


'''simple docstring'''

class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      name
        UpperCamelCase       						=      value
        UpperCamelCase       						=      weight

  def __repr__(   self   )						->   str:



        """simple docstring"""






        return F"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        return self.value

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        return self.name

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        return self.weight


  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        return self.value / self.weight







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Optional[Any]:
      UpperCamelCase       						=      []
      for i in range(len(_lowerCamelCase					)					):
            menu.append(Things(name[i]	,		value[i]	,		weight[i]					)					)
      return menu







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      sorted(_lowerCamelCase	,		key=_lowerCamelCase	,		reverse=_lowerCamelCase					)
      UpperCamelCase       						=      []
      UpperCamelCase						,UpperCamelCase       						=      0.0, 0.0
      for i in range(len(_lowerCamelCase					)					):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]					)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)







def 					lowercase__				(   )->				str:
      pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de
