

from __future__ import annotations

from collections.abc import Callable

lowerCAmelCase__       = list[list[float | int]]
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  len(__A			)
  UpperCAmelCase					      =  [[0 for _ in range(size + 1			)] for _ in range(__A			)]
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42

  for row in range(__A			):
    for col in range(__A			):
      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(__A   ,       __A			)			)[
        1
    ]
    if augmented[pivot_row][col] == 0:
      col += 1
      continue
    else:
      UpperCAmelCase      ,	UpperCAmelCase					      =  augmented[pivot_row], augmented[row]

    for rowa in range(row + 1   ,       __A			):
      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   ,       __A			):
    for row in range(__A			):
      UpperCAmelCase					      =  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      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  len(__A			)
  UpperCAmelCase					      =  [[0 for _ in range(__A			)] for _ in range(__A			)]
  UpperCAmelCase					      =  [[0] for _ in range(__A			)]
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42
  UpperCAmelCase					      =  42

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

  UpperCAmelCase					      =  solve(__A   ,       __A			)

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

  return interpolated_func
def      _lowerCAmelCase(	__A			):
  return (
      1
      - variable
      + variable**2
      - variable**3
      + variable**4
      - variable**5
      + variable**6
      - variable**7
      + variable**8
      - variable**9
      + variable**10
  )
def      _lowerCAmelCase(	__A = question_function   ,       __A = 10			):
  UpperCAmelCase					      =  [func(__A			) 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(__A			) == poly(__A			):
      x_val += 1

    ret += poly(__A			)

  return ret


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






import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase__       = get_tests_dir("fixtures")






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: Tuple    )    ->      Union[str, Any]:
   # A mock response for an HTTP head request to emulate server down
   UpperCAmelCase					      =  mock.Mock()
   UpperCAmelCase					      =  5_0_0
   UpperCAmelCase					      =  {}
   UpperCAmelCase					      =  HTTPError
   UpperCAmelCase					      =  {}

   # Download this model to make sure it's in the cache.
   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"    )
   # Under the mock environment we get a 500 error when trying to reach the model.
   with mock.patch("requests.Session.request"			,						return_value=lowerCAmelCase__    ) as mock_head:
     UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2"    )
     # This check we did call the fake head request
     mock_head.assert_called()


 def _UpperCamelCase	(						self		: List[Any]    )    ->      Dict:
   # This test is for deprecated behavior and can be removed in v5
   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(
       "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"    )






@is_staging_test
class 						__magic_name__    (       unittest.TestCase    ):


 @classmethod
 def _UpperCamelCase	(						cls		: List[str]    )    ->      List[Any]:
   UpperCAmelCase					      =  TOKEN
   HfFolder.save_token(lowerCAmelCase__    )


 @classmethod
 def _UpperCamelCase	(						cls		: Optional[int]    )    ->      Union[str, Any]:
   try:
     delete_repo(token=cls._token			,						repo_id="test-feature-extractor"    )
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token			,						repo_id="valid_org/test-feature-extractor-org"    )
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token			,						repo_id="test-dynamic-feature-extractor"    )
   except HTTPError:
     pass


 def _UpperCamelCase	(						self		: Any    )    ->      Any:
   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__    )
   feature_extractor.push_to_hub("test-feature-extractor"			,						use_auth_token=self._token    )

   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"    )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(lowerCAmelCase__			,						getattr(lowerCAmelCase__			,						lowerCAmelCase__    )    )

   # Reset repo
   delete_repo(token=self._token			,						repo_id="test-feature-extractor"    )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(
         lowerCAmelCase__			,						repo_id="test-feature-extractor"			,						push_to_hub=lowerCAmelCase__			,						use_auth_token=self._token    )

   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor"    )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(lowerCAmelCase__			,						getattr(lowerCAmelCase__			,						lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: List[Any]    )    ->      Tuple:
   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__    )
   feature_extractor.push_to_hub("valid_org/test-feature-extractor"			,						use_auth_token=self._token    )

   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor"    )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(lowerCAmelCase__			,						getattr(lowerCAmelCase__			,						lowerCAmelCase__    )    )

   # Reset repo
   delete_repo(token=self._token			,						repo_id="valid_org/test-feature-extractor"    )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(
         lowerCAmelCase__			,						repo_id="valid_org/test-feature-extractor-org"			,						push_to_hub=lowerCAmelCase__			,						use_auth_token=self._token    )

   UpperCAmelCase					      =  WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org"    )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(lowerCAmelCase__			,						getattr(lowerCAmelCase__			,						lowerCAmelCase__    )    )


 def _UpperCamelCase	(						self		: Dict    )    ->      List[str]:
   CustomFeatureExtractor.register_for_auto_class()
   UpperCAmelCase					      =  CustomFeatureExtractor.from_pretrained(lowerCAmelCase__    )

   feature_extractor.push_to_hub("test-dynamic-feature-extractor"			,						use_auth_token=self._token    )

   # This has added the proper auto_map field to the config
   self.assertDictEqual(
       feature_extractor.auto_map			,						{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}			,						)

   UpperCAmelCase					      =  AutoFeatureExtractor.from_pretrained(
       f"{USER}/test-dynamic-feature-extractor"			,						trust_remote_code=lowerCAmelCase__    )
   # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
   self.assertEqual(new_feature_extractor.__class__.__name__			,						"CustomFeatureExtractor"    )



