


import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_UpperCAmelCase      :	Optional[Any]  =get_logger(__name__)

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

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

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

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


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

_UpperCAmelCase      :	Dict  =os.environ.get("""USE_TF""", """AUTO""").upper()
_UpperCAmelCase      :	Union[str, Any]  =os.environ.get("""USE_TORCH""", """AUTO""").upper()
_UpperCAmelCase      :	Tuple  =os.environ.get("""USE_JAX""", """AUTO""").upper()

_UpperCAmelCase      :	Any  ="""N/A"""
_UpperCAmelCase      :	Optional[Any]  =False

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

_UpperCAmelCase      :	Tuple  ="""N/A"""
_UpperCAmelCase      :	Tuple  =False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
						_UpperCAmelCase      :	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:
																								_UpperCAmelCase      :	str  =version.parse(importlib_metadata.version(package))
																		except importlib_metadata.PackageNotFoundError:
																								continue
																		else:
																								break
												else:
																		_UpperCAmelCase      :	Union[str, 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.""")
																		_UpperCAmelCase      :	List[Any]  =False
												else:
																		logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
						logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_UpperCAmelCase      :	Any  ="""N/A"""
_UpperCAmelCase      :	Dict  =False

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


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

# Optional tools for feature decoding
_UpperCAmelCase      :	Any  =importlib.util.find_spec("""PIL""") is not None
_UpperCAmelCase      :	List[Any]  =importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
_UpperCAmelCase      :	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
_UpperCAmelCase      :	List[str]  =importlib.util.find_spec("""rarfile""") is not None
_UpperCAmelCase      :	Dict  =importlib.util.find_spec("""zstandard""") is not None
_UpperCAmelCase      :	str  =importlib.util.find_spec("""lz4""") is not None
_UpperCAmelCase      :	int  =importlib.util.find_spec("""py7zr""") is not None


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

_UpperCAmelCase      :	Optional[Any]  =os.path.join(HF_CACHE_HOME, """datasets""")
_UpperCAmelCase      :	Tuple  =Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

_UpperCAmelCase      :	Tuple  =os.path.join(HF_CACHE_HOME, """metrics""")
_UpperCAmelCase      :	Any  =Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

_UpperCAmelCase      :	str  =os.path.join(HF_CACHE_HOME, """modules""")
_UpperCAmelCase      :	str  =Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

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

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

# Download count for the website
_UpperCAmelCase      :	Any  =(
    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)
_UpperCAmelCase      :	Optional[int]  =1000

# Size of the preloaded record batch in `Dataset.__iter__`
_UpperCAmelCase      :	Optional[int]  =10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
_UpperCAmelCase      :	Optional[int]  =4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
_UpperCAmelCase      :	Union[str, Any]  ="""500MB"""

# Parquet configuration
_UpperCAmelCase      :	Optional[Any]  =100
_UpperCAmelCase      :	Union[str, Any]  =100
_UpperCAmelCase      :	List[str]  =100

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

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

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

_UpperCAmelCase      :	Union[str, Any]  ="""datasets_modules"""

_UpperCAmelCase      :	Any  =255

# Streaming
_UpperCAmelCase      :	int  =20
_UpperCAmelCase      :	str  =5

# Datasets without script
_UpperCAmelCase      :	str  =200
_UpperCAmelCase      :	List[str]  =10
_UpperCAmelCase      :	Union[str, Any]  =200

# Progress bars
_UpperCAmelCase      :	Optional[Any]  =0.05  # 20 progress updates per sec



import numpy as np

_UpperCAmelCase      :	int  =[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]







class 					snake_case__:







						'''simple docstring'''




						def __init__(					self				)       ->     None:
										lowerCAmelCase_			:   List[Any]     					=					np.array(__lowercase				)




						def     lowercase_			(					self ,    __lowercase				)       ->     np.ndarray:
										lowerCAmelCase_					,	lowerCAmelCase_			:   List[str]     					=					np.where(letter == self.SQUARE				)
										lowerCAmelCase_			:   Union[str, Any]     					=					np.concatenate([indexa + 1, indexa + 1]				)
										return indexes




						def     lowercase_			(					self ,    __lowercase ,    __lowercase				)       ->     str:
										return self.SQUARE[indexa - 1, indexa - 1]




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   Dict     					=					message.lower()
										lowerCAmelCase_			:   Any     					=					message.replace('''j''' ,    '''i'''				)

										lowerCAmelCase_			:   Optional[Any]     					=					''''''
										for letter_index in range(len(__lowercase				)				):
														if message[letter_index] != " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					self.letter_to_numbers(message[letter_index]				)
																		lowerCAmelCase_			:   List[Any]     					=					encoded_message + str(numbers[0]				) + str(numbers[1]				)
														elif message[letter_index] == " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					encoded_message + ''' '''

										return encoded_message




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   List[str]     					=					message.replace(''' ''' ,    '''  '''				)
										lowerCAmelCase_			:   Union[str, Any]     					=					''''''
										for numbers_index in range(int(len(__lowercase				) / 2				)				):
														if message[numbers_index * 2] != " ":
																		lowerCAmelCase_			:   Any     					=					message[numbers_index * 2]
																		lowerCAmelCase_			:   Dict     					=					message[numbers_index * 2 + 1]

																		lowerCAmelCase_			:   Dict     					=					self.numbers_to_letter(int(__lowercase				) ,    int(__lowercase				)				)
																		lowerCAmelCase_			:   Tuple     					=					decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		lowerCAmelCase_			:   List[str]     					=					decoded_message + ''' '''

										return decoded_message