

"""simple docstring"""





from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

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

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

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

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

# Save
_a							=							'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"""





import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      **UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						AutoConfig.from_pretrained(UpperCamelCase_,      **UpperCamelCase_)
   __lowercase								=						AutoModelForSeqaSeqLM.from_config(UpperCamelCase_)
   model.save_pretrained(UpperCamelCase_)
   AutoTokenizer.from_pretrained(UpperCamelCase_).save_pretrained(UpperCamelCase_)
   return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)




