

from __future__ import annotations

from collections.abc import Callable

__A			:					Optional[Any]						=			list[list[float | int]]





def 	__a     (      A__			:  Matrix			,  A__			:  Matrix       ):
    SCREAMING_SNAKE_CASE										=   len(A__       )
    SCREAMING_SNAKE_CASE										=   [[0 for _ in range(size + 1       )] for _ in range(A__       )]
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42

    for row in range(A__       ):
        for col in range(A__       ):
            SCREAMING_SNAKE_CASE										=   matrix[row][col]

        SCREAMING_SNAKE_CASE										=   vector[row][0]

    SCREAMING_SNAKE_CASE										=   0
    SCREAMING_SNAKE_CASE										=   0
    while row < size and col < size:
        # pivoting
        SCREAMING_SNAKE_CASE										=   max((abs(augmented[rowa][col]       ), rowa) for rowa in range(A__			,  A__       )       )[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   augmented[pivot_row], augmented[row]

        for rowa in range(row + 1			,  A__       ):
            SCREAMING_SNAKE_CASE										=   augmented[rowa][col] / augmented[row][col]
            SCREAMING_SNAKE_CASE										=   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			,  A__       ):
        for row in range(A__       ):
            SCREAMING_SNAKE_CASE										=   augmented[row][col] / augmented[col][col]
            for cola in range(A__			,  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(A__       )
    ]





def 	__a     (      A__			:  list[int]       ):
    SCREAMING_SNAKE_CASE										=   len(A__       )
    SCREAMING_SNAKE_CASE										=   [[0 for _ in range(A__       )] for _ in range(A__       )]
    SCREAMING_SNAKE_CASE										=   [[0] for _ in range(A__       )]
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42

    for x_val, y_val in enumerate(A__       ):
        for col in range(A__       ):
            SCREAMING_SNAKE_CASE										=   (x_val + 1) ** (size - col - 1)
        SCREAMING_SNAKE_CASE										=   y_val

    SCREAMING_SNAKE_CASE										=   solve(A__			,  A__       )

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

    return interpolated_func





def 	__a     (      A__			:  int       ):
    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )





def 	__a     (      A__			:  Callable[[int], int] = question_function			,  A__			:  int = 10       ):
    SCREAMING_SNAKE_CASE										=   [func(A__       ) for x_val in range(1			,  order + 1       )]

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

    SCREAMING_SNAKE_CASE										=   0
    SCREAMING_SNAKE_CASE										=   42
    SCREAMING_SNAKE_CASE										=   42

    for poly in polynomials:
        SCREAMING_SNAKE_CASE										=   1
        while func(A__       ) == poly(A__       ):
            x_val += 1

        ret += poly(A__       )

    return ret


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






"""simple docstring"""



import functools
import operator

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


a_					=     logging.get_logger(__name__)

a_					=     {
    """microsoft/unispeech-large-1500h-cv""": (
        """https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""unispeech"""

 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=0.0   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.0_2   ,		__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=False   ,		__lowerCamelCase=True   ,		__lowerCamelCase=0.0_5   ,		__lowerCamelCase=10   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=10   ,		__lowerCamelCase=0   ,		__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=80   ,		__lowerCamelCase=0   ,		__lowerCamelCase=1   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.5   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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

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

     # ctc loss
     __A       :		Tuple							=	ctc_loss_reduction
     __A       :		Optional[int]							=	ctc_zero_infinity

     # pretraining loss
     __A       :		str							=	replace_prob







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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




