
"""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   :      Optional[Any]       =				"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
_a   :      Union[str, Any]       =				"""https://cdn-datasets.huggingface.co/datasets/datasets"""
_a   :      List[str]       =				"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

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

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


# Imports
_a   :      str       =				version.parse(importlib_metadata.version("""dill"""))
_a   :      Tuple       =				version.parse(importlib_metadata.version("""pandas"""))
_a   :      int       =				version.parse(importlib_metadata.version("""pyarrow"""))

_a   :      Tuple       =				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   :      int       =				"""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[str]       =				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   :      str       =				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   :      Optional[int]       =				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   :      List[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   :      List[Any]       =				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   :      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   :      int       =				os.environ.get("""USE_BEAM""", """AUTO""").upper()
_a   :      str       =				"""N/A"""
_a   :      List[str]       =				False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
    try:
        _a   :      List[Any]       =				version.parse(importlib_metadata.version("""apache_beam"""))
        _a   :      int       =				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   :      Union[str, Any]       =				importlib.util.find_spec("""sqlalchemy""") is not None

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


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

_a   :      Optional[Any]       =				os.path.join(HF_CACHE_HOME, """datasets""")
_a   :      Tuple       =				Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

_a   :      List[Any]       =				os.path.join(HF_CACHE_HOME, """metrics""")
_a   :      Union[str, Any]       =				Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

_a   :      Optional[int]       =				os.path.join(HF_CACHE_HOME, """modules""")
_a   :      Dict       =				Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

_a   :      Optional[int]       =				"""downloads"""
_a   :      List[Any]       =				os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_a   :      List[str]       =				Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

_a   :      Tuple       =				"""extracted"""
_a   :      List[str]       =				os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_a   :      Union[str, Any]       =				Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_a   :      Optional[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   :      Optional[int]       =				1_000

# Size of the preloaded record batch in `Dataset.__iter__`
_a   :      Dict       =				10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
_a   :      List[str]       =				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   :      Optional[Any]       =				100
_a   :      Any       =				100
_a   :      Tuple       =				100

# Offline mode
_a   :      Union[str, Any]       =				os.environ.get("""HF_DATASETS_OFFLINE""", """AUTO""").upper() in ENV_VARS_TRUE_VALUES

# In-memory
_a   :      str       =				0  # Disabled
_a   :      str       =				float(os.environ.get("""HF_DATASETS_IN_MEMORY_MAX_SIZE""", DEFAULT_IN_MEMORY_MAX_SIZE))

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

_a   :      str       =				"""datasets_modules"""

_a   :      List[Any]       =				255

# Streaming
_a   :      Any       =				20
_a   :      Union[str, Any]       =				5

# Datasets without script
_a   :      Union[str, Any]       =				200
_a   :      List[str]       =				10
_a   :      Dict       =				200

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





"""simple docstring"""







class 		_A    :







    """simple docstring"""




    def __init__(   self							:       Any):
      a		:						Tuple 				=     [0]
      a		:						str 				=     0






    def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       int):
      a		:						Any 				=     self.__heap[i]
      while i // 2 > 0:
        if self.__heap[i] > self.__heap[i // 2]:
          a		:						Tuple 				=     self.__heap[i // 2]
          a		:						Any 				=     temporary
        i //= 2






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       int):
      self.__heap.append(__UpperCAmelCase)
      self.__size += 1
      self.__swap_up(self.__size)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       int):
      while self.__size >= 2 * i:
        if 2 * i + 1 > self.__size:
          a		:						List[Any] 				=     2 * i
        else:
          if self.__heap[2 * i] > self.__heap[2 * i + 1]:
            a		:						Tuple 				=     2 * i
          else:
            a		:						str 				=     2 * i + 1
        a		:						Optional[int] 				=     self.__heap[i]
        if self.__heap[i] < self.__heap[bigger_child]:
          a		:						Any 				=     self.__heap[bigger_child]
          a		:						Union[str, Any] 				=     temporary
        a		:						Dict 				=     bigger_child






    def       __snake_case				(   self							:       List[str]):
      a		:						int 				=     self.__heap[1]
      a		:						Tuple 				=     self.__heap[self.__size]
      self.__size -= 1
      self.__heap.pop()
      self.__swap_down(1)
      return max_value






    @property
    def       __snake_case				(   self							:       List[str]):
      return self.__heap[1:]






    def __len__(   self							:       Optional[int]):
      return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 __lowercase          = BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(10)
 binary_heap.insert(15)
 binary_heap.insert(12)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))






