


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A :	Dict												=     get_logger(__name__)

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

# Hub
A :	Union[str, Any]												=     os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
A :	Any												=     HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
A :	List[Any]												=     'main'

A :	Dict												=     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 :	int												=     {'1', 'ON', 'YES', 'TRUE'}
A :	str												=     ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
A :	List[str]												=     version.parse(importlib_metadata.version('dill'))
A :	str												=     version.parse(importlib_metadata.version('pandas'))
A :	List[Any]												=     version.parse(importlib_metadata.version('pyarrow'))

A :	Tuple												=     os.environ.get('USE_TF', 'AUTO').upper()
A :	Any												=     os.environ.get('USE_TORCH', 'AUTO').upper()
A :	Optional[int]												=     os.environ.get('USE_JAX', 'AUTO').upper()

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

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
	A :	Dict												=     importlib.util.find_spec('torch') is not None
	if TORCH_AVAILABLE:
		try:
			A :	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 :	int												=     'N/A'
A :	Optional[int]												=     False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
	A :	List[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 :	Tuple												=     version.parse(importlib_metadata.version(package))
			except importlib_metadata.PackageNotFoundError:
				continue
			else:
				break
		else:
			A :	Optional[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 :	Optional[Any]												=     'N/A'
A :	str												=     False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
	A :	List[Any]												=     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 :	List[Any]												=     'N/A'
A :	Tuple												=     False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
	try:
		A :	int												=     version.parse(importlib_metadata.version('apache_beam'))
		A :	List[Any]												=     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 :	List[Any]												=     importlib.util.find_spec('sqlalchemy') is not None

# Optional tools for feature decoding
A :	Optional[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 :	List[Any]												=     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 :	str												=     importlib.util.find_spec('rarfile') is not None
A :	List[str]												=     importlib.util.find_spec('zstandard') is not None
A :	int												=     importlib.util.find_spec('lz4') is not None
A :	Tuple												=     importlib.util.find_spec('py7zr') is not None


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

A :	Dict												=     os.path.join(HF_CACHE_HOME, 'datasets')
A :	Any												=     Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

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

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

A :	Tuple												=     'downloads'
A :	int												=     os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A :	Any												=     Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

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

# Download count for the website
A :	Any												=     (
    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 :	int												=     1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A :	List[Any]												=     1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A :	Tuple												=     4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A :	Optional[int]												=     '500MB'

# Parquet configuration
A :	int												=     1_0_0
A :	List[str]												=     1_0_0
A :	List[Any]												=     1_0_0

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

# In-memory
A :	Any												=     0  # Disabled
A :	str												=     float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A :	List[str]												=     'dataset.arrow'
A :	List[str]												=     'indices.arrow'
A :	Dict												=     'state.json'
A :	Union[str, Any]												=     'dataset_info.json'
A :	List[str]												=     'dataset_infos.json'
A :	Dict												=     'LICENSE'
A :	List[Any]												=     'metric_info.json'
A :	int												=     'dataset_dict.json'

A :	Any												=     'datasets_modules'

A :	Optional[int]												=     2_5_5

# Streaming
A :	List[Any]												=     2_0
A :	List[str]												=     5

# Datasets without script
A :	Dict												=     2_0_0
A :	Dict												=     1_0
A :	List[Any]												=     2_0_0

# Progress bars
A :	int												=     0.05  # 20 progress updates per sec





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A :	int												=     {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	List[Any]												=     [
	    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RwkvForCausalLM',
	    'RwkvModel',
	    'RwkvPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rwkv import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		    RwkvPreTrainedModel,
		)
else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

