






from __future__ import annotations

from typing import Any


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						float = 0   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   int						    =     row, column
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     [[default_value for c in range(UpperCAmelCase_   )] for r in range(UpperCAmelCase_   )]


     def __str__(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

       # Make string identifier
       SCREAMING_SNAKE_CASE							:   Dict						    =     0
       for row_vector in self.array:
         for obj in row_vector:
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     max(UpperCAmelCase_       ,			len(str(UpperCAmelCase_   )   )   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     f'''%{max_element_length}s'''

       # Make string and return
       def single_line(UpperCAmelCase_							:						list[float]   ) -> str:
         nonlocal string_format_identifier
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     "["
         line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
         line += "]"
         return line

       s += "\n".join(single_line(UpperCAmelCase_   ) for row_vector in self.array   )
       return s


     def __repr__(					self							:						Dict   ):
       return str(self   )


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						tuple[int, int]   ):
       if not (isinstance(UpperCAmelCase_       ,			(list, tuple)   ) and len(UpperCAmelCase_   ) == 2):
         return False
       elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
         return False
       else:
         return True


     def __getitem__(					self							:						Any       ,			UpperCAmelCase_							:						tuple[int, int]   ):
       assert self.validate_indicies(UpperCAmelCase_   )
       return self.array[loc[0]][loc[1]]


     def __setitem__(					self							:						int       ,			UpperCAmelCase_							:						tuple[int, int]       ,			UpperCAmelCase_							:						float   ):
       assert self.validate_indicies(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     value


     def __add__(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Matrix   ):
       assert isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   )
       assert self.row == another.row and self.column == another.column

       # Add
       SCREAMING_SNAKE_CASE							:   str						    =     Matrix(self.row       ,			self.column   )
       for r in range(self.row   ):
         for c in range(self.column   ):
           SCREAMING_SNAKE_CASE							:   Any						    =     self[r, c] + another[r, c]
       return result


     def __neg__(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     Matrix(self.row       ,			self.column   )
       for r in range(self.row   ):
         for c in range(self.column   ):
           SCREAMING_SNAKE_CASE							:   str						    =     -self[r, c]
       return result


     def __sub__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						Matrix   ):
       return self + (-another)


     def __mul__(					self							:						Dict       ,			UpperCAmelCase_							:						int | float | Matrix   ):

       if isinstance(UpperCAmelCase_       ,			(int, float)   ):  # Scalar multiplication
         SCREAMING_SNAKE_CASE							:   Any						    =     Matrix(self.row       ,			self.column   )
         for r in range(self.row   ):
           for c in range(self.column   ):
             SCREAMING_SNAKE_CASE							:   str						    =     self[r, c] * another
         return result
       elif isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):  # Matrix multiplication
         assert self.column == another.row
         SCREAMING_SNAKE_CASE							:   Any						    =     Matrix(self.row       ,			another.column   )
         for r in range(self.row   ):
           for c in range(another.column   ):
             for i in range(self.column   ):
               result[r, c] += self[r, i] * another[i, c]
         return result
       else:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     f'''Unsupported type given for another ({type(UpperCAmelCase_   )})'''
         raise TypeError(UpperCAmelCase_   )


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     Matrix(self.column       ,			self.row   )
       for r in range(self.row   ):
         for c in range(self.column   ):
           SCREAMING_SNAKE_CASE							:   List[str]						    =     self[r, c]
       return result






     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Matrix       ,			UpperCAmelCase_							:						Matrix   ):
       assert isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ) and isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   )
       assert self.row == self.column == u.row == v.row  # u, v should be column vector
       assert u.column == v.column == 1  # u, v should be column vector

       # Calculate
       SCREAMING_SNAKE_CASE							:   Tuple						    =     v.transpose()
       SCREAMING_SNAKE_CASE							:   int						    =     (v_t * self * u)[0, 0] + 1
       if numerator_factor == 0:
         return None  # It's not invertable
       return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


   def 	lowerCamelCase__      (  ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE							:   Tuple						    =     Matrix(3    ,  3    ,  0					)
     for i in range(3					):
       SCREAMING_SNAKE_CASE							:   str						    =     1
     print(F'''a^(-1) is {ainv}'''					)
     # u, v
     SCREAMING_SNAKE_CASE							:   Optional[int]						    =     Matrix(3    ,  1    ,  0					)
     SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Optional[int]						    =     1, 2, -3
     SCREAMING_SNAKE_CASE							:   Tuple						    =     Matrix(3    ,  1    ,  0					)
     SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   int						    =     4, -2, 5
     print(F'''u is {u}'''					)
     print(F'''v is {v}'''					)
     print(F'''uv^T is {u * v.transpose()}'''					)
     # Sherman Morrison
     print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(lowercase    ,  lowercase					)}'''					)


   def 	lowerCamelCase__      (  ):

