





import functools
import operator

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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	str			=			"wavlm"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Union[str, Any]=32						,lowerCamelCase__						:    List[Any]=768						,lowerCamelCase__						:    str=12						,lowerCamelCase__						:    List[str]=12						,lowerCamelCase__						:    str=3072						,lowerCamelCase__						:    Dict="gelu"						,lowerCamelCase__						:    Union[str, Any]=0.1						,lowerCamelCase__						:    str=0.1						,lowerCamelCase__						:    List[str]=0.1						,lowerCamelCase__						:    List[Any]=0.0						,lowerCamelCase__						:    int=0.1						,lowerCamelCase__						:    Dict=0.1						,lowerCamelCase__						:    str=0.02						,lowerCamelCase__						:    int=1e-5						,lowerCamelCase__						:    Tuple="group"						,lowerCamelCase__						:    List[Any]="gelu"						,lowerCamelCase__						:    List[str]=(512, 512, 512, 512, 512, 512, 512)						,lowerCamelCase__						:    Dict=(5, 2, 2, 2, 2, 2, 2)						,lowerCamelCase__						:    Dict=(10, 3, 3, 3, 3, 2, 2)						,lowerCamelCase__						:    int=False						,lowerCamelCase__						:    int=128						,lowerCamelCase__						:    Tuple=16						,lowerCamelCase__						:    Any=320						,lowerCamelCase__						:    str=800						,lowerCamelCase__						:    Tuple=False						,lowerCamelCase__						:    Any=True						,lowerCamelCase__						:    Optional[int]=0.05						,lowerCamelCase__						:    List[Any]=10						,lowerCamelCase__						:    List[str]=2						,lowerCamelCase__						:    str=0.0						,lowerCamelCase__						:    List[Any]=10						,lowerCamelCase__						:    int=320						,lowerCamelCase__						:    int=2						,lowerCamelCase__						:    Optional[int]=0.1						,lowerCamelCase__						:    Dict=100						,lowerCamelCase__						:    List[Any]=256						,lowerCamelCase__						:    List[Any]=256						,lowerCamelCase__						:    Dict=0.1						,lowerCamelCase__						:    Optional[Any]="mean"						,lowerCamelCase__						:    Dict=False						,lowerCamelCase__						:    Union[str, Any]=False						,lowerCamelCase__						:    Optional[Any]=256						,lowerCamelCase__						:    Any=(512, 512, 512, 512, 1500)						,lowerCamelCase__						:    Tuple=(5, 3, 3, 1, 1)						,lowerCamelCase__						:    Optional[Any]=(1, 2, 3, 1, 1)						,lowerCamelCase__						:    Optional[int]=512						,lowerCamelCase__						:    Optional[int]=80						,lowerCamelCase__						:    Optional[Any]=0						,lowerCamelCase__						:    Optional[Any]=1						,lowerCamelCase__						:    Any=2						,lowerCamelCase__						:    Union[str, Any]=False						,lowerCamelCase__						:    Optional[Any]=3						,lowerCamelCase__						:    List[Any]=2						,lowerCamelCase__						:    Tuple=3						,lowerCamelCase__						:    Union[str, Any]=None						,**lowerCamelCase__						:    List[Any]						,)     ->		Tuple:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__						,pad_token_id=lowerCamelCase__						,bos_token_id=lowerCamelCase__						,eos_token_id=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						feat_extract_norm
           SCREAMING_SNAKE_CASE		=						feat_extract_activation
           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						conv_bias
           SCREAMING_SNAKE_CASE		=						num_buckets
           SCREAMING_SNAKE_CASE		=						max_bucket_distance
           SCREAMING_SNAKE_CASE		=						num_conv_pos_embeddings
           SCREAMING_SNAKE_CASE		=						num_conv_pos_embedding_groups
           SCREAMING_SNAKE_CASE		=						len(self.conv_dim       )
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						intermediate_size
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						hidden_dropout
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						activation_dropout
           SCREAMING_SNAKE_CASE		=						feat_proj_dropout
           SCREAMING_SNAKE_CASE		=						final_dropout
           SCREAMING_SNAKE_CASE		=						layerdrop
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						num_ctc_classes
           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						do_stable_layer_norm
           SCREAMING_SNAKE_CASE		=						use_weighted_layer_sum
           SCREAMING_SNAKE_CASE		=						classifier_proj_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)`, but is `len(config.conv_dim) ="""
                     F""" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"""
                     F""" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."""       )

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           SCREAMING_SNAKE_CASE		=						apply_spec_augment
           SCREAMING_SNAKE_CASE		=						mask_time_prob
           SCREAMING_SNAKE_CASE		=						mask_time_length
           SCREAMING_SNAKE_CASE		=						mask_time_min_masks
           SCREAMING_SNAKE_CASE		=						mask_feature_prob
           SCREAMING_SNAKE_CASE		=						mask_feature_length

           # parameters for pretraining with codevector quantized representations
           SCREAMING_SNAKE_CASE		=						num_codevectors_per_group
           SCREAMING_SNAKE_CASE		=						num_codevector_groups
           SCREAMING_SNAKE_CASE		=						contrastive_logits_temperature
           SCREAMING_SNAKE_CASE		=						num_negatives
           SCREAMING_SNAKE_CASE		=						codevector_dim
           SCREAMING_SNAKE_CASE		=						proj_codevector_dim
           SCREAMING_SNAKE_CASE		=						diversity_loss_weight

           # ctc loss
           SCREAMING_SNAKE_CASE		=						ctc_loss_reduction
           SCREAMING_SNAKE_CASE		=						ctc_zero_infinity

           # adapter
           SCREAMING_SNAKE_CASE		=						add_adapter
           SCREAMING_SNAKE_CASE		=						adapter_kernel_size
           SCREAMING_SNAKE_CASE		=						adapter_stride
           SCREAMING_SNAKE_CASE		=						num_adapter_layers
           SCREAMING_SNAKE_CASE		=						output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           SCREAMING_SNAKE_CASE		=						classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						xvector_output_dim






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Optional[int]:




           '''simple docstring'''


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









import math
import unittest



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   bool:


      '''simple docstring'''


      assert isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) and (
          number >= 0
      ), "'number' must been an int and positive"

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5 ,						int(math.sqrt(_SCREAMING_SNAKE_CASE	) + 1	) ,						6	):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Tuple:




           '''simple docstring'''


           self.assertTrue(is_prime(2       )       )
           self.assertTrue(is_prime(3       )       )
           self.assertTrue(is_prime(5       )       )
           self.assertTrue(is_prime(7       )       )
           self.assertTrue(is_prime(11       )       )
           self.assertTrue(is_prime(13       )       )
           self.assertTrue(is_prime(17       )       )
           self.assertTrue(is_prime(19       )       )
           self.assertTrue(is_prime(23       )       )
           self.assertTrue(is_prime(29       )       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           with self.assertRaises(lowerCamelCase__       ):
                 is_prime(-19       )
           self.assertFalse(
               is_prime(0       )						,"""Zero doesn't have any positive factors, primes must have exactly two."""						,)
           self.assertFalse(
               is_prime(1       )						,"""One only has 1 positive factor, primes must have exactly two."""						,)
           self.assertFalse(is_prime(2 * 2       )       )
           self.assertFalse(is_prime(2 * 3       )       )
           self.assertFalse(is_prime(3 * 3       )       )
           self.assertFalse(is_prime(3 * 5       )       )
           self.assertFalse(is_prime(3 * 5 * 7       )       )


if __name__ == "__main__":
      unittest.main()


