


from __future__ import annotations

from typing import Any





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				int		,				__a				:				int		,				__a				:				int		,				__a				:				float = 0							)					->None:
          lowerCamelCase_,	lowerCamelCase_						:   Optional[Any]		= row, column
          lowerCamelCase_						:   Tuple		= [[default_value for c in range(__a							)] for r in range(__a							)]



       def __str__(						self				:				Union[str, Any]							)					->str:
          lowerCamelCase_						:   List[Any]		= F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

          # Make string identifier
          lowerCamelCase_						:   List[Any]		= 0
          for row_vector in self.array:
             for obj in row_vector:
                lowerCamelCase_						:   Any		= max(__a		,				len(str(__a							)							)							)
          lowerCamelCase_						:   Optional[Any]		= F'''%{max_element_length}s'''

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

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



       def __repr__(						self				:				Any							)					->str:
          return str(self							)



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				tuple[int, int]							)					->bool:
          if not (isinstance(__a		,				(list, tuple)							) and len(__a							) == 2):
             return False
          elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
          else:
             return True



       def __getitem__(						self				:				List[str]		,				__a				:				tuple[int, int]							)					->Any:
          assert self.validate_indicies(__a							)
          return self.array[loc[0]][loc[1]]



       def __setitem__(						self				:				Union[str, Any]		,				__a				:				tuple[int, int]		,				__a				:				float							)					->None:
          assert self.validate_indicies(__a							)
          lowerCamelCase_						:   int		= value



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

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



       def __neg__(						self				:				Optional[Any]							)					->Matrix:
          lowerCamelCase_						:   Dict		= Matrix(self.row		,				self.column							)
          for r in range(self.row							):
             for c in range(self.column							):
                lowerCamelCase_						:   Dict		= -self[r, c]
          return result



       def __sub__(						self				:				Optional[int]		,				__a				:				Matrix							)					->Matrix:
          return self + (-another)



       def __mul__(						self				:				Dict		,				__a				:				int | float | Matrix							)					->Matrix:

          if isinstance(__a		,				(int, float)							):  # Scalar multiplication
             lowerCamelCase_						:   Optional[Any]		= Matrix(self.row		,				self.column							)
             for r in range(self.row							):
                for c in range(self.column							):
                   lowerCamelCase_						:   int		= self[r, c] * another
             return result
          elif isinstance(__a		,				__a							):  # Matrix multiplication
             assert self.column == another.row
             lowerCamelCase_						:   Optional[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:
             lowerCamelCase_						:   Tuple		= F'''Unsupported type given for another ({type(__a							)})'''
             raise TypeError(__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->Matrix:
          lowerCamelCase_						:   str		= Matrix(self.column		,				self.row							)
          for r in range(self.row							):
             for c in range(self.column							):
                lowerCamelCase_						:   Optional[int]		= self[r, c]
          return result




       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				Matrix		,				__a				:				Matrix							)					->Any:
          assert isinstance(__a		,				__a							) and isinstance(__a		,				__a							)
          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
          lowerCamelCase_						:   List[str]		= v.transpose()
          lowerCamelCase_						:   List[str]		= (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   (      )     ->    None:
         # a^(-1)
         lowerCamelCase_						:   str		= Matrix(3						,						3						,						0					)
         for i in range(3					):
            lowerCamelCase_						:   List[Any]		= 1
         print(f'''a^(-1) is {ainv}'''					)
         # u, v
         lowerCamelCase_						:   List[Any]		= Matrix(3						,						1						,						0					)
         lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Optional[int]		= 1, 2, -3
         lowerCamelCase_						:   Tuple		= Matrix(3						,						1						,						0					)
         lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Optional[Any]		= 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(A__						,						A__					)}'''					)


      def    __lowerCamelCase   (      )     ->    None:
         import doctest

         doctest.testmod()



      testa()









import functools
import operator

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


snake_case__				:	Union[str, Any]					       =					logging.get_logger(__name__)

snake_case__				:	Tuple					       =					{
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"unispeech-sat"



