


'''simple docstring'''



import functools
import operator

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


__UpperCAmelCase      :Tuple			    =       logging.get_logger(__name__)

__UpperCAmelCase      :Dict			    =       {
    "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      a    (		UpperCAmelCase_	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       """sew-d"""

     def __init__(    self					:		Union[str, Any]							,			snake_case					:		str=32							,			snake_case					:		Union[str, Any]=768							,			snake_case					:		Optional[int]=12							,			snake_case					:		List[Any]=12							,			snake_case					:		Any=3072							,			snake_case					:		Optional[Any]=2							,			snake_case					:		int=512							,			snake_case					:		Optional[Any]=256							,			snake_case					:		Optional[int]=True							,			snake_case					:		Optional[int]=True							,			snake_case					:		Optional[Any]=("p2c", "c2p")							,			snake_case					:		Optional[int]="layer_norm"							,			snake_case					:		str="gelu_python"							,			snake_case					:		int=0.1							,			snake_case					:		Optional[int]=0.1							,			snake_case					:		Optional[int]=0.1							,			snake_case					:		Optional[int]=0.0							,			snake_case					:		List[str]=0.1							,			snake_case					:		Tuple=0.02							,			snake_case					:		List[Any]=1E-7							,			snake_case					:		List[str]=1E-5							,			snake_case					:		Any="group"							,			snake_case					:		int="gelu"							,			snake_case					:		Tuple=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)							,			snake_case					:		Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,			snake_case					:		Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,			snake_case					:		Union[str, Any]=False							,			snake_case					:		Any=128							,			snake_case					:		Any=16							,			snake_case					:		Optional[Any]=True							,			snake_case					:		List[str]=0.05							,			snake_case					:		Optional[Any]=10							,			snake_case					:		Dict=2							,			snake_case					:		Optional[Any]=0.0							,			snake_case					:		Optional[int]=10							,			snake_case					:		List[Any]=0							,			snake_case					:		List[str]="mean"							,			snake_case					:		List[str]=False							,			snake_case					:		Dict=False							,			snake_case					:		int=256							,			snake_case					:		List[Any]=0							,			snake_case					:		Tuple=1							,			snake_case					:		int=2							,			**snake_case					:		List[str]							,			)						->							Dict:
          super().__init__(**__lowercase							,			pad_token_id=__lowercase							,			bos_token_id=__lowercase							,			eos_token_id=__lowercase      )
          __UpperCAmelCase     :			List[Any]  							=      hidden_size
          __UpperCAmelCase     :			Optional[int]  							=      feat_extract_norm
          __UpperCAmelCase     :			List[Any]  							=      feat_extract_activation
          __UpperCAmelCase     :			Tuple  							=      list(__lowercase      )
          __UpperCAmelCase     :			Optional[int]  							=      list(__lowercase      )
          __UpperCAmelCase     :			Optional[Any]  							=      list(__lowercase      )
          __UpperCAmelCase     :			Optional[Any]  							=      conv_bias
          __UpperCAmelCase     :			Optional[int]  							=      num_conv_pos_embeddings
          __UpperCAmelCase     :			List[str]  							=      num_conv_pos_embedding_groups
          __UpperCAmelCase     :			Dict  							=      len(self.conv_dim      )
          __UpperCAmelCase     :			Optional[int]  							=      num_hidden_layers
          __UpperCAmelCase     :			int  							=      intermediate_size
          __UpperCAmelCase     :			Union[str, Any]  							=      squeeze_factor
          __UpperCAmelCase     :			List[Any]  							=      max_position_embeddings
          __UpperCAmelCase     :			str  							=      position_buckets
          __UpperCAmelCase     :			Union[str, Any]  							=      share_att_key
          __UpperCAmelCase     :			List[str]  							=      relative_attention
          __UpperCAmelCase     :			Any  							=      norm_rel_ebd
          __UpperCAmelCase     :			int  							=      list(__lowercase      )
          __UpperCAmelCase     :			List[str]  							=      hidden_act
          __UpperCAmelCase     :			int  							=      num_attention_heads
          __UpperCAmelCase     :			Dict  							=      hidden_dropout
          __UpperCAmelCase     :			List[str]  							=      attention_dropout
          __UpperCAmelCase     :			Any  							=      activation_dropout
          __UpperCAmelCase     :			int  							=      feat_proj_dropout
          __UpperCAmelCase     :			Optional[int]  							=      final_dropout
          __UpperCAmelCase     :			List[Any]  							=      layer_norm_eps
          __UpperCAmelCase     :			Optional[int]  							=      feature_layer_norm_eps
          __UpperCAmelCase     :			List[Any]  							=      initializer_range
          __UpperCAmelCase     :			str  							=      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
          __UpperCAmelCase     :			Dict  							=      apply_spec_augment
          __UpperCAmelCase     :			Optional[Any]  							=      mask_time_prob
          __UpperCAmelCase     :			Optional[Any]  							=      mask_time_length
          __UpperCAmelCase     :			int  							=      mask_time_min_masks
          __UpperCAmelCase     :			str  							=      mask_feature_prob
          __UpperCAmelCase     :			Any  							=      mask_feature_length
          __UpperCAmelCase     :			Optional[Any]  							=      mask_feature_min_masks

          # ctc loss
          __UpperCAmelCase     :			Optional[Any]  							=      ctc_loss_reduction
          __UpperCAmelCase     :			List[str]  							=      ctc_zero_infinity

          # sequence classification
          __UpperCAmelCase     :			Optional[Any]  							=      use_weighted_layer_sum
          __UpperCAmelCase     :			Union[str, Any]  							=      classifier_proj_size






     @property
     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							str:
          return functools.reduce(operator.mul							,			self.conv_stride							,			1      )



'''simple docstring'''



def      _a     (						_lowercase :     int = 600851475143  ):





     '''simple docstring'''






     try:
          __UpperCAmelCase     :			str  							=      int(_lowercase  )
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''  )
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''  )
     __UpperCAmelCase     :			Dict  							=      1
     __UpperCAmelCase     :			List[str]  							=      2
     while i * i <= n:
          while n % i == 0:
               __UpperCAmelCase     :			int  							=      i
               n //= i
          i += 1
     if n > 1:
          __UpperCAmelCase     :			List[str]  							=      n
     return int(_lowercase  )


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