



import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__UpperCAmelCase			  =			random.Random()
def 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       str=1.0			,	snake_case__			:       int=None			,	snake_case__			:       Union[str, Any]=None      )      ->	Any:
   if rng is None:
      UpperCamelCase       :    int		      =      global_rng

   UpperCamelCase       :    Union[str, Any]		      =      []
   for batch_idx in range(shape[0]      ):
      values.append([]      )
      for _ in range(shape[1]      ):
         values[-1].append(rng.random() * scale      )

   return values



class 							lowerCAmelCase_						(    unittest.TestCase				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=7,     SCREAMING_SNAKE_CASE_=400,     SCREAMING_SNAKE_CASE_=2000,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=1_6000,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     )	->						List[str]:
          UpperCamelCase       :    Dict		      =      parent
          UpperCamelCase       :    Dict		      =      batch_size
          UpperCamelCase       :    Any		      =      min_seq_length
          UpperCamelCase       :    Optional[int]		      =      max_seq_length
          UpperCamelCase       :    Optional[int]		      =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          UpperCamelCase       :    Tuple		      =      feature_size
          UpperCamelCase       :    Any		      =      padding_value
          UpperCamelCase       :    Tuple		      =      sampling_rate
          UpperCamelCase       :    Optional[Any]		      =      return_attention_mask
          UpperCamelCase       :    Optional[Any]		      =      do_normalize
       def    snake_case_			(     self			)	->						Union[str, Any]:
          return {
              "feature_size": self.feature_size,
              "padding_value": self.padding_value,
              "sampling_rate": self.sampling_rate,
              "return_attention_mask": self.return_attention_mask,
              "do_normalize": self.do_normalize,
          }






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=False			)	->						Union[str, Any]:
          def _flatten(SCREAMING_SNAKE_CASE_			):
             return list(itertools.chain(*SCREAMING_SNAKE_CASE_			)			)

          if equal_length:
             UpperCamelCase       :    List[str]		      =      floats_list((self.batch_size, self.max_seq_length)			)
          else:
             # make sure that inputs increase in size
             UpperCamelCase       :    Union[str, Any]		      =      [
                 _flatten(floats_list((x, self.feature_size)			)			)
                 for x in range(self.min_seq_length,     self.max_seq_length,     self.seq_length_diff			)
             ]

          if numpify:
             UpperCamelCase       :    str		      =      [np.asarray(SCREAMING_SNAKE_CASE_			) for x in speech_inputs]

          return speech_inputs






class 							lowerCAmelCase_						(    a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   Any  	=    WavaVecaFeatureExtractor
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Tuple		      =      WavaVecaFeatureExtractionTester(self			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE_,     axis=0			) < 1e-3			)			)
          self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE_,     axis=0			) - 1			) < 1e-3			)			)
       def    snake_case_			(     self			)	->						Optional[int]:
          # Tests that all call wrap to encode_plus and batch_encode_plus
          UpperCamelCase       :    Tuple		      =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          # create three inputs of length 800, 1000, and 1200
          UpperCamelCase       :    Any		      =      [floats_list((1, x)			)[0] for x in range(800,     1400,     200			)]
          UpperCamelCase       :    Dict		      =      [np.asarray(SCREAMING_SNAKE_CASE_			) for speech_input in speech_inputs]

          # Test not batched input
          UpperCamelCase       :    List[Any]		      =      feat_extract(speech_inputs[0],     return_tensors='np'			).input_values
          UpperCamelCase       :    Union[str, Any]		      =      feat_extract(np_speech_inputs[0],     return_tensors='np'			).input_values
          self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     atol=1e-3			)			)

          # Test batched
          UpperCamelCase       :    List[Any]		      =      feat_extract(SCREAMING_SNAKE_CASE_,     return_tensors='np'			).input_values
          UpperCamelCase       :    int		      =      feat_extract(SCREAMING_SNAKE_CASE_,     return_tensors='np'			).input_values
          for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     atol=1e-3			)			)

          # Test 2-D numpy arrays are batched.
          UpperCamelCase       :    Tuple		      =      [floats_list((1, x)			)[0] for x in (800, 800, 800)]
          UpperCamelCase       :    Optional[int]		      =      np.asarray(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, Any]		      =      feat_extract(SCREAMING_SNAKE_CASE_,     return_tensors='np'			).input_values
          UpperCamelCase       :    Dict		      =      feat_extract(SCREAMING_SNAKE_CASE_,     return_tensors='np'			).input_values
          for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     atol=1e-3			)			)
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    Dict		      =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          UpperCamelCase       :    Dict		      =      [floats_list((1, x)			)[0] for x in range(800,     1400,     200			)]

