





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						Any:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,      split=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      keep_in_memory=lowerCAmelCase__		,      streaming=lowerCAmelCase__		,      num_proc=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        a__	:					List[str]										=path_or_paths if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else {self.split: path_or_paths}
        a__	:					Dict										=Text(
            cache_dir=lowerCAmelCase__		,      data_files=lowerCAmelCase__		,      features=lowerCAmelCase__		,      **lowerCAmelCase__		,      )







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        if self.streaming:
          a__	:					Tuple										=self.builder.as_streaming_dataset(split=self.split							)
        # Build regular (map-style) dataset
        else:
          a__	:					Union[str, Any]										=None
          a__	:					Optional[int]										=None
          a__	:					Union[str, Any]										=None
          a__	:					Optional[Any]										=None

          self.builder.download_and_prepare(
              download_config=lowerCAmelCase__		,      download_mode=lowerCAmelCase__		,      verification_mode=lowerCAmelCase__		,      base_path=lowerCAmelCase__		,      num_proc=self.num_proc		,      )
          a__	:					int										=self.builder.as_dataset(
              split=self.split		,      verification_mode=lowerCAmelCase__		,      in_memory=self.keep_in_memory							)
        return dataset













'''simple docstring'''







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase_					:    Any      	=			get_tests_dir('fixtures')
lowerCAmelCase_					:    Union[str, Any]      	=			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase_					:    Dict      	=			get_tests_dir('fixtures/dummy-config.json')
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 0




				def   UpperCamelCase__   (  self     :					str					):
								_a								= AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with tempfile.TemporaryDirectory() as tmpdirname:
												_a								= WavaVecaConfig()

												# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
												_a								= AutoFeatureExtractor.from_pretrained(__a					).to_dict()

												config_dict.pop("feature_extractor_type"					)
												_a								= WavaVecaFeatureExtractor(**__a					)

												# save in new folder
												model_config.save_pretrained(__a					)
												config.save_pretrained(__a					)

												_a								= AutoFeatureExtractor.from_pretrained(__a					)

												# make sure private variable is not incorrectly saved
												_a								= json.loads(config.to_json_string()					)
												self.assertTrue("_processor_class" not in dict_as_saved					)

								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								with self.assertRaisesRegex(
								    __a					,				"bert-base is not a local folder and is not a valid model identifier"					):
												_a								= AutoFeatureExtractor.from_pretrained("bert-base"					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								with self.assertRaisesRegex(
								    __a					,				r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"					):
												_a								= AutoFeatureExtractor.from_pretrained(__a					,				revision="aaaaaa"					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with self.assertRaisesRegex(
								    __a					,				"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."					,				):
												_a								= AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								# If remote code is not set, we will time out when asking whether to load the model.
								with self.assertRaises(__a					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(__a					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)

								_a								= AutoFeatureExtractor.from_pretrained(
								    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
								self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)

								# Test feature extractor can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
												feature_extractor.save_pretrained(__a					)
												_a								= AutoFeatureExtractor.from_pretrained(__a					,				trust_remote_code=__a					)
								self.assertEqual(reloaded_feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)




				def   UpperCamelCase__   (  self     :					Any					):
								try:
												AutoConfig.register("custom"					,				__a					)
												AutoFeatureExtractor.register(__a					,				__a					)
												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__a					):
																AutoFeatureExtractor.register(__a					,				__a					)

												# Now that the config is registered, it can be used as any other config with the auto-API
												_a								= CustomFeatureExtractor.from_pretrained(__a					)
												with tempfile.TemporaryDirectory() as tmp_dir:
																feature_extractor.save_pretrained(__a					)
																_a								= AutoFeatureExtractor.from_pretrained(__a					)
																self.assertIsInstance(__a					,				__a					)

								finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




				def   UpperCamelCase__   (  self     :					Tuple					):
								class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






												"""simple docstring"""







												__a            =True

								try:
												AutoConfig.register("custom"					,				__a					)
												AutoFeatureExtractor.register(__a					,				__a					)
												# If remote code is not set, the default is to use local
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(feature_extractor.is_local					)

												# If remote code is disabled, we load the local one.
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(feature_extractor.is_local					)

												# If remote is enabled, we load from the Hub
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(not hasattr(__a					,				"is_local"					)					)

								finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
