
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


_snake_case				:   List[str]     =				get_tests_dir("""fixtures""")


class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								# A mock response for an HTTP head request to emulate server down
								_SCREAMING_SNAKE_CASE				= mock.Mock()
								_SCREAMING_SNAKE_CASE				= 500
								_SCREAMING_SNAKE_CASE				= {}
								_SCREAMING_SNAKE_CASE				= HTTPError
								_SCREAMING_SNAKE_CASE				= {}

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




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


@is_staging_test
class 			lowerCAmelCase (				unittest.TestCase			):




		@classmethod
		def     lowercase       (				cls      ):
								_SCREAMING_SNAKE_CASE				= TOKEN
								HfFolder.save_token(UpperCamelCase      )




		@classmethod
		def     lowercase       (				cls      ):
								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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase      )
								feature_extractor.push_to_hub("test-feature-extractor"					,						use_auth_token=self._token      )

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

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

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase      )
								feature_extractor.push_to_hub("valid_org/test-feature-extractor"					,						use_auth_token=self._token      )

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

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

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




		def     lowercase       (				self      ):
								CustomFeatureExtractor.register_for_auto_class()
								_SCREAMING_SNAKE_CASE				= CustomFeatureExtractor.from_pretrained(UpperCamelCase      )

								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"}					,						)

								_SCREAMING_SNAKE_CASE				= AutoFeatureExtractor.from_pretrained(
								    F'{USER}/test-dynamic-feature-extractor'					,						trust_remote_code=UpperCamelCase      )
								# 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'''







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case				:   List[Any]     =				logging.get_logger(__name__)


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								warnings.warn(
								    "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
								    " use DPTImageProcessor instead."					,						UpperCamelCase					,						)
								super().__init__(*UpperCamelCase					,						**UpperCamelCase      )