
import os

snake_case :		List[Any]							      =  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     0

	__magic_name__		:		Union[str, Any]	   =     0
	while index < len(_snake_case   ) - 1:
		__magic_name__		:		Any	   =     SYMBOLS[numerals[index]]
		__magic_name__		:		str	   =     SYMBOLS[numerals[index + 1]]
		if current_value < next_value:
			total_value -= current_value
		else:
			total_value += current_value
		index += 1
	total_value += SYMBOLS[numerals[index]]

	return total_value






def   lowerCAmelCase_						(					_snake_case      :  int   )				->		str:







	'''simple docstring'''


	__magic_name__		:		str	   =     ""

	__magic_name__		:		List[Any]	   =     num // 1000
	numerals += m_count * "M"
	num %= 1000

	__magic_name__		:		Optional[int]	   =     num // 100
	if c_count == 9:
		numerals += "CM"
		c_count -= 9
	elif c_count == 4:
		numerals += "CD"
		c_count -= 4
	if c_count >= 5:
		numerals += "D"
		c_count -= 5
	numerals += c_count * "C"
	num %= 100

	__magic_name__		:		Optional[int]	   =     num // 10
	if x_count == 9:
		numerals += "XC"
		x_count -= 9
	elif x_count == 4:
		numerals += "XL"
		x_count -= 4
	if x_count >= 5:
		numerals += "L"
		x_count -= 5
	numerals += x_count * "X"
	num %= 10

	if num == 9:
		numerals += "IX"
		num -= 9
	elif num == 4:
		numerals += "IV"
		num -= 4
	if num >= 5:
		numerals += "V"
		num -= 5
	numerals += num * "I"

	return numerals






def   lowerCAmelCase_						(					_snake_case      :  str = "/p089_roman.txt"   )				->		int:







	'''simple docstring'''


	__magic_name__		:		int	   =     0

	with open(os.path.dirname(_snake_case   ) + roman_numerals_filename   ) as filea:
		__magic_name__		:		List[Any]	   =     filea.readlines()

	for line in lines:
		__magic_name__		:		Tuple	   =     line.strip()
		__magic_name__		:		List[Any]	   =     parse_roman_numerals(_snake_case   )
		__magic_name__		:		List[str]	   =     generate_roman_numerals(_snake_case   )
		savings += len(_snake_case   ) - len(_snake_case   )

	return savings


if __name__ == "__main__":
				print(F"{solution() = }")


# 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 :		List[str]							      =  "facebook/wmt19-en-de"
snake_case :		Dict							      =  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 :		int							      =  FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

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

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

# Save
snake_case :		Dict							      =  "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
