
"""simple docstring"""


import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import WhisperFeatureExtractor

if is_torch_available():
		import torch

_lowercase				 =					random.Random()






def    _snake_case					(		snake_case__ :  int						,   snake_case__ :  Union[str, Any]=1.0						,   snake_case__ :  Any=None						,   snake_case__ :  Optional[Any]=None  ):




	if rng is None:
		A     = global_rng

	A     = []
	for batch_idx in range(shape[0]  ):
		values.append([]  )
		for _ in range(shape[1]  ):
			values[-1].append(rng.random() * scale  )

	return values







@require_torch
@require_torchaudio
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def __init__(			self  :   List[str]	,A_  :   Any	,A_  :   Optional[Any]=7	,A_  :   Tuple=400	,A_  :   Dict=2000	,A_  :   str=10	,A_  :   List[Any]=160	,A_  :   Dict=8	,A_  :   List[Any]=0.0	,A_  :   Optional[int]=4000	,A_  :   Dict=False	,A_  :   Optional[Any]=True	,)  -> Optional[int]:
							A     = parent
							A     = batch_size
							A     = min_seq_length
							A     = max_seq_length
							A     = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A     = padding_value
							A     = sampling_rate
							A     = return_attention_mask
							A     = do_normalize
							A     = feature_size
							A     = chunk_length
							A     = hop_length


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Dict:
							return {
							    "feature_size": self.feature_size,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   List[str]=False	,A_  :   Dict=False   )  -> Optional[Any]:
							def _flatten(A_  :   Dict   ):
								return list(itertools.chain(*lowercase_   )   )

							if equal_length:
								A     = [floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
							else:
								# make sure that inputs increase in size
								A     = [
								    floats_list((x, self.feature_size)   )
								    for x in range(self.min_seq_length	,self.max_seq_length	,self.seq_length_diff   )
								]
							if numpify:
								A     = [np.asarray(lowercase_   ) for x in speech_inputs]
							return speech_inputs







@require_torch
@require_torchaudio
class 					lowerCAmelCase_					(   SCREAMING_SNAKE_CASE_		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[Any]   =     WhisperFeatureExtractor if is_speech_available() else None


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							A     = WhisperFeatureExtractionTester(self   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> List[str]:
							A     = self.feature_extraction_class(**self.feat_extract_dict   )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A     = feat_extract_first.save_pretrained(lowercase_   )[0]
								check_json_file_has_correct_format(lowercase_   )
								A     = self.feature_extraction_class.from_pretrained(lowercase_   )

							A     = feat_extract_first.to_dict()
							A     = feat_extract_second.to_dict()
							A     = feat_extract_first.mel_filters
							A     = feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowercase_	,lowercase_   )   )
							self.assertEqual(lowercase_	,lowercase_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> List[str]:
							A     = self.feature_extraction_class(**self.feat_extract_dict   )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A     = os.path.join(lowercase_	,'feat_extract.json'   )
								feat_extract_first.to_json_file(lowercase_   )
								A     = self.feature_extraction_class.from_json_file(lowercase_   )

							A     = feat_extract_first.to_dict()
							A     = feat_extract_second.to_dict()
							A     = feat_extract_first.mel_filters
							A     = feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowercase_	,lowercase_   )   )
							self.assertEqual(lowercase_	,lowercase_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Tuple:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							A     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							# create three inputs of length 800, 1000, and 1200
							A     = [floats_list((1, x)   )[0] for x in range(800	,1400	,200   )]
							A     = [np.asarray(lowercase_   ) for speech_input in speech_inputs]

							# Test feature size
							A     = feature_extractor(lowercase_	,padding='max_length'	,return_tensors='np'   ).input_features
							self.assertTrue(input_features.ndim == 3   )
							self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames   )
							self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size   )

							# Test not batched input
							A     = feature_extractor(speech_inputs[0]	,return_tensors='np'   ).input_features
							A     = feature_extractor(np_speech_inputs[0]	,return_tensors='np'   ).input_features
							self.assertTrue(np.allclose(lowercase_	,lowercase_	,atol=1e-3   )   )

							# Test batched
							A     = feature_extractor(lowercase_	,return_tensors='np'   ).input_features
							A     = feature_extractor(lowercase_	,return_tensors='np'   ).input_features
							for enc_seq_a, enc_seq_a in zip(lowercase_	,lowercase_   ):
								self.assertTrue(np.allclose(lowercase_	,lowercase_	,atol=1e-3   )   )

