'''simple docstring'''
import functools
import operator

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


a=  logging.get_logger(__name__)

a=  {
    '''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''unispeech-sat'''







     def __init__(		self     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=7_6_8     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=3_0_7_2     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-5     ,			_lowerCamelCase="group"     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,			_lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)     ,			_lowerCamelCase=(1_0, 3, 3, 3, 3, 2, 2)     ,			_lowerCamelCase=False     ,			_lowerCamelCase=1_2_8     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=0.0_5     ,			_lowerCamelCase=1_0     ,			_lowerCamelCase=2     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=1_0     ,			_lowerCamelCase=0     ,			_lowerCamelCase=3_2_0     ,			_lowerCamelCase=2     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=1_0_0     ,			_lowerCamelCase=2_5_6     ,			_lowerCamelCase=2_5_6     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase="mean"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=2_5_6     ,			_lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,			_lowerCamelCase=(5, 3, 3, 1, 1)     ,			_lowerCamelCase=(1, 2, 3, 1, 1)     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=0     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			_lowerCamelCase=5_0_4     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase     ,			pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  hidden_size
         __UpperCamelCase	:	Tuple		    =  feat_extract_norm
         __UpperCamelCase	:	str		    =  feat_extract_activation
         __UpperCamelCase	:	Any		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  list(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  conv_bias
         __UpperCamelCase	:	Optional[Any]		    =  num_conv_pos_embeddings
         __UpperCamelCase	:	int		    =  num_conv_pos_embedding_groups
         __UpperCamelCase	:	Tuple		    =  len(self.conv_dim    )
         __UpperCamelCase	:	List[Any]		    =  num_hidden_layers
         __UpperCamelCase	:	List[Any]		    =  intermediate_size
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_act
         __UpperCamelCase	:	Any		    =  num_attention_heads
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_dropout
         __UpperCamelCase	:	Any		    =  attention_dropout
         __UpperCamelCase	:	List[Any]		    =  activation_dropout
         __UpperCamelCase	:	List[Any]		    =  feat_proj_dropout
         __UpperCamelCase	:	Any		    =  final_dropout
         __UpperCamelCase	:	Optional[int]		    =  layerdrop
         __UpperCamelCase	:	List[Any]		    =  layer_norm_eps
         __UpperCamelCase	:	Dict		    =  initializer_range
         __UpperCamelCase	:	Tuple		    =  vocab_size
         __UpperCamelCase	:	Union[str, Any]		    =  num_clusters
         __UpperCamelCase	:	Dict		    =  do_stable_layer_norm
         __UpperCamelCase	:	Dict		    =  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
         __UpperCamelCase	:	List[Any]		    =  apply_spec_augment
         __UpperCamelCase	:	List[Any]		    =  mask_time_prob
         __UpperCamelCase	:	int		    =  mask_time_length
         __UpperCamelCase	:	Dict		    =  mask_time_min_masks
         __UpperCamelCase	:	Any		    =  mask_feature_prob
         __UpperCamelCase	:	Dict		    =  mask_feature_length
         __UpperCamelCase	:	List[str]		    =  mask_feature_min_masks

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

         # ctc loss
         __UpperCamelCase	:	List[Any]		    =  ctc_loss_reduction
         __UpperCamelCase	:	Dict		    =  ctc_zero_infinity

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         __UpperCamelCase	:	List[str]		    =  classifier_proj_size

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







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




'''simple docstring'''
from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def  _UpperCamelCase (     _a	:	NDArray[floataa]				,     _a	:	NDArray[floataa]				,     _a	:	list[int]				,     _a	:	int				,     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  coefficient_matrix.shape
    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  constant_matrix.shape

    if rowsa != colsa:
        __UpperCamelCase	:	Tuple		    =  f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if colsa != 1:
        __UpperCamelCase	:	List[Any]		    =  f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
        raise ValueError(_a			)

    if rowsa != rowsa:
        __UpperCamelCase	:	Optional[int]		    =  (
            'Coefficient and constant matrices dimensions must be nxn and nx1 but '
            f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
        )
        raise ValueError(_a			)

    if len(_a			) != rowsa:
        __UpperCamelCase	:	Union[str, Any]		    =  (
            'Number of initial values must be equal to number of rows in coefficient '
            f"""matrix but received {len(_a			)} and {rowsa}"""
        )
        raise ValueError(_a			)

    if iterations <= 0:
        raise ValueError('Iterations must be at least 1'			)

    __UpperCamelCase	:	NDArray[floataa]		    =  np.concatenate(
        (coefficient_matrix, constant_matrix)				,     axis=1			)

    __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  table.shape

    strictly_diagonally_dominant(_a			)

    # Iterates the whole matrix for given number of times
    for _ in range(_a			):
        __UpperCamelCase	:	List[Any]		    =  []
        for row in range(_a			):
            __UpperCamelCase	:	List[Any]		    =  0
            for col in range(_a			):
                if col == row:
                    __UpperCamelCase	:	Optional[int]		    =  table[row][col]
                elif col == cols - 1:
                    __UpperCamelCase	:	str		    =  table[row][col]
                else:
                    temp += (-1) * table[row][col] * init_val[col]
            __UpperCamelCase	:	Any		    =  (temp + val) / denom
            new_val.append(_a			)
        __UpperCamelCase	:	List[Any]		    =  new_val

    return [float(_a			) for i in new_val]







def  _UpperCamelCase (     _a	:	NDArray[floataa]			):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  table.shape

    __UpperCamelCase	:	str		    =  True

    for i in range(0				,     _a			):
        __UpperCamelCase	:	Optional[Any]		    =  0
        for j in range(0				,     cols - 1			):
            if i == j:
                continue
            else:
                total += table[i][j]

        if table[i][i] <= total:
            raise ValueError('Coefficient matrix is not strictly diagonally dominant'			)

    return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()



