





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


_snake_case     :		Optional[Any]       						= logging.get_logger(__name__)

_snake_case     :		List[str]       						= {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''xglm'''
					UpperCamelCase      =							['''past_key_values''']

					UpperCamelCase      =							{
					    '''num_attention_heads''': '''attention_heads''',
					    '''hidden_size''': '''d_model''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(					self			:Optional[int]     ,       __UpperCamelCase			:Union[str, Any]=25_60_08     ,       __UpperCamelCase			:Optional[Any]=20_48     ,       __UpperCamelCase			:Optional[int]=10_24     ,       __UpperCamelCase			:Dict=40_96     ,       __UpperCamelCase			:List[Any]=24     ,       __UpperCamelCase			:str=16     ,       __UpperCamelCase			:str="gelu"     ,       __UpperCamelCase			:Any=0.1     ,       __UpperCamelCase			:Dict=0.1     ,       __UpperCamelCase			:List[Any]=0.0     ,       __UpperCamelCase			:Optional[Any]=0.0     ,       __UpperCamelCase			:int=0.02     ,       __UpperCamelCase			:List[str]=True     ,       __UpperCamelCase			:Tuple=True     ,       __UpperCamelCase			:int=2     ,       __UpperCamelCase			:List[Any]=1     ,       __UpperCamelCase			:Tuple=0     ,       __UpperCamelCase			:int=2     ,       **__UpperCamelCase			:List[Any]     ,       ):
												A		=  vocab_size
												A		=  max_position_embeddings
												A		=  d_model
												A		=  ffn_dim
												A		=  num_layers
												A		=  attention_heads
												A		=  activation_function
												A		=  dropout
												A		=  attention_dropout
												A		=  activation_dropout
												A		=  layerdrop
												A		=  init_std
												A		=  scale_embedding  # scale factor will be sqrt(d_model) if True
												A		=  use_cache

												super().__init__(
												    pad_token_id=__UpperCamelCase     ,       bos_token_id=__UpperCamelCase     ,       eos_token_id=__UpperCamelCase     ,       decoder_start_token_id=__UpperCamelCase     ,       **__UpperCamelCase     ,       )









"""simple docstring"""
from math import isqrt, loga






def 					A__				(			UpperCamelCase       ):
							A		=  [True] * max_number
							for i in range(2					,  isqrt(max_number - 1       ) + 1       ):
														if is_prime[i]:
																					for j in range(i**2					,  UpperCamelCase					,  UpperCamelCase       ):
																												A		=  False

							return [i for i in range(2					,  UpperCamelCase       ) if is_prime[i]]






def 					A__				(			UpperCamelCase = 800_800					,  UpperCamelCase = 800_800       ):
							A		=  degree * loga(UpperCamelCase       )
							A		=  int(UpperCamelCase       )
							A		=  calculate_prime_numbers(UpperCamelCase       )

							A		=  0
							A		=  0
							A		=  len(UpperCamelCase       ) - 1
							while left < right:
														while (
														    prime_numbers[right] * loga(prime_numbers[left]       )
														    + prime_numbers[left] * loga(prime_numbers[right]       )
														    > upper_bound
														):
																					right -= 1
														hybrid_integers_count += right - left
														left += 1

							return hybrid_integers_count


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


