



"""simple docstring"""

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A_	:						List[Any]				=			get_logger(__name__)

# Datasets
A_	:						Any				=			'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
A_	:						List[Any]				=			'https://cdn-datasets.huggingface.co/datasets/datasets'
A_	:						Optional[int]				=			'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

# Metrics
A_	:						Optional[int]				=			'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
A_	:						Tuple				=			'https://cdn-datasets.huggingface.co/datasets/metric'
A_	:						List[Any]				=			'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

# Hub
A_	:						Dict				=			os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
A_	:						Optional[int]				=			HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
A_	:						Optional[int]				=			'main'

A_	:						List[str]				=			version.parse(platform.python_version())

if PY_VERSION < version.parse('3.8'):
							import importlib_metadata
else:
							import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A_	:						Union[str, Any]				=			{'1', 'ON', 'YES', 'TRUE'}
A_	:						Optional[int]				=			ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
A_	:						str				=			version.parse(importlib_metadata.version('dill'))
A_	:						int				=			version.parse(importlib_metadata.version('pandas'))
A_	:						int				=			version.parse(importlib_metadata.version('pyarrow'))

A_	:						List[Any]				=			os.environ.get('USE_TF', 'AUTO').upper()
A_	:						Union[str, Any]				=			os.environ.get('USE_TORCH', 'AUTO').upper()
A_	:						List[Any]				=			os.environ.get('USE_JAX', 'AUTO').upper()

A_	:						Tuple				=			'N/A'
A_	:						str				=			False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
							A_	:						List[Any]				=			importlib.util.find_spec('torch') is not None
							if TORCH_AVAILABLE:
														try:
																					A_	:						Optional[int]				=			version.parse(importlib_metadata.version('torch'))
																					logger.info(f'''PyTorch version {TORCH_VERSION} available.''')
														except importlib_metadata.PackageNotFoundError:
																					pass
else:
							logger.info('Disabling PyTorch because USE_TF is set')

A_	:						Union[str, Any]				=			'N/A'
A_	:						str				=			False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
							A_	:						Optional[Any]				=			importlib.util.find_spec('tensorflow') is not None
							if TF_AVAILABLE:
														# For the metadata, we have to look for both tensorflow and tensorflow-cpu
														for package in [
														    "tensorflow",
														    "tensorflow-cpu",
														    "tensorflow-gpu",
														    "tf-nightly",
														    "tf-nightly-cpu",
														    "tf-nightly-gpu",
														    "intel-tensorflow",
														    "tensorflow-rocm",
														    "tensorflow-macos",
														]:
																					try:
																												A_	:						str				=			version.parse(importlib_metadata.version(package))
																					except importlib_metadata.PackageNotFoundError:
																												continue
																					else:
																												break
														else:
																					A_	:						Any				=			False
							if TF_AVAILABLE:
														if TF_VERSION.major < 2:
																					logger.info(f'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
																					A_	:						Tuple				=			False
														else:
																					logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
							logger.info('Disabling Tensorflow because USE_TORCH is set')


A_	:						List[Any]				=			'N/A'
A_	:						str				=			False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
							A_	:						int				=			importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
							if JAX_AVAILABLE:
														try:
																					A_	:						Union[str, Any]				=			version.parse(importlib_metadata.version('jax'))
																					logger.info(f'''JAX version {JAX_VERSION} available.''')
														except importlib_metadata.PackageNotFoundError:
																					pass
else:
							logger.info('Disabling JAX because USE_JAX is set to False')


A_	:						List[Any]				=			os.environ.get('USE_BEAM', 'AUTO').upper()
A_	:						Optional[int]				=			'N/A'
A_	:						str				=			False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
							try:
														A_	:						List[Any]				=			version.parse(importlib_metadata.version('apache_beam'))
														A_	:						str				=			True
														logger.info(f'''Apache Beam version {BEAM_VERSION} available.''')
							except importlib_metadata.PackageNotFoundError:
														pass
else:
							logger.info('Disabling Apache Beam because USE_BEAM is set to False')


# Optional tools for data loading
A_	:						Any				=			importlib.util.find_spec('sqlalchemy') is not None

# Optional tools for feature decoding
A_	:						Any				=			importlib.util.find_spec('PIL') is not None
A_	:						Union[str, Any]				=			importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.0.31')
A_	:						Optional[int]				=			importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.1.0')

# Optional compression tools
A_	:						Any				=			importlib.util.find_spec('rarfile') is not None
A_	:						Dict				=			importlib.util.find_spec('zstandard') is not None
A_	:						str				=			importlib.util.find_spec('lz4') is not None
A_	:						Union[str, Any]				=			importlib.util.find_spec('py7zr') is not None


# Cache location
A_	:						Dict				=			'~/.cache'
A_	:						Union[str, Any]				=			os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
A_	:						int				=			os.path.join(XDG_CACHE_HOME, 'huggingface')
A_	:						Optional[Any]				=			os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))

A_	:						str				=			os.path.join(HF_CACHE_HOME, 'datasets')
A_	:						List[Any]				=			Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

A_	:						Optional[Any]				=			os.path.join(HF_CACHE_HOME, 'metrics')
A_	:						Union[str, Any]				=			Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))

A_	:						Union[str, Any]				=			os.path.join(HF_CACHE_HOME, 'modules')
A_	:						List[Any]				=			Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

A_	:						str				=			'downloads'
A_	:						Dict				=			os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A_	:						Tuple				=			Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

A_	:						Optional[Any]				=			'extracted'
A_	:						Union[str, Any]				=			os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A_	:						Optional[Any]				=			Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A_	:						Tuple				=			(
    os.environ.get('HF_UPDATE_DOWNLOAD_COUNTS', 'AUTO').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A_	:						str				=			1000

# Size of the preloaded record batch in `Dataset.__iter__`
A_	:						Tuple				=			10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A_	:						List[Any]				=			4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A_	:						Tuple				=			'500MB'

# Parquet configuration
A_	:						int				=			100
A_	:						List[str]				=			100
A_	:						Union[str, Any]				=			100

# Offline mode
A_	:						Dict				=			os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES

# In-memory
A_	:						Union[str, Any]				=			0  # Disabled
A_	:						Optional[int]				=			float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A_	:						Any				=			'dataset.arrow'
A_	:						str				=			'indices.arrow'
A_	:						Optional[Any]				=			'state.json'
A_	:						Any				=			'dataset_info.json'
A_	:						List[Any]				=			'dataset_infos.json'
A_	:						Tuple				=			'LICENSE'
A_	:						List[str]				=			'metric_info.json'
A_	:						Optional[int]				=			'dataset_dict.json'

A_	:						Optional[int]				=			'datasets_modules'

A_	:						int				=			255

# Streaming
A_	:						Dict				=			20
A_	:						Dict				=			5

# Datasets without script
A_	:						Tuple				=			200
A_	:						Dict				=			10
A_	:						str				=			200

# Progress bars
A_	:						Dict				=			0.05  # 20 progress updates per sec










"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


A_	:						Any				=			logging.get_logger(__name__)
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					*_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  None:





					"""simple docstring"""


					warnings.warn(
					    'The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use ConvNextImageProcessor instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
					super().__init__(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