							# Test 2-D numpy arrays are batched.
							A     = [floats_list((1, x)   )[0] for x in (800, 800, 800)]
							A     = np.asarray(lowercase_   )
							A     = feature_extractor(lowercase_	,return_tensors='np'   ).input_features
							A     = feature_extractor(lowercase_	,return_tensors='np'   ).input_features
							for enc_seq_a, enc_seq_a in zip(lowercase_	,lowercase_   ):
								self.assertTrue(np.allclose(lowercase_	,lowercase_	,atol=1e-3   )   )

							# Test truncation required
							A     = [floats_list((1, x)   )[0] for x in range(200	,(feature_extractor.n_samples + 500)	,200   )]
							A     = [np.asarray(lowercase_   ) for speech_input in speech_inputs]

							A     = [x[: feature_extractor.n_samples] for x in speech_inputs]
							A     = [np.asarray(lowercase_   ) for speech_input in speech_inputs_truncated]

							A     = feature_extractor(lowercase_	,return_tensors='np'   ).input_features
							A     = feature_extractor(lowercase_	,return_tensors='np'   ).input_features
							for enc_seq_a, enc_seq_a in zip(lowercase_	,lowercase_   ):
								self.assertTrue(np.allclose(lowercase_	,lowercase_	,atol=1e-3   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[Any]:
							import torch

							A     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							A     = np.random.rand(100	,32   ).astype(np.floataa   )
							A     = np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
								A     = feature_extractor.pad([{'input_features': inputs}]	,return_tensors='np'   )
								self.assertTrue(np_processed.input_features.dtype == np.floataa   )
								A     = feature_extractor.pad([{'input_features': inputs}]	,return_tensors='pt'   )
								self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Dict   )  -> Optional[Any]:
							A     = load_dataset('hf-internal-testing/librispeech_asr_dummy'	,'clean'	,split='validation'   )
							# automatic decoding with librispeech
							A     = ds.sort('id'   ).select(range(lowercase_   )   )[:num_samples]['audio']

							return [x["array"] for x in speech_samples]


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Dict:
							# fmt: off
							A     = torch.tensor(
							    [
							        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
							        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
							        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
							        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
							    ]   )
							# fmt: on

							A     = self._load_datasamples(1   )
							A     = WhisperFeatureExtractor()
							A     = feature_extractor(lowercase_	,return_tensors='pt'   ).input_features
							self.assertEqual(input_features.shape	,(1, 80, 3000)   )
							self.assertTrue(torch.allclose(input_features[0, 0, :30]	,lowercase_	,atol=1e-4   )   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[Any]:
							A     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							A     = self._load_datasamples(1   )[0]
							A     = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
							A     = feat_extract.zero_mean_unit_var_norm([audio]	,attention_mask=lowercase_   )[0]

							self.assertTrue(np.all(np.mean(lowercase_   ) < 1e-3   )   )
							self.assertTrue(np.all(np.abs(np.var(lowercase_   ) - 1   ) < 1e-3   )   )

"""simple docstring"""

from __future__ import annotations

from collections.abc import Callable

SCREAMING_SNAKE_CASE_            =    list[list[float | int]]




def      lowercase__    (       lowerCAmelCase					:       Matrix			,					lowerCAmelCase					:       Matrix			)			->   Matrix:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(size + 1			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for row in range(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       matrix[row][col]

        UpperCAmelCase          =       vector[row][0]

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

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




def      lowercase__    (       lowerCAmelCase					:       list[int]			)			->   Callable[[int], int]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(lowerCAmelCase			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       [[0] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

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

    UpperCAmelCase          =       solve(lowerCAmelCase			,					lowerCAmelCase			)

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

    return interpolated_func




def      lowercase__    (       lowerCAmelCase					:       int			)			->   int:






    """simple docstring"""




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




def      lowercase__    (       lowerCAmelCase					:       Callable[[int], int] = question_function			,					lowerCAmelCase					:       int = 10			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       [func(lowerCAmelCase			) for x_val in range(1			,					order + 1			)]

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

    UpperCAmelCase          =       0
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for poly in polynomials:
        UpperCAmelCase          =       1
        while func(lowerCAmelCase			) == poly(lowerCAmelCase			):
            x_val += 1

        ret += poly(lowerCAmelCase			)

    return ret


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



