






from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

snake_case     					=		"""facebook/wmt19-en-de"""
snake_case     					=		FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
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,
    )
)

snake_case     					=		FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

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

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

# Save
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














'''simple docstring'''



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   if not all(x.isalpha() for x in string     ):
      raise ValueError('String must only contain alphabetic characters.'     )

   __a					  =     sorted(string.lower()     )
   return len(SCREAMING_SNAKE_CASE__     ) == len(set(SCREAMING_SNAKE_CASE__     )     )


if __name__ == "__main__":
 __UpperCamelCase   :							int         =   input("""Enter a string """).strip()

 __UpperCamelCase   :							Tuple         =   is_isogram(input_str)
 print(f"""{input_str} is {"an" if isogram else "not an"} isogram.""")