          UpperCamelCase       :    str		      =      ['longest', 'max_length', 'do_not_pad']
          UpperCamelCase       :    Any		      =      [None, 1600, None]
          for max_length, padding in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Optional[Any]		      =      feat_extract(SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_,     max_length=SCREAMING_SNAKE_CASE_,     return_tensors='np'			)
             UpperCamelCase       :    Tuple		      =      processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]			)
             self.assertTrue(input_values[0][800:].sum() < 1e-6			)
             self._check_zero_mean_unit_variance(input_values[1][:1000]			)
             self.assertTrue(input_values[0][1000:].sum() < 1e-6			)
             self._check_zero_mean_unit_variance(input_values[2][:1200]			)
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    List[str]		      =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          UpperCamelCase       :    Tuple		      =      range(800,     1400,     200			)
          UpperCamelCase       :    str		      =      [floats_list((1, x)			)[0] for x in lengths]

          UpperCamelCase       :    int		      =      ['longest', 'max_length', 'do_not_pad']
          UpperCamelCase       :    List[str]		      =      [None, 1600, None]

          for max_length, padding in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Tuple		      =      feat_extract(SCREAMING_SNAKE_CASE_,     max_length=SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Optional[int]		      =      processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]			)
             self._check_zero_mean_unit_variance(input_values[1][:1000]			)
             self._check_zero_mean_unit_variance(input_values[2][:1200]			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Optional[int]		      =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          UpperCamelCase       :    Optional[int]		      =      [floats_list((1, x)			)[0] for x in range(800,     1400,     200			)]
          UpperCamelCase       :    int		      =      feat_extract(
              SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     max_length=1000,     padding='max_length',     return_tensors='np'			)
          UpperCamelCase       :    Tuple		      =      processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]			)
          self._check_zero_mean_unit_variance(input_values[1]			)
          self._check_zero_mean_unit_variance(input_values[2]			)
       def    snake_case_			(     self			)	->						List[Any]:
          UpperCamelCase       :    List[Any]		      =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          UpperCamelCase       :    Union[str, Any]		      =      [floats_list((1, x)			)[0] for x in range(800,     1400,     200			)]
          UpperCamelCase       :    Any		      =      feat_extract(
              SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     max_length=1000,     padding='longest',     return_tensors='np'			)
          UpperCamelCase       :    Dict		      =      processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]			)
          self._check_zero_mean_unit_variance(input_values[1, :1000]			)
          self._check_zero_mean_unit_variance(input_values[2]			)

          # make sure that if max_length < longest -> then pad to max_length
          self.assertTrue(input_values.shape == (3, 1000)			)

          UpperCamelCase       :    str		      =      [floats_list((1, x)			)[0] for x in range(800,     1400,     200			)]
          UpperCamelCase       :    Any		      =      feat_extract(
              SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     max_length=2000,     padding='longest',     return_tensors='np'			)
          UpperCamelCase       :    int		      =      processed.input_values

          self._check_zero_mean_unit_variance(input_values[0, :800]			)
          self._check_zero_mean_unit_variance(input_values[1, :1000]			)
          self._check_zero_mean_unit_variance(input_values[2]			)

          # make sure that if max_length > longest -> then pad to longest
          self.assertTrue(input_values.shape == (3, 1200)			)
       @require_torch
       def    snake_case_			(     self			)	->						str:
          import torch

          UpperCamelCase       :    Any		      =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
          UpperCamelCase       :    Dict		      =      np.random.rand(100			).astype(np.floataa			)
          UpperCamelCase       :    Dict		      =      np_speech_inputs.tolist()

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






       @slow
       @require_torch
       def    snake_case_			(     self			)	->						Tuple:
          # this test makes sure that models that are using
          # group norm don't have their feature extractor return the
          # attention_mask
          for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
             UpperCamelCase       :    int		      =      WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Dict		      =      WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)

             # only "layer" feature extraction norm should make use of
             # attention_mask
             self.assertEqual(feat_extract.return_attention_mask,     config.feat_extract_norm == 'layer'			)





"""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() = }')



