

'''simple docstring'''






from __future__ import annotations

from typing import Any


def  __lowerCamelCase      (				*SCREAMING_SNAKE_CASE_					:     Any   )							->       Dict:
      """simple docstring"""

      if all(_check_not_integer(_UpperCamelCase   ) for m in matrix_s   ):
            for i in matrix_s[1:]:
                  _verify_matrix_sizes(matrix_s[0]  ,  _UpperCamelCase   )
            return [[sum(_UpperCamelCase   ) for t in zip(*_UpperCamelCase   )] for m in zip(*_UpperCamelCase   )]
      raise TypeError("Expected a matrix, got int/list instead"   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int  ,  SCREAMING_SNAKE_CASE_					:     Dict   )							->       Union[str, Any]:
      """simple docstring"""

      if (
          _check_not_integer(_UpperCamelCase   )
          and _check_not_integer(_UpperCamelCase   )
          and _verify_matrix_sizes(_UpperCamelCase  ,  _UpperCamelCase   )
      ):
            return [[i - j for i, j in zip(*_UpperCamelCase   )] for m in zip(_UpperCamelCase  ,  _UpperCamelCase   )]
      raise TypeError("Expected a matrix, got int/list instead"   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any   )							->       int:
      """simple docstring"""

      return [[x * n for x in row] for row in matrix]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Dict  ,  SCREAMING_SNAKE_CASE_					:     int   )							->       List[Any]:
      """simple docstring"""

      if _check_not_integer(_UpperCamelCase   ) and _check_not_integer(_UpperCamelCase   ):
            SCREAMING_SNAKE_CASE_				:      int					=	_verify_matrix_sizes(_UpperCamelCase  ,  _UpperCamelCase   )

      if cols[0] != rows[1]:
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(
                "Cannot multiply matrix of dimensions "
                F"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
            )
            raise ValueError(_UpperCamelCase   )
      return [
          [sum(m * n for m, n in zip(_UpperCamelCase  ,  _UpperCamelCase   )   ) for j in zip(*_UpperCamelCase   )] for i in matrix_a
      ]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Union[str, Any]   )							->       List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	int(_UpperCamelCase   )
      return [[int(row == column   ) for column in range(_UpperCamelCase   )] for row in range(_UpperCamelCase   )]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Dict  ,  SCREAMING_SNAKE_CASE_					:     Tuple = True   )							->       Tuple:
      """simple docstring"""

      if _check_not_integer(_UpperCamelCase   ):
            if return_map:
                  return map(_UpperCamelCase  ,  zip(*_UpperCamelCase   )   )
            else:
                  return list(map(_UpperCamelCase  ,  zip(*_UpperCamelCase   )   )   )
      raise TypeError("Expected a matrix, got int/list instead"   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Any   )							->       str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      int					=	matrix[:row] + matrix[row + 1 :]
      return [row[:column] + row[column + 1 :] for row in minor]


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Union[str, Any]   )							->       Any:
      """simple docstring"""

      if len(_UpperCamelCase   ) == 1:
            return matrix[0][0]

      return sum(
          x * determinant(minor(_UpperCamelCase  ,  0  ,  _UpperCamelCase   )   ) * (-1) ** i
          for i, x in enumerate(matrix[0]   )   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	determinant(_UpperCamelCase   )
      if det == 0:
            return None

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	[
          [determinant(minor(_UpperCamelCase  ,  _UpperCamelCase  ,  _UpperCamelCase   )   ) for j in range(len(_UpperCamelCase   )   )]
          for i in range(len(_UpperCamelCase   )   )
      ]

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[
          [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]   )]
          for row in range(len(_UpperCamelCase   )   )
      ]
      SCREAMING_SNAKE_CASE_				:      Any					=	list(transpose(_UpperCamelCase   )   )
      return scalar_multiply(_UpperCamelCase  ,  1 / det   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int   )							->       Tuple:
      """simple docstring"""

      return not isinstance(_UpperCamelCase  ,  _UpperCamelCase   ) and not isinstance(matrix[0]  ,  _UpperCamelCase   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       str:
      """simple docstring"""

      return len(_UpperCamelCase   ), len(matrix[0]   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      str					=	_shape(_UpperCamelCase   ) + _shape(_UpperCamelCase   )
      if shape[0] != shape[3] or shape[1] != shape[2]:
            SCREAMING_SNAKE_CASE_				:      Tuple					=	(
                "operands could not be broadcast together with shape "
                F"({shape[0], shape[1]}), ({shape[2], shape[3]})"
            )
            raise ValueError(_UpperCamelCase   )
      return (shape[0], shape[2]), (shape[1], shape[3])


