


"""simple docstring"""
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__SCREAMING_SNAKE_CASE							:      Optional[int]						=      'facebook/wmt19-en-de'
__SCREAMING_SNAKE_CASE							:      Any						=      FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__SCREAMING_SNAKE_CASE							:      str						=      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							:      List[Any]						=      FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__SCREAMING_SNAKE_CASE							:      str						=      tokenizer(['Making tiny model'], return_tensors='pt')
__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      tiny_model(**batch)

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

# Save
__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      '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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 0			)      ->			list:
							snake_case_          =						length or len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						False
							for i in range(length - 1			):
														if list_data[i] > list_data[i + 1]:
																					snake_case_    ,  snake_case_          =						list_data[i + 1], list_data[i]
																					snake_case_          =						True

							return list_data if not swapped else bubble_sort(_SCREAMING_SNAKE_CASE			,	length - 1			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



