


import os

a : Optional[int]		=				{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   0

			__lowercase 					=   0
			while index < len(_UpperCamelCase  ) - 1:
						__lowercase 					=   SYMBOLS[numerals[index]]
						__lowercase 					=   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 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   ''''''

			__lowercase 					=   num // 10_00
			numerals += m_count * "M"
			num %= 10_00

			__lowercase 					=   num // 1_00
			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 %= 1_00

			__lowercase 					=   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 		lowercase_  (     _UpperCamelCase = "/p089_roman.txt"  ):


			'''simple docstring'''







			__lowercase 					=   0

			with open(os.path.dirname(_UpperCamelCase  ) + roman_numerals_filename  ) as filea:
						__lowercase 					=   filea.readlines()

			for line in lines:
						__lowercase 					=   line.strip()
						__lowercase 					=   parse_roman_numerals(_UpperCamelCase  )
						__lowercase 					=   generate_roman_numerals(_UpperCamelCase  )
						savings += len(_UpperCamelCase  ) - len(_UpperCamelCase  )

			return savings


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






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Optional[int]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


