




'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       List[str]						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Any						=			{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				List[Any]							=   "mvp"
			lowerCAmelCase_					:				Optional[Any]							=   ["past_key_values"]
			lowerCAmelCase_					:				Tuple							=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__( self    ,							a__=50_267    ,							a__=1_024    ,							a__=12    ,							a__=4_096    ,							a__=16    ,							a__=12    ,							a__=4_096    ,							a__=16    ,							a__=0.0    ,							a__=0.0    ,							a__="gelu"    ,							a__=1_024    ,							a__=0.1    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0_2    ,							a__=0.0    ,							a__=False    ,							a__=True    ,							a__=1    ,							a__=0    ,							a__=2    ,							a__=True    ,							a__=2    ,							a__=2    ,							a__=False    ,							a__=100    ,							a__=800    ,							**a__    ,							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				vocab_size
										snake_case_      =				max_position_embeddings
										snake_case_      =				d_model
										snake_case_      =				encoder_ffn_dim
										snake_case_      =				encoder_layers
										snake_case_      =				encoder_attention_heads
										snake_case_      =				decoder_ffn_dim
										snake_case_      =				decoder_layers
										snake_case_      =				decoder_attention_heads
										snake_case_      =				dropout
										snake_case_      =				attention_dropout
										snake_case_      =				activation_dropout
										snake_case_      =				activation_function
										snake_case_      =				init_std
										snake_case_      =				encoder_layerdrop
										snake_case_      =				decoder_layerdrop
										snake_case_      =				classifier_dropout
										snake_case_      =				use_cache
										snake_case_      =				encoder_layers
										snake_case_      =				scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case_      =				use_prompt
										snake_case_      =				prompt_length
										snake_case_      =				prompt_mid_dim

										super().__init__(
										    pad_token_id=a__    ,							bos_token_id=a__    ,							eos_token_id=a__    ,							is_encoder_decoder=a__    ,							decoder_start_token_id=a__    ,							forced_eos_token_id=a__    ,							**a__    ,							)

										if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"    ,							a__							):
																	snake_case_      =				self.bos_token_id
																	warnings.warn(
																	    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
																	    "The config can simply be saved and uploaded again to be fixed."							)










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0_0_0_0					):

							'''simple docstring'''






							snake_case_      =				set(range(3							,    snake_case							,    2					)					)
							primes.add(2					)
							for p in range(3							,    snake_case							,    2					):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p							,    snake_case							,    snake_case					)					)					)

							snake_case_      =				[float(snake_case					) for n in range(limit + 1					)]

							for p in primes:
														for n in range(snake_case							,    limit + 1							,    snake_case					):
																					phi[n] *= 1 - 1 / p

							return int(sum(phi[2:]					)					)


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




