
'''simple docstring'''

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


UpperCAmelCase__ :			str											=		get_tests_dir("fixtures")






class  A  (						unittest.TestCase     ):





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				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=__magic_name__				) 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[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				WavaVecaFeatureExtractor.from_pretrained(
				    "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"				)






@is_staging_test
class  A  (						unittest.TestCase     ):





	@classmethod
	def 				__SCREAMING_SNAKE_CASE	(       cls      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				TOKEN
				HfFolder.save_token(__magic_name__				)





	@classmethod
	def 				__SCREAMING_SNAKE_CASE	(       cls      :							str				):


				"""simple docstring"""

				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      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				WavaVecaFeatureExtractor.from_pretrained(__magic_name__				)
				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(__magic_name__   ,				getattr(__magic_name__   ,				__magic_name__				)				)

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

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				lowerCAmelCase__										=				WavaVecaFeatureExtractor.from_pretrained(__magic_name__				)
				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(__magic_name__   ,				getattr(__magic_name__   ,				__magic_name__				)				)

				# 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(
							    __magic_name__   ,				repo_id="valid_org/test-feature-extractor-org"   ,				push_to_hub=__magic_name__   ,				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(__magic_name__   ,				getattr(__magic_name__   ,				__magic_name__				)				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				CustomFeatureExtractor.register_for_auto_class()
				lowerCAmelCase__										=				CustomFeatureExtractor.from_pretrained(__magic_name__				)

				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=__magic_name__				)
				# 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'''
def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Dict	  =      set()
 # edges = list of graph's edges
 __A	:		Any	  =      get_edges(UpperCamelCase__      )

 # While there are still elements in edges list, take an arbitrary edge
 # (from_node, to_node) and add his extremity to chosen_vertices and then
 # remove all arcs adjacent to the from_node and to_node
 while edges:
  __A  ,__A	:		List[Any]	  =      edges.pop()
  chosen_vertices.add(UpperCamelCase__      )
  chosen_vertices.add(UpperCamelCase__      )
  for edge in edges.copy():
   if from_node in edge or to_node in edge:
    edges.discard(UpperCamelCase__      )
 return chosen_vertices


def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Any	  =      set()
 for from_node, to_nodes in graph.items():
  for to_node in to_nodes:
   edges.add((from_node, to_node)      )
 return edges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")






