
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


__A				=get_tests_dir('''fixtures''')




class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					# A mock response for an HTTP head request to emulate server down
					lowerCamelCase_						=			mock.Mock()
					lowerCamelCase_						=			500
					lowerCamelCase_						=			{}
					lowerCamelCase_						=			HTTPError
					lowerCamelCase_						=			{}

					# Download this model to make sure it's in the cache.
					lowerCamelCase_						=			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=lowercase ) as mock_head:
									lowerCamelCase_						=			WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
									# This check we did call the fake head request
									mock_head.assert_called()



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




@is_staging_test
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@classmethod
	def     SCREAMING_SNAKE_CASE_(    cls )				->       Optional[Any]:
					lowerCamelCase_						=			TOKEN
					HfFolder.save_token(lowercase )



	@classmethod
	def     SCREAMING_SNAKE_CASE_(    cls )				->       List[str]:
					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     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			WavaVecaFeatureExtractor.from_pretrained(lowercase )
					feature_extractor.push_to_hub("test-feature-extractor"						,  use_auth_token=self._token )

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

					# 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(
									    lowercase						,  repo_id="test-feature-extractor"						,  push_to_hub=lowercase						,  use_auth_token=self._token )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			WavaVecaFeatureExtractor.from_pretrained(lowercase )
					feature_extractor.push_to_hub("valid_org/test-feature-extractor"						,  use_auth_token=self._token )

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

					# 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(
									    lowercase						,  repo_id="valid_org/test-feature-extractor-org"						,  push_to_hub=lowercase						,  use_auth_token=self._token )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					CustomFeatureExtractor.register_for_auto_class()
					lowerCamelCase_						=			CustomFeatureExtractor.from_pretrained(lowercase )

					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"}						,  )

					lowerCamelCase_						=			AutoFeatureExtractor.from_pretrained(
					    f'{USER}/test-dynamic-feature-extractor'						,  trust_remote_code=lowercase )
					# 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" )






"""simple docstring"""







from collections import defaultdict
from math import ceil, sqrt




def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000  ,						lowerCAmelCase_ = 10   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				defaultdict(lowerCAmelCase_   )

					for outer_width in range(3  ,						(t_limit // 4) + 2   ):
										if outer_width * outer_width > t_limit:
															__SCREAMING_SNAKE_CASE						   =				max(
															    ceil(sqrt(outer_width * outer_width - t_limit   )   )  ,						1   )
										else:
															__SCREAMING_SNAKE_CASE						   =				1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(lowerCAmelCase_  ,						outer_width - 1  ,						2   ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10   )


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