


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :    Tuple									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Tuple									=				{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='nllb-moe'
		lowerCamelCase__				=['past_key_values']
		lowerCamelCase__				={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



		def __init__(self				,       a_=12_81_12				,       a_=10_24				,       a_=12				,       a_=40_96				,       a_=16				,       a_=12				,       a_=40_96				,       a_=16				,       a_=0.05				,       a_=0.05				,       a_=True				,       a_=True				,       a_="relu"				,       a_=10_24				,       a_=0.1				,       a_=0.1				,       a_=0.0				,       a_=0.02				,       a_=2				,       a_=True				,       a_=False				,       a_="float32"				,       a_=False				,       a_=1_28				,       a_=64				,       a_=4				,       a_=4				,       a_=0.001				,       a_=0.001				,       a_="all"				,       a_=False				,       a_=False				,       a_=1.0				,       a_=0.2				,       a_=1				,       a_=0				,       a_=2				,       a_=False				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				vocab_size
			__snake_case	:						int						=				max_position_embeddings
			__snake_case	:						int						=				d_model
			__snake_case	:						List[str]						=				encoder_ffn_dim
			__snake_case	:						Optional[Any]						=				encoder_layers
			__snake_case	:						Dict						=				encoder_attention_heads
			__snake_case	:						str						=				decoder_ffn_dim
			__snake_case	:						Dict						=				decoder_layers
			__snake_case	:						Union[str, Any]						=				decoder_attention_heads
			__snake_case	:						Optional[Any]						=				dropout
			__snake_case	:						Union[str, Any]						=				attention_dropout
			__snake_case	:						Union[str, Any]						=				activation_dropout
			__snake_case	:						List[Any]						=				activation_function
			__snake_case	:						int						=				init_std
			__snake_case	:						List[Any]						=				encoder_layerdrop
			__snake_case	:						Optional[Any]						=				decoder_layerdrop
			__snake_case	:						Tuple						=				use_cache
			__snake_case	:						Dict						=				encoder_layers
			__snake_case	:						List[str]						=				scale_embedding  # scale factor will be sqrt(d_model) if True
			__snake_case	:						List[str]						=				router_z_loss_coef
			__snake_case	:						int						=				router_aux_loss_coef
			__snake_case	:						Union[str, Any]						=				decoder_sparse_step
			__snake_case	:						Tuple						=				encoder_sparse_step
			__snake_case	:						int						=				num_experts
			__snake_case	:						List[Any]						=				expert_capacity
			__snake_case	:						int						=				router_bias
			if router_dtype not in ["float32", "float16", "bfloat16"]:
				raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""					)
			__snake_case	:						Optional[int]						=				router_dtype

			__snake_case	:						Tuple						=				router_ignore_padding_tokens
			__snake_case	:						List[Any]						=				batch_prioritized_routing
			__snake_case	:						Optional[int]						=				second_expert_policy
			__snake_case	:						Any						=				normalize_router_prob_before_dropping
			__snake_case	:						List[Any]						=				moe_eval_capacity_token_fraction
			__snake_case	:						Optional[int]						=				moe_token_dropout
			__snake_case	:						int						=				output_router_logits
			super().__init__(
			    pad_token_id=a_				,       bos_token_id=a_				,       eos_token_id=a_				,       is_encoder_decoder=a_				,       decoder_start_token_id=a_				,       **a_				,       )








'''simple docstring'''






import os

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


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = 0

 __lowercase							  = 0
 while index < len(A__      ) - 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 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = ''''''

 __lowercase							  = num // 1000
 numerals += m_count * "M"
 num %= 1000

 __lowercase							  = 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

 __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 		_A     (    A__ = "/p089_roman.txt"      ):






 """simple docstring"""



 __lowercase							  = 0

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

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

 return savings


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

