



import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_lowerCamelCase         =	get_logger(__name__)

# Datasets
_lowerCamelCase         =	"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
_lowerCamelCase         =	"""https://cdn-datasets.huggingface.co/datasets/datasets"""
_lowerCamelCase         =	"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

# Metrics
_lowerCamelCase         =	"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
_lowerCamelCase         =	"""https://cdn-datasets.huggingface.co/datasets/metric"""
_lowerCamelCase         =	"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

# Hub
_lowerCamelCase         =	os.environ.get("""HF_ENDPOINT""", """https://huggingface.co""")
_lowerCamelCase         =	HF_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
_lowerCamelCase         =	"""main"""

_lowerCamelCase         =	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
_lowerCamelCase         =	{"""1""", """ON""", """YES""", """TRUE"""}
_lowerCamelCase         =	ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
_lowerCamelCase         =	version.parse(importlib_metadata.version("""dill"""))
_lowerCamelCase         =	version.parse(importlib_metadata.version("""pandas"""))
_lowerCamelCase         =	version.parse(importlib_metadata.version("""pyarrow"""))

_lowerCamelCase         =	os.environ.get("""USE_TF""", """AUTO""").upper()
_lowerCamelCase         =	os.environ.get("""USE_TORCH""", """AUTO""").upper()
_lowerCamelCase         =	os.environ.get("""USE_JAX""", """AUTO""").upper()

_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
      _lowerCamelCase         =	importlib.util.find_spec("""torch""") is not None
      if TORCH_AVAILABLE:
            try:
                  _lowerCamelCase         =	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""")

_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
      _lowerCamelCase         =	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:
                        _lowerCamelCase         =	version.parse(importlib_metadata.version(package))
                  except importlib_metadata.PackageNotFoundError:
                        continue
                  else:
                        break
            else:
                  _lowerCamelCase         =	False
      if TF_AVAILABLE:
            if TF_VERSION.major < 2:
                  logger.info(f'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
                  _lowerCamelCase         =	False
            else:
                  logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
      logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
      _lowerCamelCase         =	importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
      if JAX_AVAILABLE:
            try:
                  _lowerCamelCase         =	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""")


_lowerCamelCase         =	os.environ.get("""USE_BEAM""", """AUTO""").upper()
_lowerCamelCase         =	"""N/A"""
_lowerCamelCase         =	False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
      try:
            _lowerCamelCase         =	version.parse(importlib_metadata.version("""apache_beam"""))
            _lowerCamelCase         =	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
_lowerCamelCase         =	importlib.util.find_spec("""sqlalchemy""") is not None

# Optional tools for feature decoding
_lowerCamelCase         =	importlib.util.find_spec("""PIL""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
_lowerCamelCase         =	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
_lowerCamelCase         =	importlib.util.find_spec("""rarfile""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""zstandard""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""lz4""") is not None
_lowerCamelCase         =	importlib.util.find_spec("""py7zr""") is not None


# Cache location
_lowerCamelCase         =	"""~/.cache"""
_lowerCamelCase         =	os.getenv("""XDG_CACHE_HOME""", DEFAULT_XDG_CACHE_HOME)
_lowerCamelCase         =	os.path.join(XDG_CACHE_HOME, """huggingface""")
_lowerCamelCase         =	os.path.expanduser(os.getenv("""HF_HOME""", DEFAULT_HF_CACHE_HOME))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """datasets""")
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """metrics""")
_lowerCamelCase         =	Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

_lowerCamelCase         =	os.path.join(HF_CACHE_HOME, """modules""")
_lowerCamelCase         =	Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

_lowerCamelCase         =	"""downloads"""
_lowerCamelCase         =	os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

_lowerCamelCase         =	"""extracted"""
_lowerCamelCase         =	os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_lowerCamelCase         =	Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_lowerCamelCase         =	(
    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)
_lowerCamelCase         =	1000

# Size of the preloaded record batch in `Dataset.__iter__`
_lowerCamelCase         =	10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
_lowerCamelCase         =	4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
_lowerCamelCase         =	"""500MB"""

# Parquet configuration
_lowerCamelCase         =	100
_lowerCamelCase         =	100
_lowerCamelCase         =	100

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

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

# File names
_lowerCamelCase         =	"""dataset.arrow"""
_lowerCamelCase         =	"""indices.arrow"""
_lowerCamelCase         =	"""state.json"""
_lowerCamelCase         =	"""dataset_info.json"""
_lowerCamelCase         =	"""dataset_infos.json"""
_lowerCamelCase         =	"""LICENSE"""
_lowerCamelCase         =	"""metric_info.json"""
_lowerCamelCase         =	"""dataset_dict.json"""

_lowerCamelCase         =	"""datasets_modules"""

_lowerCamelCase         =	255

# Streaming
_lowerCamelCase         =	20
_lowerCamelCase         =	5

# Datasets without script
_lowerCamelCase         =	200
_lowerCamelCase         =	10
_lowerCamelCase         =	200

# Progress bars
_lowerCamelCase         =	0.0_5  # 20 progress updates per sec











import sys

sys.set_int_max_str_digits(0)  # type: ignore





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			_UpperCAmelCase									=  [0] * 11
			_UpperCAmelCase									=  [0] * 11

			# mark last 9 numbers
			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10
			# flag
			_UpperCAmelCase									=  True

			# check last 9 numbers for pandigitality

			for x in range(9       ):
						if not check_last[x + 1]:
									_UpperCAmelCase									=  False
			if not f:
						return f

			# mark first 9 numbers
			_UpperCAmelCase									=  int(str(_UpperCAmelCase       )[:9]       )

			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10

			# check first 9 numbers for pandigitality

			for x in range(9       ):
						if not check_front[x + 1]:
									_UpperCAmelCase									=  False
			return f





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			_UpperCAmelCase									=  [0] * 11

			# mark last 9 numbers
			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10
			# flag
			_UpperCAmelCase									=  True

			# check last 9 numbers for pandigitality

			for x in range(9       ):
						if not check_last[x + 1]:
									_UpperCAmelCase									=  False
			return f





def 				A      (	)							->					int:







			'''simple docstring'''
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  2
			# temporary Fibonacci numbers

			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  2
			# temporary Fibonacci numbers mod 1e9

			# mod m=1e9, done for fast optimisation
			_UpperCAmelCase									=  [0] * 1_000_000
			_UpperCAmelCase									=  1_000_000_000

			for x in range(1_000_000       ):
						_UpperCAmelCase									=  (aa + ba) % m
						_UpperCAmelCase									=  ba % m
						_UpperCAmelCase									=  ca % m
						if checka(_UpperCAmelCase       ):
									_UpperCAmelCase									=  1

			for x in range(1_000_000       ):
						_UpperCAmelCase									=  a + b
						_UpperCAmelCase									=  b
						_UpperCAmelCase									=  c
						# perform check only if in tocheck
						if tocheck[x + 3] and check(_UpperCAmelCase       ):
									return x + 3  # first 2 already done
			return -1


if __name__ == "__main__":
							print(f"""{solution() = }""")
