          raise ValueError('both inputs must be positive integers'		)

     SCREAMING_SNAKE_CASE					:			str    	=       str(bin(a_		)		)
     binary_number += "0" * shift_amount
     return binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number < 0 or shift_amount < 0:
          raise ValueError('both inputs must be positive integers'		)

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       str(bin(a_		)		)[2:]
     if shift_amount >= len(a_		):
          return "0b0"
     SCREAMING_SNAKE_CASE					:			Dict    	=       binary_number[: len(a_		) - shift_amount]
     return "0b" + shifted_binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number >= 0:  # Get binary representation of positive number
          SCREAMING_SNAKE_CASE					:			Tuple    	=       '0' + str(bin(a_		)		).strip('-'		)[2:]
     else:  # Get binary (2's complement) representation of negative number
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(bin(a_		)[3:]		)  # Find 2's complement of number
          SCREAMING_SNAKE_CASE					:			Any    	=       bin(abs(a_		) - (1 << binary_number_length)		)[3:]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (
              '1' + '0' * (binary_number_length - len(a_		)) + binary_number
          )

     if shift_amount >= len(a_		):
          return "0b" + binary_number[0] * len(a_		)
     return (
         "0b"
         + binary_number[0] * shift_amount
         + binary_number[: len(a_		) - shift_amount]
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



