
from typing import Mapping

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


__A				=logging.get_logger(__name__)

__A				={
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'umt5'
	lowerCAmelCase__         = ['past_key_values']



	def __init__(    self						,  lowercase=250112						,  lowercase=512						,  lowercase=64						,  lowercase=1024						,  lowercase=8						,  lowercase=None						,  lowercase=6						,  lowercase=32						,  lowercase=128						,  lowercase=0.1						,  lowercase=1e-6						,  lowercase=1.0						,  lowercase="gated-gelu"						,  lowercase=True						,  lowercase=True						,  lowercase="T5Tokenizer"						,  lowercase=True						,  lowercase=0						,  lowercase=1						,  lowercase=0						,  **lowercase						,  )				->       List[Any]:
					super().__init__(
					    is_encoder_decoder=lowercase						,  tokenizer_class=lowercase						,  tie_word_embeddings=lowercase						,  pad_token_id=lowercase						,  eos_token_id=lowercase						,  decoder_start_token_id=lowercase						,  **lowercase						,  )
					lowerCamelCase_						=			vocab_size
					lowerCamelCase_						=			d_model
					lowerCamelCase_						=			d_kv
					lowerCamelCase_						=			d_ff
					lowerCamelCase_						=			num_layers
					lowerCamelCase_						=			(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					lowerCamelCase_						=			num_heads
					lowerCamelCase_						=			relative_attention_num_buckets
					lowerCamelCase_						=			relative_attention_max_distance
					lowerCamelCase_						=			dropout_rate
					lowerCamelCase_						=			layer_norm_epsilon
					lowerCamelCase_						=			initializer_factor
					lowerCamelCase_						=			feed_forward_proj
					lowerCamelCase_						=			use_cache

					lowerCamelCase_						=			self.feed_forward_proj.split("-" )
					lowerCamelCase_						=			act_info[-1]
					lowerCamelCase_						=			act_info[0] == "gated"

					if len(lowercase ) > 1 and act_info[0] != "gated" or len(lowercase ) > 2:
									raise ValueError(
									    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
									    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
									    "'gated-gelu' or 'relu'" )

					if feed_forward_proj == "gated-gelu":
									lowerCamelCase_						=			"gelu_new"



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					return self.d_model



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self.num_heads



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					return self.num_layers




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def     SCREAMING_SNAKE_CASE_(    self )				->       Mapping[str, Mapping[int, str]]:
					lowerCamelCase_						=			{
					    "input_ids": {0: "batch", 1: "encoder_sequence"},
					    "attention_mask": {0: "batch", 1: "encoder_sequence"},
					}
					if self.use_past:
									lowerCamelCase_						=			"past_encoder_sequence + sequence"
									lowerCamelCase_						=			{0: "batch"}
									lowerCamelCase_						=			{0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
									lowerCamelCase_						=			{0: "batch", 1: "decoder_sequence"}
									lowerCamelCase_						=			{0: "batch", 1: "decoder_sequence"}

					if self.use_past:
									self.fill_with_past_key_values_(lowercase						,  direction="inputs" )

					return common_inputs



	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					return 13



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       float:
					return 5e-4









import os

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



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				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				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

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

      __A          =				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

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







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

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

      return savings


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





