






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					)							-> Dict:
          UpperCamelCase			:Dict      				=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''					)
          UpperCamelCase			:Optional[int]      				=  AutoTokenizer.from_pretrained('''google/mt5-small'''					)

          UpperCamelCase			:Any      				=  tokenizer('''Hello there'''       ,							return_tensors='''np'''					).input_ids
          UpperCamelCase			:Union[str, Any]      				=  tokenizer('''Hi I am'''       ,							return_tensors='''np'''					).input_ids

          UpperCamelCase			:List[str]      				=  shift_tokens_right(SCREAMING_SNAKE_CASE_       ,							model.config.pad_token_id       ,							model.config.decoder_start_token_id					)

          UpperCamelCase			:Any      				=  model(SCREAMING_SNAKE_CASE_       ,							decoder_input_ids=SCREAMING_SNAKE_CASE_					).logits
          UpperCamelCase			:Any      				=  optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE_       ,							onehot(SCREAMING_SNAKE_CASE_       ,							logits.shape[-1]					)					).mean()

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

          UpperCamelCase			:Optional[int]      				=  -84.9127
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1e-4					)












def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    str   		=	set({"(", "[", "{"} )
   a__						:    List[str]   		=	set({")", "]", "}"} )
   a__						:    int   		=	{"{": "}", "[": "]", "(": ")"}

   for i in range(len(lowerCamelCase ) ):
      if s[i] in open_brackets:
         stack.append(s[i] )

      elif s[i] in closed_brackets and (
          len(lowerCamelCase ) == 0 or (len(lowerCamelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
      ):
         return False

   return len(lowerCamelCase ) == 0






def    _A	(   ):
   a__						:    Optional[int]   		=	input("Enter sequence of brackets: " )
   if is_balanced(lowerCamelCase ):
      print(lowerCamelCase				,			"is balanced" )
   else:
      print(lowerCamelCase				,			"is not balanced" )


if __name__ == "__main__":
  main()