       def __init__(						self				:				List[str]		,				__a				:				Dict=32		,				__a				:				int=768		,				__a				:				int=12		,				__a				:				Tuple=12		,				__a				:				Optional[int]=3_072		,				__a				:				int="gelu"		,				__a				:				Optional[int]=0.1		,				__a				:				Union[str, Any]=0.1		,				__a				:				Any=0.1		,				__a				:				str=0.0		,				__a				:				List[str]=0.0		,				__a				:				int=0.1		,				__a				:				List[Any]=0.1		,				__a				:				Tuple=0.02		,				__a				:				str=1e-5		,				__a				:				Optional[int]="group"		,				__a				:				Any="gelu"		,				__a				:				Optional[Any]=(512, 512, 512, 512, 512, 512, 512)		,				__a				:				Optional[Any]=(5, 2, 2, 2, 2, 2, 2)		,				__a				:				List[Any]=(10, 3, 3, 3, 3, 2, 2)		,				__a				:				Optional[Any]=False		,				__a				:				Optional[int]=128		,				__a				:				Dict=16		,				__a				:				str=False		,				__a				:				List[Any]=True		,				__a				:				Tuple=0.05		,				__a				:				str=10		,				__a				:				Any=2		,				__a				:				Any=0.0		,				__a				:				List[Any]=10		,				__a				:				str=0		,				__a				:				str=320		,				__a				:				int=2		,				__a				:				Optional[Any]=0.1		,				__a				:				Optional[int]=100		,				__a				:				Any=256		,				__a				:				Optional[int]=256		,				__a				:				int=0.1		,				__a				:				Tuple="mean"		,				__a				:				List[str]=False		,				__a				:				Optional[Any]=False		,				__a				:				List[Any]=256		,				__a				:				Dict=(512, 512, 512, 512, 1_500)		,				__a				:				int=(5, 3, 3, 1, 1)		,				__a				:				str=(1, 2, 3, 1, 1)		,				__a				:				str=512		,				__a				:				str=0		,				__a				:				List[Any]=1		,				__a				:				Optional[Any]=2		,				__a				:				Optional[int]=504		,				**__a				:				Tuple		,				)					->Dict:
          super().__init__(**__a		,				pad_token_id=__a		,				bos_token_id=__a		,				eos_token_id=__a							)
          lowerCamelCase_						:   Any		= hidden_size
          lowerCamelCase_						:   Optional[int]		= feat_extract_norm
          lowerCamelCase_						:   Dict		= feat_extract_activation
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   Any		= list(__a							)
          lowerCamelCase_						:   List[Any]		= list(__a							)
          lowerCamelCase_						:   str		= conv_bias
          lowerCamelCase_						:   int		= num_conv_pos_embeddings
          lowerCamelCase_						:   List[Any]		= num_conv_pos_embedding_groups
          lowerCamelCase_						:   str		= len(self.conv_dim							)
          lowerCamelCase_						:   Any		= num_hidden_layers
          lowerCamelCase_						:   List[str]		= intermediate_size
          lowerCamelCase_						:   Tuple		= hidden_act
          lowerCamelCase_						:   List[str]		= num_attention_heads
          lowerCamelCase_						:   Any		= hidden_dropout
          lowerCamelCase_						:   Optional[int]		= attention_dropout
          lowerCamelCase_						:   Tuple		= activation_dropout
          lowerCamelCase_						:   str		= feat_proj_dropout
          lowerCamelCase_						:   Any		= final_dropout
          lowerCamelCase_						:   Optional[Any]		= layerdrop
          lowerCamelCase_						:   str		= layer_norm_eps
          lowerCamelCase_						:   str		= initializer_range
          lowerCamelCase_						:   Optional[Any]		= vocab_size
          lowerCamelCase_						:   Optional[int]		= num_clusters
          lowerCamelCase_						:   List[Any]		= do_stable_layer_norm
          lowerCamelCase_						:   Tuple		= use_weighted_layer_sum

          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
          lowerCamelCase_						:   List[str]		= apply_spec_augment
          lowerCamelCase_						:   str		= mask_time_prob
          lowerCamelCase_						:   List[Any]		= mask_time_length
          lowerCamelCase_						:   Optional[int]		= mask_time_min_masks
          lowerCamelCase_						:   List[Any]		= mask_feature_prob
          lowerCamelCase_						:   Optional[int]		= mask_feature_length
          lowerCamelCase_						:   Dict		= mask_feature_min_masks

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

          # ctc loss
          lowerCamelCase_						:   Optional[int]		= ctc_loss_reduction
          lowerCamelCase_						:   int		= ctc_zero_infinity

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

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




       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[Any]:
          return functools.reduce(operator.mul		,				self.conv_stride		,				1							)





