




import math



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







 return math.pow(lowerCAmelCase_						,							2	) - a



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 return 2 * x



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						2.0

 while start <= a:
  UpperCAmelCase__							 =						math.pow(lowerCAmelCase_						,							2	)

 return start



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   int = 9999						,							_lowerCAmelCase							:   Optional[Any] = 0.00_000_000_000_001	):




 """simple docstring"""







 if a < 0:
  raise ValueError("math domain error"	)

 UpperCAmelCase__							 =						get_initial_point(lowerCAmelCase_	)

 for _ in range(lowerCAmelCase_	):
  UpperCAmelCase__							 =						value
  UpperCAmelCase__							 =						value - fx(lowerCAmelCase_						,							lowerCAmelCase_	) / fx_derivative(lowerCAmelCase_	)
  if abs(prev_value - value	) < tolerance:
   return value

 return value


if __name__ == "__main__":
  from doctest import testmod

  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						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   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						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

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

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

# Save
_lowerCAmelCase						:   Optional[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





