




import functools
import operator

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


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

_lowerCamelCase     :		List[str]								=       {
    """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     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''sew'''


    def __init__(      self				:     Dict ,			UpperCAmelCase__				:     List[Any]=32 ,			UpperCAmelCase__				:     List[Any]=768 ,			UpperCAmelCase__				:     List[str]=12 ,			UpperCAmelCase__				:     Optional[Any]=12 ,			UpperCAmelCase__				:     Dict=3_072 ,			UpperCAmelCase__				:     List[str]=2 ,			UpperCAmelCase__				:     Optional[Any]="gelu" ,			UpperCAmelCase__				:     Dict=0.1 ,			UpperCAmelCase__				:     Tuple=0.1 ,			UpperCAmelCase__				:     List[str]=0.1 ,			UpperCAmelCase__				:     List[str]=0.0 ,			UpperCAmelCase__				:     Optional[int]=0.1 ,			UpperCAmelCase__				:     List[Any]=0.1 ,			UpperCAmelCase__				:     List[str]=0.02 ,			UpperCAmelCase__				:     List[str]=1e-5 ,			UpperCAmelCase__				:     Dict="group" ,			UpperCAmelCase__				:     Optional[int]="gelu" ,			UpperCAmelCase__				:     Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,			UpperCAmelCase__				:     int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,			UpperCAmelCase__				:     Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,			UpperCAmelCase__				:     Optional[Any]=False ,			UpperCAmelCase__				:     Optional[Any]=128 ,			UpperCAmelCase__				:     Optional[int]=16 ,			UpperCAmelCase__				:     Optional[Any]=True ,			UpperCAmelCase__				:     Union[str, Any]=0.05 ,			UpperCAmelCase__				:     str=10 ,			UpperCAmelCase__				:     int=2 ,			UpperCAmelCase__				:     Dict=0.0 ,			UpperCAmelCase__				:     Any=10 ,			UpperCAmelCase__				:     Optional[Any]=0 ,			UpperCAmelCase__				:     Optional[int]="mean" ,			UpperCAmelCase__				:     List[Any]=False ,			UpperCAmelCase__				:     Any=False ,			UpperCAmelCase__				:     Any=256 ,			UpperCAmelCase__				:     Dict=0 ,			UpperCAmelCase__				:     Any=1 ,			UpperCAmelCase__				:     List[str]=2 ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->List[str]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__ ,			pad_token_id=UpperCAmelCase__ ,			bos_token_id=UpperCAmelCase__ ,			eos_token_id=UpperCAmelCase__)
           A__							=   hidden_size
           A__							=   feat_extract_norm
           A__							=   feat_extract_activation
           A__							=   list(UpperCAmelCase__)
           A__							=   list(UpperCAmelCase__)
           A__							=   list(UpperCAmelCase__)
           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__							=   squeeze_factor
           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

           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
           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

           # ctc loss
           A__							=   ctc_loss_reduction
           A__							=   ctc_zero_infinity

           # sequence classification
           A__							=   use_weighted_layer_sum
           A__							=   classifier_proj_size





    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Any:





           '''simple docstring'''

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








from __future__ import annotations

from collections import Counter
from random import random


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           A__							=   probability


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->list[str]:





           '''simple docstring'''

           return list(self.connections)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           A__							=   0
           A__							=   random()

           for dest in self.connections[node]:
                  current_probability += self.connections[node][dest]
                  if current_probability > random_value:
                         return dest
           return ""







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	dict[str, int]:



       """simple docstring"""

       A__							=   MarkovChainGraphUndirectedUnweighted()

       for nodea, nodea, probability in transitions:
              graph.add_transition_probability(lowercase_			,    lowercase_			,    lowercase_						)

       A__							=   Counter(graph.get_nodes()						)
       A__							=   start

       for _ in range(lowercase_						):
              A__							=   graph.transition(lowercase_						)
              visited[node] += 1

       return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()


