



import functools
import operator

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


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Union[str, Any] =  "unispeech-sat"

 def __init__(    self					:					Tuple			,						__lowerCAmelCase					:					Tuple=32			,						__lowerCAmelCase					:					List[Any]=7_68			,						__lowerCAmelCase					:					Optional[Any]=12			,						__lowerCAmelCase					:					Optional[Any]=12			,						__lowerCAmelCase					:					Dict=30_72			,						__lowerCAmelCase					:					str="gelu"			,						__lowerCAmelCase					:					int=0.1			,						__lowerCAmelCase					:					int=0.1			,						__lowerCAmelCase					:					Optional[int]=0.1			,						__lowerCAmelCase					:					Tuple=0.0			,						__lowerCAmelCase					:					Optional[int]=0.0			,						__lowerCAmelCase					:					Dict=0.1			,						__lowerCAmelCase					:					Optional[int]=0.1			,						__lowerCAmelCase					:					Optional[Any]=0.02			,						__lowerCAmelCase					:					Any=1E-5			,						__lowerCAmelCase					:					List[Any]="group"			,						__lowerCAmelCase					:					Optional[Any]="gelu"			,						__lowerCAmelCase					:					List[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,						__lowerCAmelCase					:					Any=(5, 2, 2, 2, 2, 2, 2)			,						__lowerCAmelCase					:					int=(10, 3, 3, 3, 3, 2, 2)			,						__lowerCAmelCase					:					List[str]=False			,						__lowerCAmelCase					:					int=1_28			,						__lowerCAmelCase					:					Tuple=16			,						__lowerCAmelCase					:					int=False			,						__lowerCAmelCase					:					int=True			,						__lowerCAmelCase					:					Union[str, Any]=0.05			,						__lowerCAmelCase					:					str=10			,						__lowerCAmelCase					:					List[str]=2			,						__lowerCAmelCase					:					Optional[int]=0.0			,						__lowerCAmelCase					:					Any=10			,						__lowerCAmelCase					:					str=0			,						__lowerCAmelCase					:					List[str]=3_20			,						__lowerCAmelCase					:					List[Any]=2			,						__lowerCAmelCase					:					Optional[Any]=0.1			,						__lowerCAmelCase					:					Dict=1_00			,						__lowerCAmelCase					:					Optional[int]=2_56			,						__lowerCAmelCase					:					List[Any]=2_56			,						__lowerCAmelCase					:					Any=0.1			,						__lowerCAmelCase					:					List[Any]="mean"			,						__lowerCAmelCase					:					Dict=False			,						__lowerCAmelCase					:					List[Any]=False			,						__lowerCAmelCase					:					Union[str, Any]=2_56			,						__lowerCAmelCase					:					int=(5_12, 5_12, 5_12, 5_12, 15_00)			,						__lowerCAmelCase					:					Optional[Any]=(5, 3, 3, 1, 1)			,						__lowerCAmelCase					:					str=(1, 2, 3, 1, 1)			,						__lowerCAmelCase					:					Union[str, Any]=5_12			,						__lowerCAmelCase					:					List[str]=0			,						__lowerCAmelCase					:					Dict=1			,						__lowerCAmelCase					:					List[str]=2			,						__lowerCAmelCase					:					Dict=5_04			,						**__lowerCAmelCase					:					Optional[Any]			,						)   ->	int:
    super().__init__(**__lowerCAmelCase			,						pad_token_id=__lowerCAmelCase			,						bos_token_id=__lowerCAmelCase			,						eos_token_id=__lowerCAmelCase      )
    _A      =       hidden_size
    _A      =       feat_extract_norm
    _A      =       feat_extract_activation
    _A      =       list(__lowerCAmelCase      )
    _A      =       list(__lowerCAmelCase      )
    _A      =       list(__lowerCAmelCase      )
    _A      =       conv_bias
    _A      =       num_conv_pos_embeddings
    _A      =       num_conv_pos_embedding_groups
    _A      =       len(self.conv_dim      )
    _A      =       num_hidden_layers
    _A      =       intermediate_size
    _A      =       hidden_act
    _A      =       num_attention_heads
    _A      =       hidden_dropout
    _A      =       attention_dropout
    _A      =       activation_dropout
    _A      =       feat_proj_dropout
    _A      =       final_dropout
    _A      =       layerdrop
    _A      =       layer_norm_eps
    _A      =       initializer_range
    _A      =       vocab_size
    _A      =       num_clusters
    _A      =       do_stable_layer_norm
    _A      =       use_weighted_layer_sum

    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
    _A      =       apply_spec_augment
    _A      =       mask_time_prob
    _A      =       mask_time_length
    _A      =       mask_time_min_masks
    _A      =       mask_feature_prob
    _A      =       mask_feature_length
    _A      =       mask_feature_min_masks

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

    # ctc loss
    _A      =       ctc_loss_reduction
    _A      =       ctc_zero_infinity

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

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

 @property
 def     snake_case_      (    self					:					Tuple      )   ->	Dict:
    return functools.reduce(operator.mul			,						self.conv_stride			,						1      )








import itertools
import math

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     bool:

   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(_snake_case				) + 1				)	,   6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Dict:
   _A      =       2
   while True:
      if is_prime(_snake_case				):
         yield num
      num += 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 10_001				)  ->     int:
   return next(itertools.islice(prime_generator()	,   nth - 1	,   _snake_case				)				)


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



