


import math
import sys




def    a__    (		snake_case__					:							Any     ):
  if number != int(__UpperCamelCase     ):
    raise ValueError("""the value of input must be a natural number"""     )
  if number < 0:
    raise ValueError("""the value of input must not be a negative number"""     )
  if number == 0:
    return 1
  _UpperCAmelCase		:							Union[str, Any]							=     [-1] * (number + 1)
  _UpperCAmelCase		:							Dict							=     0
  for i in range(1						,   number + 1     ):
    _UpperCAmelCase		:							Dict							=     sys.maxsize
    _UpperCAmelCase		:							Union[str, Any]							=     int(math.sqrt(__UpperCamelCase     )     )
    for j in range(1						,   root + 1     ):
      _UpperCAmelCase		:							List[Any]							=     1 + answers[i - (j**2)]
      _UpperCAmelCase		:							Union[str, Any]							=     min(__UpperCamelCase						,   __UpperCamelCase     )
    _UpperCAmelCase		:							int							=     answer
  return answers[number]


if __name__ == "__main__":
 import doctest

 doctest.testmod()










# 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