     """simple docstring"""






     import doctest

     doctest.testmod()

   testa()










import functools
import operator

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


snake_case							:       Optional[int]										=	logging.get_logger(__name__)

snake_case							:       Optional[Any]										=	{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'wavlm'




 def __init__(						self   ,    _lowerCamelCase=32   ,    _lowerCamelCase=768   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-5   ,    _lowerCamelCase="group"   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,    _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,    _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,    _lowerCamelCase=False   ,    _lowerCamelCase=128   ,    _lowerCamelCase=16   ,    _lowerCamelCase=320   ,    _lowerCamelCase=800   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=0.05   ,    _lowerCamelCase=10   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=10   ,    _lowerCamelCase=320   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=100   ,    _lowerCamelCase=256   ,    _lowerCamelCase=256   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase="mean"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False   ,    _lowerCamelCase=256   ,    _lowerCamelCase=(512, 512, 512, 512, 1500)   ,    _lowerCamelCase=(5, 3, 3, 1, 1)   ,    _lowerCamelCase=(1, 2, 3, 1, 1)   ,    _lowerCamelCase=512   ,    _lowerCamelCase=80   ,    _lowerCamelCase=0   ,    _lowerCamelCase=1   ,    _lowerCamelCase=2   ,    _lowerCamelCase=False   ,    _lowerCamelCase=3   ,    _lowerCamelCase=2   ,    _lowerCamelCase=3   ,    _lowerCamelCase=None   ,    **_lowerCamelCase   ,    ):
       super().__init__(**_lowerCamelCase   ,    pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase       )
       a		:Union[str, Any]									=			hidden_size
       a		:List[Any]									=			feat_extract_norm
       a		:Tuple									=			feat_extract_activation
       a		:Tuple									=			list(_lowerCamelCase       )
       a		:Optional[Any]									=			list(_lowerCamelCase       )
       a		:Optional[Any]									=			list(_lowerCamelCase       )
       a		:int									=			conv_bias
       a		:Tuple									=			num_buckets
       a		:Tuple									=			max_bucket_distance
       a		:int									=			num_conv_pos_embeddings
       a		:List[str]									=			num_conv_pos_embedding_groups
       a		:Tuple									=			len(self.conv_dim       )
       a		:Any									=			num_hidden_layers
       a		:Tuple									=			intermediate_size
       a		:Any									=			hidden_act
       a		:List[str]									=			num_attention_heads
       a		:int									=			hidden_dropout
       a		:Optional[Any]									=			attention_dropout
       a		:Dict									=			activation_dropout
       a		:Any									=			feat_proj_dropout
       a		:Dict									=			final_dropout
       a		:int									=			layerdrop
       a		:Tuple									=			layer_norm_eps
       a		:Union[str, Any]									=			initializer_range
       a		:Union[str, Any]									=			num_ctc_classes
       a		:Optional[Any]									=			vocab_size
       a		:str									=			do_stable_layer_norm
       a		:Dict									=			use_weighted_layer_sum
       a		: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
       a		:List[Any]									=			apply_spec_augment
       a		:Optional[int]									=			mask_time_prob
       a		:Optional[Any]									=			mask_time_length
       a		:Tuple									=			mask_time_min_masks
       a		:Dict									=			mask_feature_prob
       a		:List[Any]									=			mask_feature_length

       # parameters for pretraining with codevector quantized representations
       a		:Tuple									=			num_codevectors_per_group
       a		:List[str]									=			num_codevector_groups
       a		:List[Any]									=			contrastive_logits_temperature
       a		:Union[str, Any]									=			num_negatives
       a		:Union[str, Any]									=			codevector_dim
       a		:Dict									=			proj_codevector_dim
       a		:Union[str, Any]									=			diversity_loss_weight

       # ctc loss
       a		:Dict									=			ctc_loss_reduction
       a		:Optional[int]									=			ctc_zero_infinity

       # adapter
       a		:Any									=			add_adapter
       a		:str									=			adapter_kernel_size
       a		:Tuple									=			adapter_stride
       a		:Dict									=			num_adapter_layers
       a		:Any									=			output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       a		:Tuple									=			classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       a		:List[Any]									=			list(_lowerCamelCase       )
       a		:Optional[int]									=			list(_lowerCamelCase       )
       a		:List[str]									=			list(_lowerCamelCase       )
       a		:Optional[int]									=			xvector_output_dim




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return functools.reduce(operator.mul   ,    self.conv_stride   ,    1       )





