

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__a						:Any            = get_logger(__name__)

# Datasets
__a						:Tuple            = 'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
__a						:Dict            = 'https://cdn-datasets.huggingface.co/datasets/datasets'
__a						:Any            = 'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

# Metrics
__a						:str            = 'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
__a						:int            = 'https://cdn-datasets.huggingface.co/datasets/metric'
__a						:Union[str, Any]            = 'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

# Hub
__a						:Dict            = os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
__a						:Union[str, Any]            = HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
__a						:Optional[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						:Optional[Any]            = {'1', 'ON', 'YES', 'TRUE'}
__a						:Any            = ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
__a						:int            = version.parse(importlib_metadata.version('dill'))
__a						:List[Any]            = version.parse(importlib_metadata.version('pandas'))
__a						:Optional[Any]            = version.parse(importlib_metadata.version('pyarrow'))

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

__a						:Optional[Any]            = 'N/A'
__a						:Dict            = False

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

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
      __a						:Union[str, 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						:int            = 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						:str            = False
            else:
                  logger.info(F"TensorFlow version {TF_VERSION} available.")
else:
      logger.info('Disabling Tensorflow because USE_TORCH is set')


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

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
      __a						:str            = importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
      if JAX_AVAILABLE:
            try:
                  __a						:Tuple            = 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						:Dict            = os.environ.get('USE_BEAM', 'AUTO').upper()
__a						:Any            = 'N/A'
__a						:Union[str, Any]            = False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
      try:
            __a						:Optional[Any]            = version.parse(importlib_metadata.version('apache_beam'))
            __a						: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						:Tuple            = importlib.util.find_spec('sqlalchemy') is not None

# Optional tools for feature decoding
__a						:str            = importlib.util.find_spec('PIL') is not None
__a						:Optional[Any]            = importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.0.31')
__a						:Tuple            = 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						:List[str]            = importlib.util.find_spec('rarfile') is not None
__a						:str            = importlib.util.find_spec('zstandard') is not None
__a						:Tuple            = importlib.util.find_spec('lz4') is not None
__a						:Dict            = importlib.util.find_spec('py7zr') is not None


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

__a						:Optional[int]            = 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						: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						:Optional[Any]            = Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

__a						:Union[str, Any]            = 'downloads'
__a						:List[Any]            = os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
__a						:Any            = Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

__a						:Dict            = 'extracted'
__a						: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						:Optional[int]            = (
    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						:Union[str, Any]            = 1000

# Size of the preloaded record batch in `Dataset.__iter__`
__a						:Optional[Any]            = 10

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

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

# Parquet configuration
__a						:Dict            = 100
__a						:Union[str, Any]            = 100
__a						:List[str]            = 100

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

# In-memory
__a						:Dict            = 0  # Disabled
__a						:Optional[int]            = float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

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

__a						:Optional[int]            = 'datasets_modules'

__a						:List[Any]            = 255

# Streaming
__a						:Tuple            = 20
__a						:List[Any]            = 5

# Datasets without script
__a						:Tuple            = 200
__a						:Tuple            = 10
__a						:Dict            = 200

# Progress bars
__a						:Optional[int]            = 0.05  # 20 progress updates per sec


import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"							,[
        (Version("1.0.0"						), True),
        ("1.0.0", True),
        (Version("2.0.0"						), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]							,)
def 			__snake_case	(   __UpperCamelCase  :   Any							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							Version("1.0.0"						)
      assert (version == other) is expected_equality
      assert (version != other) is not expected_equality
      assert (hash(__UpperCamelCase						) == hash(__UpperCamelCase						)) is expected_equality