

'''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


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






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							Union[str, Any]:
											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=lowerCamelCase__       ) 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   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor.from_pretrained(
											    'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'       )






@is_staging_test
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				@classmethod
				def   a__					(    cls       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				TOKEN
											HfFolder.save_token(lowerCamelCase__       )



				@classmethod
				def   a__					(    cls       )     ->							Tuple:

											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   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__       )
											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(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       )

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



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__       )
											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(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       )

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





				def   a__					(    self       )     ->							str:
											CustomFeatureExtractor.register_for_auto_class()
											SCREAMING_SNAKE_CASE_				=				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'},     )

											SCREAMING_SNAKE_CASE_				=				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'       )













import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase    							=      namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Any							)     ->  Optional[int]:
   return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Dict					=					_TestCommandArgs(dataset=__lowerCAmelCase					,							all_configs=__lowerCAmelCase					,							save_infos=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					TestCommand(*__lowerCAmelCase							)
   test_command.run()
   __UpperCamelCase	:			Any					=					os.path.join(__lowerCAmelCase					,							"""README.md"""							)
   assert os.path.exists(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					DatasetInfosDict.from_directory(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DatasetInfosDict(
       {
           """default""": DatasetInfo(
               features=Features(
                   {
                       """tokens""": Sequence(Value("""string"""							)							),
                       """ner_tags""": Sequence(
                           ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
                       """langs""": Sequence(Value("""string"""							)							),
                       """spans""": Sequence(Value("""string"""							)							),
                   }							)					,							splits=[
                   {
                       """name""": """train""",
                       """num_bytes""": 2351563,
                       """num_examples""": 10000,
                   },
                   {
                       """name""": """validation""",
                       """num_bytes""": 238418,
                       """num_examples""": 1000,
                   },
               ]					,							download_size=3940680					,							dataset_size=2589981					,							)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					getattr(dataset_infos["""default"""]					,							__lowerCAmelCase							), getattr(expected_dataset_infos["""default"""]					,							__lowerCAmelCase							)
      if key == "num_bytes":
         assert is_apercent_close(__lowerCAmelCase					,							__lowerCAmelCase							)
      elif key == "splits":
         assert list(__lowerCAmelCase							) == list(__lowerCAmelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes					,							expected[split].num_bytes							)
      else:
         result == expected



