
from __future__ import annotations

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  NDArray[floataa]    ,							__lowerCAmelCase  :  NDArray[floataa]    ,							__lowerCAmelCase  :  list[int]    ,							__lowerCAmelCase  :  int    ,							):




			"""simple docstring"""


			lowerCAmelCase_	,  lowerCAmelCase_						=	coefficient_matrix.shape
			lowerCAmelCase_	,  lowerCAmelCase_						=	constant_matrix.shape

			if rowsa != colsa:
						lowerCAmelCase_						=	F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(__lowerCAmelCase				)

			if colsa != 1:
						lowerCAmelCase_						=	F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(__lowerCAmelCase				)

			if rowsa != rowsa:
						lowerCAmelCase_						=	(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
						)
						raise ValueError(__lowerCAmelCase				)

			if len(__lowerCAmelCase				) != rowsa:
						lowerCAmelCase_						=	(
						    "Number of initial values must be equal to number of rows in coefficient "
						    F"""matrix but received {len(__lowerCAmelCase				)} and {rowsa}"""
						)
						raise ValueError(__lowerCAmelCase				)

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

			lowerCAmelCase_						=	np.concatenate(
			    (coefficient_matrix, constant_matrix)    ,							axis=1				)

			lowerCAmelCase_	,  lowerCAmelCase_						=	table.shape

			strictly_diagonally_dominant(__lowerCAmelCase				)

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

			return [float(__lowerCAmelCase				) for i in new_val]






def    lowerCamelCase__				(				__lowerCAmelCase  :  NDArray[floataa]				):




			"""simple docstring"""


			lowerCAmelCase_	,  lowerCAmelCase_						=	table.shape

			lowerCAmelCase_						=	True

			for i in range(0    ,							__lowerCAmelCase				):
						lowerCAmelCase_						=	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()





import functools
import operator

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


_A   =     logging.get_logger(__name__)

_A   =     {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''sew'''




				def __init__(					self				,				_UpperCamelCase=32				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase=2				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-5				,				_UpperCamelCase="group"				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_UpperCamelCase=False				,				_UpperCamelCase=128				,				_UpperCamelCase=16				,				_UpperCamelCase=True				,				_UpperCamelCase=0.05				,				_UpperCamelCase=10				,				_UpperCamelCase=2				,				_UpperCamelCase=0.0				,				_UpperCamelCase=10				,				_UpperCamelCase=0				,				_UpperCamelCase="mean"				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=256				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=2				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							super().__init__(**_UpperCamelCase				,				pad_token_id=_UpperCamelCase				,				bos_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	feat_extract_norm
							lowerCAmelCase_						=	feat_extract_activation
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	conv_bias
							lowerCAmelCase_						=	num_conv_pos_embeddings
							lowerCAmelCase_						=	num_conv_pos_embedding_groups
							lowerCAmelCase_						=	len(self.conv_dim     )
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	squeeze_factor
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	feat_proj_dropout
							lowerCAmelCase_						=	final_dropout
							lowerCAmelCase_						=	layerdrop
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	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
							lowerCAmelCase_						=	apply_spec_augment
							lowerCAmelCase_						=	mask_time_prob
							lowerCAmelCase_						=	mask_time_length
							lowerCAmelCase_						=	mask_time_min_masks
							lowerCAmelCase_						=	mask_feature_prob
							lowerCAmelCase_						=	mask_feature_length
							lowerCAmelCase_						=	mask_feature_min_masks

							# ctc loss
							lowerCAmelCase_						=	ctc_loss_reduction
							lowerCAmelCase_						=	ctc_zero_infinity

							# sequence classification
							lowerCAmelCase_						=	use_weighted_layer_sum
							lowerCAmelCase_						=	classifier_proj_size




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



