


"""simple docstring"""

import functools
import operator

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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "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    lowerCamelCase__						(		_a    ):
      a :     Tuple											=		"""sew-d"""
      def __init__(		self	:	Optional[int]      ,       A_	:	int=3_2      ,       A_	:	Any=7_6_8      ,       A_	:	Any=1_2      ,       A_	:	int=1_2      ,       A_	:	List[Any]=3_0_7_2      ,       A_	:	int=2      ,       A_	:	Optional[Any]=5_1_2      ,       A_	:	Union[str, Any]=2_5_6      ,       A_	:	Union[str, Any]=True      ,       A_	:	Union[str, Any]=True      ,       A_	:	List[Any]=("p2c", "c2p")      ,       A_	:	Dict="layer_norm"      ,       A_	:	List[Any]="gelu_python"      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Any=0.1      ,       A_	:	Optional[Any]=0.1      ,       A_	:	Optional[Any]=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	List[str]=0.02      ,       A_	:	List[str]=1e-7      ,       A_	:	List[str]=1e-5      ,       A_	:	Union[str, Any]="group"      ,       A_	:	int="gelu"      ,       A_	:	Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,       A_	:	Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,       A_	:	int=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,       A_	:	str=False      ,       A_	:	Tuple=1_2_8      ,       A_	:	List[Any]=1_6      ,       A_	:	Dict=True      ,       A_	:	str=0.05      ,       A_	:	Tuple=1_0      ,       A_	:	List[str]=2      ,       A_	:	List[Any]=0.0      ,       A_	:	Optional[Any]=1_0      ,       A_	:	str=0      ,       A_	:	Tuple="mean"      ,       A_	:	List[str]=False      ,       A_	:	Tuple=False      ,       A_	:	List[Any]=2_5_6      ,       A_	:	Optional[Any]=0      ,       A_	:	Optional[int]=1      ,       A_	:	Optional[int]=2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


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

       # ctc loss
       __lowercase									=	ctc_loss_reduction
       __lowercase									=	ctc_zero_infinity

       # sequence classification
       __lowercase									=	use_weighted_layer_sum
       __lowercase									=	classifier_proj_size






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


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




"""simple docstring"""

# Algorithm for the pigeonhole sorting




def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	min(UpperCamelCase__ )  # min() finds the minimum value
 __lowercase									=	max(UpperCamelCase__ )  # max() finds the maximum value

 __lowercase									=	max_val - min_val + 1  # size is difference of max and min values plus one

 # list of pigeonholes of size equal to the variable size
 __lowercase									=	[0] * size

 # Populate the pigeonholes.
 for x in a:
  assert isinstance(UpperCamelCase__				,   UpperCamelCase__ ), "integers only please"
  holes[x - min_val] += 1

 # Putting the elements back into the array in an order.
 __lowercase									=	0
 for count in range(UpperCamelCase__ ):
  while holes[count] > 0:
   holes[count] -= 1
   __lowercase									=	count + min_val
   i += 1


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	[8, 3, 2, 7, 4, 6, 8]
 pigeonhole_sort(UpperCamelCase__ )
 print("""Sorted order is:"""				,   """ """.join(UpperCamelCase__ ) )


if __name__ == "__main__":
       main()
