





"""simple docstring"""


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a__       :  int       		=					get_logger(__name__)

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

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

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

a__       :  int       		=					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__       :  Any       		=					ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


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

a__       :  List[Any]       		=					os.environ.get("""USE_TF""", """AUTO""").upper()
a__       :  Union[str, Any]       		=					os.environ.get("""USE_TORCH""", """AUTO""").upper()
a__       :  Optional[Any]       		=					os.environ.get("""USE_JAX""", """AUTO""").upper()

a__       :  Tuple       		=					"""N/A"""
a__       :  int       		=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
   a__       :  Union[str, Any]       		=					importlib.util.find_spec("""torch""") is not None
   if TORCH_AVAILABLE:
      try:
         a__       :  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__       :  Tuple       		=					version.parse(importlib_metadata.version(package))
         except importlib_metadata.PackageNotFoundError:
            continue
         else:
            break
      else:
         a__       :  List[str]       		=					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__       :  Tuple       		=					"""N/A"""
a__       :  int       		=					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__       :  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__       :  List[str]       		=					os.environ.get("""USE_BEAM""", """AUTO""").upper()
a__       :  str       		=					"""N/A"""
a__       :  Any       		=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
   try:
      a__       :  Union[str, Any]       		=					version.parse(importlib_metadata.version("""apache_beam"""))
      a__       :  Dict       		=					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__       :  Optional[int]       		=					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__       :  Dict       		=					importlib.util.find_spec("""rarfile""") is not None
a__       :  List[Any]       		=					importlib.util.find_spec("""zstandard""") is not None
a__       :  int       		=					importlib.util.find_spec("""lz4""") is not None
a__       :  Optional[int]       		=					importlib.util.find_spec("""py7zr""") is not None


# Cache location
a__       :  Any       		=					"""~/.cache"""
a__       :  Optional[int]       		=					os.getenv("""XDG_CACHE_HOME""", DEFAULT_XDG_CACHE_HOME)
a__       :  Tuple       		=					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__       :  List[str]       		=					Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

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

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

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

a__       :  Tuple       		=					"""extracted"""
a__       :  int       		=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
a__       :  int       		=					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__       :  Dict       		=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
a__       :  List[str]       		=					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__       :  List[Any]       		=					"""500MB"""

# Parquet configuration
a__       :  Tuple       		=					1_0_0
a__       :  List[str]       		=					1_0_0
a__       :  Dict       		=					1_0_0

# Offline mode
a__       :  List[str]       		=					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__       :  Union[str, Any]       		=					"""dataset.arrow"""
a__       :  str       		=					"""indices.arrow"""
a__       :  Optional[Any]       		=					"""state.json"""
a__       :  Tuple       		=					"""dataset_info.json"""
a__       :  List[str]       		=					"""dataset_infos.json"""
a__       :  Tuple       		=					"""LICENSE"""
a__       :  List[Any]       		=					"""metric_info.json"""
a__       :  Optional[Any]       		=					"""dataset_dict.json"""

a__       :  Tuple       		=					"""datasets_modules"""

a__       :  Optional[Any]       		=					2_5_5

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

# Datasets without script
a__       :  Tuple       		=					2_0_0
a__       :  Any       		=					1_0
a__       :  Any       		=					2_0_0

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








"""simple docstring"""


a__       :  Optional[Any]       		=					{
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}





def 				a__      (	_UpperCAmelCase						)     ->	list[tuple[str, str]]:

 '''simple docstring'''






 return [alphabet[char] for char in key.upper()]





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__     				=					""
 snake_case__     				=					0
 snake_case__     				=					generate_table(_UpperCAmelCase						)
 for char in words.upper():
  cipher += get_opponent(table[count]						,  _UpperCAmelCase						)
  snake_case__     				=					(count + 1) % len(_UpperCAmelCase						)
 return cipher





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 return encrypt(_UpperCAmelCase						,  _UpperCAmelCase						)





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	tuple[int, int]:

 '''simple docstring'''






 snake_case__     				=					0 if char in table[0] else 1
 snake_case__     				=					table[row].index(_UpperCAmelCase						)
 return row, col





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__				, snake_case__     				=					get_position(_UpperCAmelCase						,  char.upper()						)
 if row == 1:
  return table[0][col]
 else:
  return table[1][col] if row == 0 else char


if __name__ == "__main__":
   import doctest

   doctest.testmod()  # Fist ensure that all our tests are passing...
   a__       :  int       		=					input("""Enter key: """).strip()
   a__       :  Optional[int]       		=					input("""Enter text to encrypt: """).strip()
   a__       :  Dict       		=					encrypt(key, text)

   print(f'''Encrypted: {cipher_text}''')
   print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')

