import os

SCREAMING_SNAKE_CASE__		:       Any       						=       {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}







def      _a					(   lowercase__	:      str			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		0

     SCREAMING_SNAKE_CASE__					:					Dict      =		0
     while index < len(lowercase__			) - 1:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		SYMBOLS[numerals[index]]
          SCREAMING_SNAKE_CASE__					:					List[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      _a					(   lowercase__	:      int			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[Any]      =		''

     SCREAMING_SNAKE_CASE__					:					Tuple      =		num // 10_00
     numerals += m_count * "M"
     num %= 10_00

     SCREAMING_SNAKE_CASE__					:					Tuple      =		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

     SCREAMING_SNAKE_CASE__					:					Dict      =		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      _a					(   lowercase__	:      str = "/p089_roman.txt"			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Tuple      =		0

     with open(os.path.dirname(lowercase__			) + roman_numerals_filename			) as filea:
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		filea.readlines()

     for line in lines:
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		line.strip()
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		parse_roman_numerals(lowercase__			)
          SCREAMING_SNAKE_CASE__					:					int      =		generate_roman_numerals(lowercase__			)
          savings += len(lowercase__			) - len(lowercase__			)

     return savings


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









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_			      =		{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaPreLayerNormForCausalLM",
          "RobertaPreLayerNormForMaskedLM",
          "RobertaPreLayerNormForMultipleChoice",
          "RobertaPreLayerNormForQuestionAnswering",
          "RobertaPreLayerNormForSequenceClassification",
          "RobertaPreLayerNormForTokenClassification",
          "RobertaPreLayerNormModel",
          "RobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaPreLayerNormForCausalLM",
          "TFRobertaPreLayerNormForMaskedLM",
          "TFRobertaPreLayerNormForMultipleChoice",
          "TFRobertaPreLayerNormForQuestionAnswering",
          "TFRobertaPreLayerNormForSequenceClassification",
          "TFRobertaPreLayerNormForTokenClassification",
          "TFRobertaPreLayerNormMainLayer",
          "TFRobertaPreLayerNormModel",
          "TFRobertaPreLayerNormPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			      =		[
          "FlaxRobertaPreLayerNormForCausalLM",
          "FlaxRobertaPreLayerNormForMaskedLM",
          "FlaxRobertaPreLayerNormForMultipleChoice",
          "FlaxRobertaPreLayerNormForQuestionAnswering",
          "FlaxRobertaPreLayerNormForSequenceClassification",
          "FlaxRobertaPreLayerNormForTokenClassification",
          "FlaxRobertaPreLayerNormModel",
          "FlaxRobertaPreLayerNormPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RobertaPreLayerNormConfig,
          RobertaPreLayerNormOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta_prelayernorm import (
                ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaPreLayerNormForCausalLM,
                RobertaPreLayerNormForMaskedLM,
                RobertaPreLayerNormForMultipleChoice,
                RobertaPreLayerNormForQuestionAnswering,
                RobertaPreLayerNormForSequenceClassification,
                RobertaPreLayerNormForTokenClassification,
                RobertaPreLayerNormModel,
                RobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta_prelayernorm import (
                TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaPreLayerNormForCausalLM,
                TFRobertaPreLayerNormForMaskedLM,
                TFRobertaPreLayerNormForMultipleChoice,
                TFRobertaPreLayerNormForQuestionAnswering,
                TFRobertaPreLayerNormForSequenceClassification,
                TFRobertaPreLayerNormForTokenClassification,
                TFRobertaPreLayerNormMainLayer,
                TFRobertaPreLayerNormModel,
                TFRobertaPreLayerNormPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta_prelayernorm import (
                FlaxRobertaPreLayerNormForCausalLM,
                FlaxRobertaPreLayerNormForMaskedLM,
                FlaxRobertaPreLayerNormForMultipleChoice,
                FlaxRobertaPreLayerNormForQuestionAnswering,
                FlaxRobertaPreLayerNormForSequenceClassification,
                FlaxRobertaPreLayerNormForTokenClassification,
                FlaxRobertaPreLayerNormModel,
                FlaxRobertaPreLayerNormPreTrainedModel,
            )

else:
      import sys

      A_			      =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
