






import math
import sys







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



						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
						lowerCAmelCase__    						=						[-1] * (number + 1)
						lowerCAmelCase__    						=						0
						for i in range(1    ,       number + 1		):
												lowerCAmelCase__    						=						sys.maxsize
												lowerCAmelCase__    						=						int(math.sqrt(UpperCamelCase_		)		)
												for j in range(1    ,       root + 1		):
																		lowerCAmelCase__    						=						1 + answers[i - (j**2)]
																		lowerCAmelCase__    						=						min(UpperCamelCase_    ,       UpperCamelCase_		)
												lowerCAmelCase__    						=						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

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[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,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

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

# Save
__snake_case    :    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





