



"""simple docstring"""







import functools
import operator

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


lowercase__		:				Dict          =	logging.get_logger(__name__)

lowercase__		:				Optional[int]          =	{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """sew-d"""



      def __init__(      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Any=3_2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Tuple=1_2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=3_0_7_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      int=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Any=2_5_6    ,			SCREAMING_SNAKE_CASE_				:      List[str]=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=True    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=("p2c", "c2p")    ,			SCREAMING_SNAKE_CASE_				:      str="layer_norm"    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]="gelu_python"    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=0.1    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=0.1    ,			SCREAMING_SNAKE_CASE_				:      Any=0.1    ,			SCREAMING_SNAKE_CASE_				:      Dict=0.0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.1    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=0.02    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1E-7    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=1E-5    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="group"    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      str=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=False    ,			SCREAMING_SNAKE_CASE_				:      Any=1_2_8    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1_6    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=True    ,			SCREAMING_SNAKE_CASE_				:      Any=0.05    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_0    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=0.0    ,			SCREAMING_SNAKE_CASE_				:      int=1_0    ,			SCREAMING_SNAKE_CASE_				:      str=0    ,			SCREAMING_SNAKE_CASE_				:      int="mean"    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=False    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=False    ,			SCREAMING_SNAKE_CASE_				:      str=2_5_6    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=0    ,			SCREAMING_SNAKE_CASE_				:      Dict=1    ,			SCREAMING_SNAKE_CASE_				:      List[str]=2    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        super().__init__(**__UpperCamelCase    ,			pad_token_id=__UpperCamelCase    ,			bos_token_id=__UpperCamelCase    ,			eos_token_id=__UpperCamelCase       )
        lowerCAmelCase_   :				int								= hidden_size
        lowerCAmelCase_   :				int								= feat_extract_norm
        lowerCAmelCase_   :				Optional[Any]								= feat_extract_activation
        lowerCAmelCase_   :				List[str]								= list(__UpperCamelCase       )
        lowerCAmelCase_   :				Optional[int]								= list(__UpperCamelCase       )
        lowerCAmelCase_   :				List[Any]								= list(__UpperCamelCase       )
        lowerCAmelCase_   :				List[str]								= conv_bias
        lowerCAmelCase_   :				Dict								= num_conv_pos_embeddings
        lowerCAmelCase_   :				List[Any]								= num_conv_pos_embedding_groups
        lowerCAmelCase_   :				Union[str, Any]								= len(self.conv_dim       )
        lowerCAmelCase_   :				List[str]								= num_hidden_layers
        lowerCAmelCase_   :				Union[str, Any]								= intermediate_size
        lowerCAmelCase_   :				List[str]								= squeeze_factor
        lowerCAmelCase_   :				Dict								= max_position_embeddings
        lowerCAmelCase_   :				Any								= position_buckets
        lowerCAmelCase_   :				Any								= share_att_key
        lowerCAmelCase_   :				Optional[Any]								= relative_attention
        lowerCAmelCase_   :				List[Any]								= norm_rel_ebd
        lowerCAmelCase_   :				Dict								= list(__UpperCamelCase       )
        lowerCAmelCase_   :				List[Any]								= hidden_act
        lowerCAmelCase_   :				Union[str, Any]								= num_attention_heads
        lowerCAmelCase_   :				Tuple								= hidden_dropout
        lowerCAmelCase_   :				Tuple								= attention_dropout
        lowerCAmelCase_   :				List[str]								= activation_dropout
        lowerCAmelCase_   :				Optional[int]								= feat_proj_dropout
        lowerCAmelCase_   :				Any								= final_dropout
        lowerCAmelCase_   :				Optional[Any]								= layer_norm_eps
        lowerCAmelCase_   :				Dict								= feature_layer_norm_eps
        lowerCAmelCase_   :				Any								= initializer_range
        lowerCAmelCase_   :				int								= vocab_size

        if (
            (len(self.conv_stride       ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
            or (len(self.conv_dim       ) != self.num_feat_extract_layers)
        ):
          raise ValueError(
              'Configuration for convolutional layers is incorrect.'
              'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
              F"but is `len(config.conv_dim) = {len(self.conv_dim       )}`, `len(config.conv_stride)"
              F"= {len(self.conv_stride       )}`, `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        lowerCAmelCase_   :				List[Any]								= apply_spec_augment
        lowerCAmelCase_   :				str								= mask_time_prob
        lowerCAmelCase_   :				Any								= mask_time_length
        lowerCAmelCase_   :				Optional[int]								= mask_time_min_masks
        lowerCAmelCase_   :				Dict								= mask_feature_prob
        lowerCAmelCase_   :				Optional[int]								= mask_feature_length
        lowerCAmelCase_   :				Optional[Any]								= mask_feature_min_masks

        # ctc loss
        lowerCAmelCase_   :				int								= ctc_loss_reduction
        lowerCAmelCase_   :				str								= ctc_zero_infinity

        # sequence classification
        lowerCAmelCase_   :				List[str]								= use_weighted_layer_sum
        lowerCAmelCase_   :				Union[str, Any]								= classifier_proj_size





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        return functools.reduce(operator.mul    ,			self.conv_stride    ,			1       )












"""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() = }""")


