
"""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

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

UpperCAmelCase__   :					int												=				FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

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

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

# Save
UpperCAmelCase__   :					Optional[int]												=				'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 torch

def lowercase_						(     ):
							if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.cuda.device_count()
							else:
														SCREAMING_SNAKE_CASE__		: int     =     0
							print(f'''Successfully ran on {num_gpus} GPUs'''		)


if __name__ == "__main__":
					main()




