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

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

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

a__						:				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__						:				str           =    {"1", "ON", "YES", "TRUE"}
a__						:				int           =    ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
a__						:				int           =    version.parse(importlib_metadata.version("dill"))
a__						:				Optional[Any]           =    version.parse(importlib_metadata.version("pandas"))
a__						:				Optional[int]           =    version.parse(importlib_metadata.version("pyarrow"))

a__						:				Tuple           =    os.environ.get("USE_TF", "AUTO").upper()
a__						:				int           =    os.environ.get("USE_TORCH", "AUTO").upper()
a__						:				Any           =    os.environ.get("USE_JAX", "AUTO").upper()

a__						:				List[str]           =    "N/A"
a__						:				Tuple           =    False

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

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
 a__						:				Tuple           =    importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
 if JAX_AVAILABLE:
  try:
   a__						:				Dict           =    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__						:				int           =    "N/A"
a__						:				str           =    False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
 try:
  a__						:				List[str]           =    version.parse(importlib_metadata.version("apache_beam"))
  a__						:				str           =    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__						:				Optional[int]           =    importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
a__						:				List[Any]           =    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__						:				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__						:				Optional[Any]           =    importlib.util.find_spec("zstandard") is not None
a__						:				int           =    importlib.util.find_spec("lz4") is not None
a__						:				int           =    importlib.util.find_spec("py7zr") is not None


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

a__						:				Union[str, Any]           =    os.path.join(HF_CACHE_HOME, "datasets")
a__						:				str           =    Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

a__						:				Dict           =    os.path.join(HF_CACHE_HOME, "metrics")
a__						:				Optional[Any]           =    Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

a__						:				Optional[Any]           =    os.path.join(HF_CACHE_HOME, "modules")
a__						:				Optional[Any]           =    Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

a__						:				Dict           =    "downloads"
a__						:				str           =    os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
a__						:				Dict           =    Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

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

# Download count for the website
a__						:				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__						:				Optional[int]           =    1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
a__						:				Dict           =    1_0

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

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

# Parquet configuration
a__						:				Tuple           =    1_0_0
a__						:				Dict           =    1_0_0
a__						:				Union[str, Any]           =    1_0_0

# 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__						:				Union[str, Any]           =    float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
a__						:				Any           =    "dataset.arrow"
a__						:				Tuple           =    "indices.arrow"
a__						:				str           =    "state.json"
a__						:				List[str]           =    "dataset_info.json"
a__						:				Dict           =    "dataset_infos.json"
a__						:				int           =    "LICENSE"
a__						:				int           =    "metric_info.json"
a__						:				int           =    "dataset_dict.json"

a__						:				Any           =    "datasets_modules"

a__						:				List[str]           =    2_5_5

# Streaming
a__						:				Dict           =    2_0
a__						:				Dict           =    5

# Datasets without script
a__						:				Union[str, Any]           =    2_0_0
a__						:				str           =    1_0
a__						:				Optional[int]           =    2_0_0

# Progress bars
a__						:				List[str]           =    0.05  # 20 progress updates per sec








'''simple docstring'''





import itertools
from pathlib import Path





def     snake_case							(						UpperCAmelCase		)->     int:







      """simple docstring"""
      __A    		=     [tuple(UpperCAmelCase		) for login in logins]

      __A    		=     {char for login in split_logins for char in login}

      for permutation in itertools.permutations(UpperCAmelCase		):
            __A    		=     True
            for login in logins:
                  if not (
                      permutation.index(login[0]		)
                      < permutation.index(login[1]		)
                      < permutation.index(login[2]		)
                  ):
                        __A    		=     False
                        break

            if satisfied:
                  return int(''.join(UpperCAmelCase		)		)

      raise Exception('Unable to find the secret passcode'		)





def     snake_case							(						UpperCAmelCase = "keylog.txt"		)->     int:







      """simple docstring"""
      __A    		=     Path(UpperCAmelCase		).parent.joinpath(UpperCAmelCase		).read_text().splitlines()

      return find_secret_passcode(UpperCAmelCase		)


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






