


from __future__ import annotations

from collections.abc import Callable

snake_case__			 =						list[list[float | int]]


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   str				)    ->	Tuple:





			"""simple docstring"""
			a__ :List[str]						  =    len(lowercase__				)
			a__ :List[str]						  =    [[0 for _ in range(size + 1				)] for _ in range(lowercase__				)]
			a__ :Tuple						  =    42
			a__ :int						  =    42
			a__ :List[Any]						  =    42
			a__ :int						  =    42
			a__ :List[Any]						  =    42
			a__ :Union[str, Any]						  =    42

			for row in range(lowercase__				):
						for col in range(lowercase__				):
									a__ :Union[str, Any]						  =    matrix[row][col]

						a__ :int						  =    vector[row][0]

			a__ :str						  =    0
			a__ :int						  =    0
			while row < size and col < size:
						# pivoting
						a__ :Dict						  =    max((abs(augmented[rowa][col]				), rowa) for rowa in range(lowercase__	,  lowercase__				)				)[
						    1
						]
						if augmented[pivot_row][col] == 0:
									col += 1
									continue
						else:
									a__  ,  a__ :Any						  =    augmented[pivot_row], augmented[row]

						for rowa in range(row + 1	,  lowercase__				):
									a__ :Optional[int]						  =    augmented[rowa][col] / augmented[row][col]
									a__ :Union[str, Any]						  =    0
									for cola in range(col + 1	,  size + 1				):
												augmented[rowa][cola] -= augmented[row][cola] * ratio

						row += 1
						col += 1

			# back substitution
			for col in range(1	,  lowercase__				):
						for row in range(lowercase__				):
									a__ :Dict						  =    augmented[row][col] / augmented[col][col]
									for cola in range(lowercase__	,  size + 1				):
												augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
			return [
			    [round(augmented[row][size] / augmented[row][row]	,  10				)] for row in range(lowercase__				)
			]


def 				lowerCamelCase__	(      a	:   str				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    len(lowercase__				)
			a__ :Dict						  =    [[0 for _ in range(lowercase__				)] for _ in range(lowercase__				)]
			a__ :str						  =    [[0] for _ in range(lowercase__				)]
			a__ :Tuple						  =    42
			a__ :str						  =    42
			a__ :int						  =    42
			a__ :Optional[int]						  =    42

			for x_val, y_val in enumerate(lowercase__				):
						for col in range(lowercase__				):
									a__ :str						  =    (x_val + 1) ** (size - col - 1)
						a__ :int						  =    y_val

			a__ :Tuple						  =    solve(lowercase__	,  lowercase__				)

			def interpolated_func(a	:   List[Any]				) -> int:
						return sum(
						    round(coeffs[x_val][0]				) * (var ** (size - x_val - 1))
						    for x_val in range(lowercase__				)				)

			return interpolated_func


def 				lowerCamelCase__	(      a	:   Any				)    ->	Optional[Any]:





			"""simple docstring"""
			return (
			    1
			    - variable
			    + variable**2
			    - variable**3
			    + variable**4
			    - variable**5
			    + variable**6
			    - variable**7
			    + variable**8
			    - variable**9
			    + variable**10
			)


def 				lowerCamelCase__	(      a	:   Union[str, Any] = question_function	,  a	:   Any = 10				)    ->	Any:





			"""simple docstring"""
			a__ :List[str]						  =    [func(lowercase__				) for x_val in range(1	,  order + 1				)]

			a__ :List[str]						  =    [
			    interpolate(data_points[:max_coeff]				) for max_coeff in range(1	,  order + 1				)
			]

			a__ :Any						  =    0
			a__ :Tuple						  =    42
			a__ :Optional[int]						  =    42

			for poly in polynomials:
						a__ :Any						  =    1
						while func(lowercase__				) == poly(lowercase__				):
									x_val += 1

						ret += poly(lowercase__				)

			return ret


if __name__ == "__main__":
		print(f'''{solution() = }''')






import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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):
						lowerCamelCase_         =     'sew'




						def __init__(	self						:   Any   ,     __A						:   str=32   ,     __A						:   Dict=768   ,     __A						:   int=12   ,     __A						:   Dict=12   ,     __A						:   Dict=3072   ,     __A						:   int=2   ,     __A						:   Union[str, Any]="gelu"   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   str=0.0   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Tuple=0.02   ,     __A						:   Any=1E-5   ,     __A						:   Optional[Any]="group"   ,     __A						:   str="gelu"   ,     __A						:   Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[str]=False   ,     __A						:   Tuple=128   ,     __A						:   Tuple=16   ,     __A						:   Optional[int]=True   ,     __A						:   Union[str, Any]=0.05   ,     __A						:   List[str]=10   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.0   ,     __A						:   Optional[Any]=10   ,     __A						:   Tuple=0   ,     __A						:   Tuple="mean"   ,     __A						:   Any=False   ,     __A						:   str=False   ,     __A						:   Dict=256   ,     __A						:   Union[str, Any]=0   ,     __A						:   Optional[int]=1   ,     __A						:   Optional[Any]=2   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :List[Any]						  =    hidden_size
									a__ :List[Any]						  =    feat_extract_norm
									a__ :List[str]						  =    feat_extract_activation
									a__ :Any						  =    list(__A						)
									a__ :Dict						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Any						  =    conv_bias
									a__ :List[str]						  =    num_conv_pos_embeddings
									a__ :str						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[Any]						  =    num_hidden_layers
									a__ :str						  =    intermediate_size
									a__ :Dict						  =    squeeze_factor
									a__ :List[Any]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Tuple						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :List[Any]						  =    activation_dropout
									a__ :str						  =    feat_proj_dropout
									a__ :Any						  =    final_dropout
									a__ :Dict						  =    layerdrop
									a__ :List[str]						  =    layer_norm_eps
									a__ :Tuple						  =    initializer_range
									a__ :Dict						  =    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
									a__ :int						  =    apply_spec_augment
									a__ :Optional[Any]						  =    mask_time_prob
									a__ :List[Any]						  =    mask_time_length
									a__ :Any						  =    mask_time_min_masks
									a__ :Any						  =    mask_feature_prob
									a__ :Dict						  =    mask_feature_length
									a__ :Union[str, Any]						  =    mask_feature_min_masks

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






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


