


"""simple docstring"""






import os

UpperCAmelCase    :     List[Any]   						=		{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def      lowerCamelCase       (     _UpperCamelCase		:      str							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					0

							__UpperCAmelCase   :						Union[str, Any]      							=					0
							while index < len(_snake_case							) - 1:
														__UpperCAmelCase   :						Any      							=					SYMBOLS[numerals[index]]
														__UpperCAmelCase   :						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       (     _UpperCamelCase		:      int							)		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						str      							=					""

							__UpperCAmelCase   :						List[Any]      							=					num // 1_0_0_0
							numerals += m_count * "M"
							num %= 1_0_0_0

							__UpperCAmelCase   :						Optional[int]      							=					num // 1_0_0
							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_0_0

							__UpperCAmelCase   :						Optional[int]      							=					num // 1_0
							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 %= 1_0

							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       (     _UpperCamelCase		:      str = "/p089_roman.txt"							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					0

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

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

							return savings


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


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'convbert'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=1						,   _a=0						,   _a=2						,   _a=768						,   _a=2						,   _a=9						,   _a=1						,   _a=None						,   **_a						,   ):
				super().__init__(
				    pad_token_id=_a						,   bos_token_id=_a						,   eos_token_id=_a						,   **_a						,   )

				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     hidden_size
				__magic_name__		:		Union[str, Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		str	   =     intermediate_size
				__magic_name__		:		Any	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     max_position_embeddings
				__magic_name__		:		str	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     initializer_range
				__magic_name__		:		Tuple	   =     layer_norm_eps
				__magic_name__		:		List[Any]	   =     embedding_size
				__magic_name__		:		List[Any]	   =     head_ratio
				__magic_name__		:		str	   =     conv_kernel_size
				__magic_name__		:		Dict	   =     num_groups
				__magic_name__		:		str	   =     classifier_dropout







class       _snake_case					(     snake_case				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		Dict	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Dict	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)
