





'''simple docstring'''

import functools
import operator

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


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

__lowerCAmelCase							:	List[Any]			={
    "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    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""sew-d"""
 def __init__(				self      :Union[str, Any]						,       lowercase_      :str=32						,       lowercase_      :Any=7_68						,       lowercase_      :Optional[int]=12						,       lowercase_      :int=12						,       lowercase_      :str=30_72						,       lowercase_      :Union[str, Any]=2						,       lowercase_      :List[str]=5_12						,       lowercase_      :Dict=2_56						,       lowercase_      :Union[str, Any]=True						,       lowercase_      :Dict=True						,       lowercase_      :Union[str, Any]=("p2c", "c2p")						,       lowercase_      :Optional[Any]="layer_norm"						,       lowercase_      :Any="gelu_python"						,       lowercase_      :Optional[int]=0.1						,       lowercase_      :str=0.1						,       lowercase_      :Optional[Any]=0.1						,       lowercase_      :int=0.0						,       lowercase_      :Optional[Any]=0.1						,       lowercase_      :Any=0.0_2						,       lowercase_      :int=1E-7						,       lowercase_      :Any=1E-5						,       lowercase_      :int="group"						,       lowercase_      :List[str]="gelu"						,       lowercase_      :str=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)						,       lowercase_      :Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,       lowercase_      :Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,       lowercase_      :int=False						,       lowercase_      :Optional[Any]=1_28						,       lowercase_      :List[Any]=16						,       lowercase_      :int=True						,       lowercase_      :Tuple=0.0_5						,       lowercase_      :str=10						,       lowercase_      :Dict=2						,       lowercase_      :str=0.0						,       lowercase_      :Any=10						,       lowercase_      :List[str]=0						,       lowercase_      :Optional[int]="mean"						,       lowercase_      :Tuple=False						,       lowercase_      :List[str]=False						,       lowercase_      :int=2_56						,       lowercase_      :Dict=0						,       lowercase_      :Optional[Any]=1						,       lowercase_      :Any=2						,       **lowercase_      :List[Any]						,       )->							Dict:
     super().__init__(**lowercase_						,       pad_token_id=lowercase_						,       bos_token_id=lowercase_						,       eos_token_id=lowercase_							)
     A__				=			hidden_size
     A__				=			feat_extract_norm
     A__				=			feat_extract_activation
     A__				=			list(lowercase_							)
     A__				=			list(lowercase_							)
     A__				=			list(lowercase_							)
     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__				=			max_position_embeddings
     A__				=			position_buckets
     A__				=			share_att_key
     A__				=			relative_attention
     A__				=			norm_rel_ebd
     A__				=			list(lowercase_							)
     A__				=			hidden_act
     A__				=			num_attention_heads
     A__				=			hidden_dropout
     A__				=			attention_dropout
     A__				=			activation_dropout
     A__				=			feat_proj_dropout
     A__				=			final_dropout
     A__				=			layer_norm_eps
     A__				=			feature_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      UpperCAmelCase_							(				self      :Any							)->							Dict:
     return functools.reduce(operator.mul						,       self.conv_stride						,       1							)










'''simple docstring'''

from __future__ import annotations

from decimal import Decimal

from numpy import array





def 						UpperCamelCase				(							_lowerCamelCase					:					list[list[float]]       ):
    A__				=			Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(_lowerCamelCase       ) == 2 and len(matrix[0]       ) == 2 and len(matrix[1]       ) == 2:
        # Calculate the determinant of the matrix
        A__				=			float(
            d(matrix[0][0]       ) * d(matrix[1][1]       ) - d(matrix[1][0]       ) * d(matrix[0][1]       )       )
        if determinant == 0:
            raise ValueError("This matrix has no inverse."       )

        # Creates a copy of the matrix with swapped positions of the elements
        A__				=			[[0.0, 0.0], [0.0, 0.0]]
        A__,	A__				=			matrix[1][1], matrix[0][0]
        A__,	A__				=			-matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(_lowerCamelCase       )       ) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(_lowerCamelCase       ) == 3
        and len(matrix[0]       ) == 3
        and len(matrix[1]       ) == 3
        and len(matrix[2]       ) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        A__				=			float(
            (
                (d(matrix[0][0]       ) * d(matrix[1][1]       ) * d(matrix[2][2]       ))
                + (d(matrix[0][1]       ) * d(matrix[1][2]       ) * d(matrix[2][0]       ))
                + (d(matrix[0][2]       ) * d(matrix[1][0]       ) * d(matrix[2][1]       ))
            )
            - (
                (d(matrix[0][2]       ) * d(matrix[1][1]       ) * d(matrix[2][0]       ))
                + (d(matrix[0][1]       ) * d(matrix[1][0]       ) * d(matrix[2][2]       ))
                + (d(matrix[0][0]       ) * d(matrix[1][2]       ) * d(matrix[2][1]       ))
            )       )
        if determinant == 0:
            raise ValueError("This matrix has no inverse."       )

        # Creating cofactor matrix
        A__				=			[
            [d(0.0       ), d(0.0       ), d(0.0       )],
            [d(0.0       ), d(0.0       ), d(0.0       )],
            [d(0.0       ), d(0.0       ), d(0.0       )],
        ]
        A__				=			(d(matrix[1][1]       ) * d(matrix[2][2]       )) - (
            d(matrix[1][2]       ) * d(matrix[2][1]       )
        )
        A__				=			-(
            (d(matrix[1][0]       ) * d(matrix[2][2]       )) - (d(matrix[1][2]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[1][0]       ) * d(matrix[2][1]       )) - (
            d(matrix[1][1]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][1]       ) * d(matrix[2][2]       )) - (d(matrix[0][2]       ) * d(matrix[2][1]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[2][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[2][1]       )) - (d(matrix[0][1]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[0][1]       ) * d(matrix[1][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[1][1]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[1][2]       )) - (d(matrix[0][2]       ) * d(matrix[1][0]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[1][1]       )) - (
            d(matrix[0][1]       ) * d(matrix[1][0]       )
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        A__				=			array(_lowerCamelCase       )
        for i in range(3       ):
            for j in range(3       ):
                A__				=			cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        A__				=			array(_lowerCamelCase       )
        for i in range(3       ):
            for j in range(3       ):
                inverse_matrix[i][j] /= d(_lowerCamelCase       )

        # Calculate the inverse of the matrix
        return [[float(d(_lowerCamelCase       )       ) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3."       )



