




import functools
import operator

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


lowerCamelCase							:						int					       =  logging.get_logger(__name__)

lowerCamelCase							:						Union[str, Any]					       =  {
    "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     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		'''sew'''

 def __init__(							self   :    int		,				A_   :    Optional[Any]=32		,				A_   :    str=768		,				A_   :    Any=12		,				A_   :    Optional[Any]=12		,				A_   :    str=3072		,				A_   :    Union[str, Any]=2		,				A_   :    Union[str, Any]="gelu"		,				A_   :    Dict=0.1		,				A_   :    Optional[int]=0.1		,				A_   :    Optional[int]=0.1		,				A_   :    List[str]=0.0		,				A_   :    List[str]=0.1		,				A_   :    int=0.1		,				A_   :    Any=0.02		,				A_   :    Tuple=1E-5		,				A_   :    Optional[Any]="group"		,				A_   :    Union[str, Any]="gelu"		,				A_   :    List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,				A_   :    Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				A_   :    List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				A_   :    str=False		,				A_   :    int=128		,				A_   :    Optional[Any]=16		,				A_   :    List[Any]=True		,				A_   :    List[str]=0.05		,				A_   :    List[str]=10		,				A_   :    int=2		,				A_   :    Union[str, Any]=0.0		,				A_   :    List[Any]=10		,				A_   :    Dict=0		,				A_   :    List[str]="mean"		,				A_   :    Optional[Any]=False		,				A_   :    Union[str, Any]=False		,				A_   :    Optional[int]=256		,				A_   :    Optional[Any]=0		,				A_   :    List[Any]=1		,				A_   :    Optional[int]=2		,				**A_   :    Tuple		,				)  ->      List[Any]:

       """simple docstring"""






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

       # ctc loss
       lowerCamelCase_							=       ctc_loss_reduction
       lowerCamelCase_							=       ctc_zero_infinity

       # sequence classification
       lowerCamelCase_							=       use_weighted_layer_sum
       lowerCamelCase_							=       classifier_proj_size





 @property
 def 							a__					(							self   :    Union[str, Any]    )  ->      Any:

       """simple docstring"""






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







from random import randint, random




def    a__    (		snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							bool = False						,   snake_case__					:							bool = False						,   snake_case__					:							int = 5						,   ):
  _UpperCAmelCase		:							Optional[int]							=     [[-1] * number_of_cells]  # Create a highway without any car
  _UpperCAmelCase		:							Optional[Any]							=     0
  _UpperCAmelCase		:							Dict							=     max(snake_case__						,   0     )
  while i < number_of_cells:
    _UpperCAmelCase		:							int							=     (
        randint(0						,   snake_case__     ) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1						,   max_speed * 2     ) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							List[Any]							=     0
  _UpperCAmelCase		:							int							=     highway_now[car_index + 1 :]
  for cell in range(len(snake_case__     )     ):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(snake_case__						,   -1     )




def    a__    (		snake_case__					:							list						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     len(snake_case__     )
  # Beforce calculations, the highway is empty
  _UpperCAmelCase		:							Dict							=     [-1] * number_of_cells

  for car_index in range(snake_case__     ):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _UpperCAmelCase		:							Dict							=     min(highway_now[car_index] + 1						,   snake_case__     )
      # Number of empty cell before the next car
      _UpperCAmelCase		:							List[str]							=     get_distance(snake_case__						,   snake_case__     ) - 1
      # We can't have the car causing an accident
      _UpperCAmelCase		:							List[str]							=     min(next_highway[car_index]						,   snake_case__     )
      if random() < probability:
        # Randomly, a driver will slow down
        _UpperCAmelCase		:							Dict							=     max(next_highway[car_index] - 1						,   0     )
  return next_highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Union[str, Any]							=     len(highway[0]     )

  for i in range(snake_case__     ):
    _UpperCAmelCase		:							Tuple							=     update(highway[i]						,   snake_case__						,   snake_case__     )
    _UpperCAmelCase		:							int							=     [-1] * number_of_cells

    for car_index in range(snake_case__     ):
      _UpperCAmelCase		:							List[Any]							=     next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _UpperCAmelCase		:							Optional[Any]							=     (car_index + speed) % number_of_cells
        # Commit the change of position
        _UpperCAmelCase		:							Optional[Any]							=     speed
    highway.append(snake_case__     )

  return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()