def  __lowerCamelCase      (				)							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	[[1_2, 1_0], [3, 9]]
      SCREAMING_SNAKE_CASE_				:      str					=	[[3, 4], [7, 4]]
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	[[1_1, 1_2, 1_3, 1_4], [2_1, 2_2, 2_3, 2_4], [3_1, 3_2, 3_3, 3_4], [4_1, 4_2, 4_3, 4_4]]
      SCREAMING_SNAKE_CASE_				:      List[str]					=	[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
      print(F"Add Operation, {add(_UpperCamelCase  ,  _UpperCamelCase   ) = } \n"   )
      print(
          F"Multiply Operation, {multiply(_UpperCamelCase  ,  _UpperCamelCase   ) = } \n"  ,  )
      print(F"Identity: {identity(5   )}\n"   )
      print(F"Minor of {matrix_c} = {minor(_UpperCamelCase  ,  1  ,  2   )} \n"   )
      print(F"Determinant of {matrix_b} = {determinant(_UpperCamelCase   )} \n"   )
      print(F"Inverse of {matrix_d} = {inverse(_UpperCamelCase   )}\n"   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




'''simple docstring'''






import functools
import operator

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


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							"hubert"




    def __init__( self    ,					lowercase__=32    ,					lowercase__=768    ,					lowercase__=12    ,					lowercase__=12    ,					lowercase__=3072    ,					lowercase__="gelu"    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=True    ,					lowercase__=0.0    ,					lowercase__=0.1    ,					lowercase__=0.1    ,					lowercase__=0.02    ,					lowercase__=1e-5    ,					lowercase__="group"    ,					lowercase__="gelu"    ,					lowercase__=(512, 512, 512, 512, 512, 512, 512)    ,					lowercase__=(5, 2, 2, 2, 2, 2, 2)    ,					lowercase__=(10, 3, 3, 3, 3, 2, 2)    ,					lowercase__=False    ,					lowercase__=128    ,					lowercase__=16    ,					lowercase__=False    ,					lowercase__=True    ,					lowercase__=0.05    ,					lowercase__=10    ,					lowercase__=2    ,					lowercase__=0.0    ,					lowercase__=10    ,					lowercase__=0    ,					lowercase__="sum"    ,					lowercase__=False    ,					lowercase__=False    ,					lowercase__=256    ,					lowercase__=0    ,					lowercase__=1    ,					lowercase__=2    ,					**lowercase__    ,					):






          """simple docstring"""






          super().__init__(**lowercase__    ,					pad_token_id=lowercase__    ,					bos_token_id=lowercase__    ,					eos_token_id=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	hidden_size
          SCREAMING_SNAKE_CASE_				:      List[str]					=	feat_extract_norm
          SCREAMING_SNAKE_CASE_				:      Dict					=	feat_extract_activation
          SCREAMING_SNAKE_CASE_				:      str					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      int					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	list(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	conv_bias
          SCREAMING_SNAKE_CASE_				:      str					=	num_conv_pos_embeddings
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	num_conv_pos_embedding_groups
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	len(self.conv_dim      )
          SCREAMING_SNAKE_CASE_				:      Any					=	num_hidden_layers
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	intermediate_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	hidden_act
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	num_attention_heads
          SCREAMING_SNAKE_CASE_				:      Any					=	hidden_dropout
          SCREAMING_SNAKE_CASE_				:      List[str]					=	attention_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	activation_dropout
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	feat_proj_layer_norm
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	feat_proj_dropout
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	final_dropout
          SCREAMING_SNAKE_CASE_				:      Dict					=	layerdrop
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	layer_norm_eps
          SCREAMING_SNAKE_CASE_				:      Tuple					=	initializer_range
          SCREAMING_SNAKE_CASE_				:      List[str]					=	vocab_size
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	do_stable_layer_norm
          SCREAMING_SNAKE_CASE_				:      List[str]					=	use_weighted_layer_sum
          SCREAMING_SNAKE_CASE_				:      Any					=	classifier_proj_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)`, but is `len(config.conv_dim) ="
                    F" {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,"
                    F" `len(config.conv_kernel) = {len(self.conv_kernel      )}`."      )

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          SCREAMING_SNAKE_CASE_				:      Tuple					=	apply_spec_augment
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	mask_time_prob
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	mask_time_length
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	mask_time_min_masks
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	mask_feature_prob
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	mask_feature_length
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	mask_feature_min_masks

          # ctc loss
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_loss_reduction
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ctc_zero_infinity







    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

