




"""simple docstring"""







def      __snake_case (				_lowercase    ):


    """simple docstring"""







    return sum(i for i in range(1			,number // 2 + 1    ) if number % i == 0    ) == number


if __name__ == "__main__":
  print('Program to check whether a number is a Perfect number or not...')
  SCREAMING_SNAKE_CASE_	     =       int(input('Enter number: ').strip())
  print(f'{number} is {"" if perfect(number) else "not "}a Perfect Number.')







'''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




