





"""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

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

lowerCAmelCase_			=							FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

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

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

# Save
lowerCAmelCase_			=							'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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'							)

       _UpperCAmelCase     =     str(bin(SCREAMING_SNAKE_CASE							)							)[2:]  # remove the leading "0b"
       _UpperCAmelCase     =     str(bin(SCREAMING_SNAKE_CASE							)							)[2:]  # remove the leading "0b"

       _UpperCAmelCase     =     max(len(SCREAMING_SNAKE_CASE							),len(SCREAMING_SNAKE_CASE							)							)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'							)							)
           for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE							),b_binary.zfill(SCREAMING_SNAKE_CASE							)							)							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






