



"""simple docstring"""




import functools
import operator

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


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

SCREAMING_SNAKE_CASE_     :			int	  =			{
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class 		_A       (			__a					):
      __a								=	"sew"





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=3072    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=1e-5    ,						SCREAMING_SNAKE_CASE__="group"    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,						SCREAMING_SNAKE_CASE__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,						SCREAMING_SNAKE_CASE__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=128    ,						SCREAMING_SNAKE_CASE__=16    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=0.05    ,						SCREAMING_SNAKE_CASE__=10    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=10    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__="mean"    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=256    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__=1    ,						SCREAMING_SNAKE_CASE__=2    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Tuple:
         super().__init__(**SCREAMING_SNAKE_CASE__    ,						pad_token_id=SCREAMING_SNAKE_CASE__    ,						bos_token_id=SCREAMING_SNAKE_CASE__    ,						eos_token_id=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			hidden_size
         lowerCamelCase__						=			feat_extract_norm
         lowerCamelCase__						=			feat_extract_activation
         lowerCamelCase__						=			list(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			list(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			list(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			conv_bias
         lowerCamelCase__						=			num_conv_pos_embeddings
         lowerCamelCase__						=			num_conv_pos_embedding_groups
         lowerCamelCase__						=			len(self.conv_dim		)
         lowerCamelCase__						=			num_hidden_layers
         lowerCamelCase__						=			intermediate_size
         lowerCamelCase__						=			squeeze_factor
         lowerCamelCase__						=			hidden_act
         lowerCamelCase__						=			num_attention_heads
         lowerCamelCase__						=			hidden_dropout
         lowerCamelCase__						=			attention_dropout
         lowerCamelCase__						=			activation_dropout
         lowerCamelCase__						=			feat_proj_dropout
         lowerCamelCase__						=			final_dropout
         lowerCamelCase__						=			layerdrop
         lowerCamelCase__						=			layer_norm_eps
         lowerCamelCase__						=			initializer_range
         lowerCamelCase__						=			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__						=			apply_spec_augment
         lowerCamelCase__						=			mask_time_prob
         lowerCamelCase__						=			mask_time_length
         lowerCamelCase__						=			mask_time_min_masks
         lowerCamelCase__						=			mask_feature_prob
         lowerCamelCase__						=			mask_feature_length
         lowerCamelCase__						=			mask_feature_min_masks

         # ctc loss
         lowerCamelCase__						=			ctc_loss_reduction
         lowerCamelCase__						=			ctc_zero_infinity

         # sequence classification
         lowerCamelCase__						=			use_weighted_layer_sum
         lowerCamelCase__						=			classifier_proj_size





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







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if length <= 0 or not isinstance(A__	,    A__   ):
      raise ValueError("Length must be a positive integer."   )
   return [n * (2 * n - 1) for n in range(A__   )]


if __name__ == "__main__":
     print(hexagonal_numbers(length=5))
     print(hexagonal_numbers(length=10))




