




import functools
import operator

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


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

snake_case_ : Tuple    		=       {
    "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 __snake_case			(			a		):
   UpperCAmelCase__  :    List[Any]          =  '''sew-d'''







   def __init__(      self		:						List[str]					,						_snake_case		:						Tuple=32					,						_snake_case		:						Dict=768					,						_snake_case		:						str=12					,						_snake_case		:						Optional[int]=12					,						_snake_case		:						Optional[int]=3072					,						_snake_case		:						str=2					,						_snake_case		:						Tuple=512					,						_snake_case		:						Optional[Any]=256					,						_snake_case		:						Tuple=True					,						_snake_case		:						Dict=True					,						_snake_case		:						Optional[Any]=("p2c", "c2p")					,						_snake_case		:						int="layer_norm"					,						_snake_case		:						Any="gelu_python"					,						_snake_case		:						Any=0.1					,						_snake_case		:						Any=0.1					,						_snake_case		:						List[str]=0.1					,						_snake_case		:						List[str]=0.0					,						_snake_case		:						Optional[int]=0.1					,						_snake_case		:						int=0.0_2					,						_snake_case		:						Dict=1e-7					,						_snake_case		:						str=1e-5					,						_snake_case		:						Optional[Any]="group"					,						_snake_case		:						List[str]="gelu"					,						_snake_case		:						Union[str, Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)					,						_snake_case		:						int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)					,						_snake_case		:						Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)					,						_snake_case		:						int=False					,						_snake_case		:						List[str]=128					,						_snake_case		:						Optional[int]=16					,						_snake_case		:						int=True					,						_snake_case		:						List[Any]=0.0_5					,						_snake_case		:						str=10					,						_snake_case		:						Any=2					,						_snake_case		:						Tuple=0.0					,						_snake_case		:						Tuple=10					,						_snake_case		:						str=0					,						_snake_case		:						List[Any]="mean"					,						_snake_case		:						str=False					,						_snake_case		:						List[Any]=False					,						_snake_case		:						Dict=256					,						_snake_case		:						Union[str, Any]=0					,						_snake_case		:						Union[str, Any]=1					,						_snake_case		:						Optional[Any]=2					,						**_snake_case		:						Dict					,						):

        """simple docstring"""


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

        # ctc loss
        UpperCAmelCase_          =							ctc_loss_reduction
        UpperCAmelCase_          =							ctc_zero_infinity

        # sequence classification
        UpperCAmelCase_          =							use_weighted_layer_sum
        UpperCAmelCase_          =							classifier_proj_size







   @property
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
