


'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a_  :    Optional[int]      =			get_logger(__name__)

# Datasets
a_  :    str      =			"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
a_  :    Optional[Any]      =			"""https://cdn-datasets.huggingface.co/datasets/datasets"""
a_  :    Optional[Any]      =			"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

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

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

a_  :    Optional[Any]      =			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[int]      =			{"""1""", """ON""", """YES""", """TRUE"""}
a_  :    Optional[int]      =			ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
a_  :    Tuple      =			version.parse(importlib_metadata.version("""dill"""))
a_  :    Union[str, Any]      =			version.parse(importlib_metadata.version("""pandas"""))
a_  :    int      =			version.parse(importlib_metadata.version("""pyarrow"""))

a_  :    Optional[Any]      =			os.environ.get("""USE_TF""", """AUTO""").upper()
a_  :    Dict      =			os.environ.get("""USE_TORCH""", """AUTO""").upper()
a_  :    int      =			os.environ.get("""USE_JAX""", """AUTO""").upper()

a_  :    Optional[Any]      =			"""N/A"""
a_  :    Optional[int]      =			False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
     a_  :    Any      =			importlib.util.find_spec("""torch""") is not None
     if TORCH_AVAILABLE:
          try:
               a_  :    List[Any]      =			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_  :    Any      =			"""N/A"""
a_  :    int      =			False

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

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

# Optional tools for feature decoding
a_  :    int      =			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[str]      =			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_  :    Optional[int]      =			importlib.util.find_spec("""rarfile""") is not None
a_  :    int      =			importlib.util.find_spec("""zstandard""") is not None
a_  :    Union[str, Any]      =			importlib.util.find_spec("""lz4""") is not None
a_  :    Tuple      =			importlib.util.find_spec("""py7zr""") is not None


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

a_  :    Any      =			os.path.join(HF_CACHE_HOME, """datasets""")
a_  :    int      =			Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

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

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

a_  :    Tuple      =			"""downloads"""
a_  :    Any      =			os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
a_  :    Tuple      =			Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

a_  :    Any      =			"""extracted"""
a_  :    Optional[Any]      =			os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
a_  :    Any      =			Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
a_  :    List[str]      =			(
    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_  :    Tuple      =			1000

# Size of the preloaded record batch in `Dataset.__iter__`
a_  :    str      =			10

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

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

# In-memory
a_  :    List[Any]      =			0  # Disabled
a_  :    List[Any]      =			float(os.environ.get("""HF_DATASETS_IN_MEMORY_MAX_SIZE""", DEFAULT_IN_MEMORY_MAX_SIZE))

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

a_  :    List[str]      =			"""datasets_modules"""

a_  :    str      =			255

# Streaming
a_  :    Optional[int]      =			20
a_  :    Union[str, Any]      =			5

# Datasets without script
a_  :    Optional[int]      =			200
a_  :    str      =			10
a_  :    Dict      =			200

# Progress bars
a_  :    Tuple      =			0.0_5  # 20 progress updates per sec







'''simple docstring'''

class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  data
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None





def __snake_case (				UpperCAmelCase_						:						BinaryTreeNode | None ,    UpperCAmelCase_						:						int		):
 if node is None:
  lowerCamelCase_								=  BinaryTreeNode(UpperCAmelCase_		)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase_								=  insert(node.left_child ,    UpperCAmelCase_		)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase_								=  insert(node.right_child ,    UpperCAmelCase_		)
 return node





def __snake_case (				UpperCAmelCase_						:						None | BinaryTreeNode		):  # if node is None,return
 if node:
  lowerCamelCase_								=  inorder(node.left_child		)
  lowerCamelCase_								=  [*inorder_array, node.data]
  lowerCamelCase_								=  inorder_array + inorder(node.right_child		)
 else:
  lowerCamelCase_								=  []
 return inorder_array





def __snake_case (				):
 lowerCamelCase_								=  insert(UpperCAmelCase_ ,    15		)
 insert(UpperCAmelCase_ ,    10		)
 insert(UpperCAmelCase_ ,    25		)
 insert(UpperCAmelCase_ ,    6		)
 insert(UpperCAmelCase_ ,    14		)
 insert(UpperCAmelCase_ ,    20		)
 insert(UpperCAmelCase_ ,    60		)
 return root





def __snake_case (				):
 # main function
 lowerCamelCase_								=  make_tree()
 print("Printing values of binary search tree in Inorder Traversal."		)
 inorder(UpperCAmelCase_		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()



