

"""simple docstring"""


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


__A		     =    get_logger(__name__)

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

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

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

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


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

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

__A		     =    "N/A"
__A		     =    False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
			__A		     =    importlib.util.find_spec("torch") is not None
			if TORCH_AVAILABLE:
						try:
									__A		     =    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		     =    "N/A"
__A		     =    False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
			__A		     =    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		     =    version.parse(importlib_metadata.version(package))
									except importlib_metadata.PackageNotFoundError:
												continue
									else:
												break
						else:
									__A		     =    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		     =    False
						else:
									logger.info(F'''TensorFlow version {TF_VERSION} available.''')
else:
			logger.info("Disabling Tensorflow because USE_TORCH is set")


__A		     =    "N/A"
__A		     =    False

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

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


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

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

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

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

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

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

# Download count for the website
__A		     =    (
    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		     =    1000

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

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

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

# Parquet configuration
__A		     =    100
__A		     =    100
__A		     =    100

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

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

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

__A		     =    "datasets_modules"

__A		     =    255

# Streaming
__A		     =    20
__A		     =    5

# Datasets without script
__A		     =    200
__A		     =    10
__A		     =    200

# Progress bars
__A		     =    0.05  # 20 progress updates per sec





"""simple docstring"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    "", "", ""
		__lowerCAmelCase: Any					   =    []

		for character in message_part:
				tmp.append(character_to_number[character]				)

		for each in tmp:
				one += each[0]
				two += each[1]
				three += each[2]

		return one + two + three






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, str]:
		__lowerCAmelCase   ,				__lowerCAmelCase: Union[str, Any]					   =    "", ""
		__lowerCAmelCase: int					   =    []

		for character in message_part:
				this_part += character_to_number[character]

		for digit in this_part:
				tmp += digit
				if len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				):
						result.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: Union[str, Any]					   =    ""

		return result[0], result[1], result[2]






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[str, str, dict[str, str], dict[str, str]]:
		# Validate message and alphabet, set to upper and remove spaces
		__lowerCAmelCase: int					   =    alphabet.replace(" "      ,				""				).upper()
		__lowerCAmelCase: int					   =    message.replace(" "      ,				""				).upper()

		# Check length and characters
		if len(__SCREAMING_SNAKE_CASE				) != 2_7:
				raise KeyError("Length of alphabet has to be 27."				)
		for each in message:
				if each not in alphabet:
						raise ValueError("Each message character has to be included in alphabet!"				)

    # Generate dictionares
		__lowerCAmelCase: Optional[int]					   =    (
		    "111",
		    "112",
		    "113",
		    "121",
		    "122",
		    "123",
		    "131",
		    "132",
		    "133",
		    "211",
		    "212",
		    "213",
		    "221",
		    "222",
		    "223",
		    "231",
		    "232",
		    "233",
		    "311",
		    "312",
		    "313",
		    "321",
		    "322",
		    "323",
		    "331",
		    "332",
		    "333",
		)
		__lowerCAmelCase: Tuple					   =    {}
		__lowerCAmelCase: Optional[int]					   =    {}
		for letter, number in zip(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: List[Any]					   =    number
				__lowerCAmelCase: Any					   =    letter

		return message, alphabet, character_to_number, number_to_character






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase   ,				__lowerCAmelCase: str					   =    "", ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				encrypted_numeric += __encrypt_part(
				    message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				)      ,				3				):
				encrypted += number_to_character[encrypted_numeric[i : i + 3]]

		return encrypted






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."      ,				__SCREAMING_SNAKE_CASE = 5				)			->			str:
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Dict					   =    __prepare(
		    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: List[str]					   =    []
		__lowerCAmelCase: Optional[int]					   =    ""

		for i in range(0      ,				len(__SCREAMING_SNAKE_CASE				) + 1      ,				__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    __decrypt_part(message[i : i + period]      ,				__SCREAMING_SNAKE_CASE				)

				for j in range(0      ,				len(__SCREAMING_SNAKE_CASE				)				):
						decrypted_numeric.append(a[j] + b[j] + c[j]				)

		for each in decrypted_numeric:
				decrypted += number_to_character[each]

		return decrypted


if __name__ == "__main__":
			__A		     =    "DEFEND THE EAST WALL OF THE CASTLE."
			__A		     =    encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			__A		     =    decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
			print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')


