






'''simple docstring'''

import functools
import operator

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


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    '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_						(      lowerCamelCase    ):
  lowercase							=				"""sew-d"""

  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=512      ,		_SCREAMING_SNAKE_CASE=256      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=("p2c", "c2p")      ,		_SCREAMING_SNAKE_CASE="layer_norm"      ,		_SCREAMING_SNAKE_CASE="gelu_python"      ,		_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=0.0_2      ,		_SCREAMING_SNAKE_CASE=1e-7      ,		_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=0.0_5      ,		_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      ,		)  ->				List[Any]:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE      ,		pad_token_id=_SCREAMING_SNAKE_CASE      ,		bos_token_id=_SCREAMING_SNAKE_CASE      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = hidden_size
        UpperCamelCase      = feat_extract_norm
        UpperCamelCase      = feat_extract_activation
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = conv_bias
        UpperCamelCase      = num_conv_pos_embeddings
        UpperCamelCase      = num_conv_pos_embedding_groups
        UpperCamelCase      = len(self.conv_dim				)
        UpperCamelCase      = num_hidden_layers
        UpperCamelCase      = intermediate_size
        UpperCamelCase      = squeeze_factor
        UpperCamelCase      = max_position_embeddings
        UpperCamelCase      = position_buckets
        UpperCamelCase      = share_att_key
        UpperCamelCase      = relative_attention
        UpperCamelCase      = norm_rel_ebd
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = hidden_act
        UpperCamelCase      = num_attention_heads
        UpperCamelCase      = hidden_dropout
        UpperCamelCase      = attention_dropout
        UpperCamelCase      = activation_dropout
        UpperCamelCase      = feat_proj_dropout
        UpperCamelCase      = final_dropout
        UpperCamelCase      = layer_norm_eps
        UpperCamelCase      = feature_layer_norm_eps
        UpperCamelCase      = initializer_range
        UpperCamelCase      = 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      = apply_spec_augment
        UpperCamelCase      = mask_time_prob
        UpperCamelCase      = mask_time_length
        UpperCamelCase      = mask_time_min_masks
        UpperCamelCase      = mask_feature_prob
        UpperCamelCase      = mask_feature_length
        UpperCamelCase      = mask_feature_min_masks

        # ctc loss
        UpperCamelCase      = ctc_loss_reduction
        UpperCamelCase      = ctc_zero_infinity

        # sequence classification
        UpperCamelCase      = use_weighted_layer_sum
        UpperCamelCase      = classifier_proj_size


  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return functools.reduce(operator.mul      ,		self.conv_stride      ,		1				)








'''simple docstring'''

from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = prime_factors(__UpperCamelCase   )
      if is_square_free(__UpperCamelCase   ):
            return -1 if len(__UpperCamelCase   ) % 2 else 1
      return 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()
