code: 
  
  """simple docstring"""
  
  
  
  
  import importlib
  import os
  import platform
  from pathlib import Path
  
  from packaging import version
  
  from .utils.logging import get_logger
  
  
  __A  :					Union[str, Any]												=	get_logger(__name__)
  
  # Datasets
  __A  :					str												=	'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
  __A  :					Union[str, Any]												=	'https://cdn-datasets.huggingface.co/datasets/datasets'
  __A  :					str												=	'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'
  
  # Metrics
  __A  :					Optional[Any]												=	'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
  __A  :					str												=	'https://cdn-datasets.huggingface.co/datasets/metric'
  __A  :					List[str]												=	'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'
  
  # Hub
  __A  :					List[str]												=	os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
  __A  :					Optional[int]												=	HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
  __A  :					Optional[Any]												=	'main'
  
  __A  :					Tuple												=	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  :					List[Any]												=	ENV_VARS_TRUE_VALUES.union({'AUTO'})
  
  
  # Imports
  __A  :					List[Any]												=	version.parse(importlib_metadata.version('dill'))
  __A  :					Dict												=	version.parse(importlib_metadata.version('pandas'))
  __A  :					int												=	version.parse(importlib_metadata.version('pyarrow'))
  
  __A  :					Dict												=	os.environ.get('USE_TF', 'AUTO').upper()
  __A  :					Union[str, Any]												=	os.environ.get('USE_TORCH', 'AUTO').upper()
  __A  :					List[str]												=	os.environ.get('USE_JAX', 'AUTO').upper()
  
  __A  :					Tuple												=	'N/A'
  __A  :					Optional[int]												=	False
  
  if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
  	__A  :					Optional[Any]												=	importlib.util.find_spec('torch') is not None
  	if TORCH_AVAILABLE:
  		try:
  			__A  :					int												=	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  :					List[Any]												=	'N/A'
  __A  :					Optional[int]												=	False
  
  if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
  	__A  :					Union[str, 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  :					str												=	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  :					str												=	False
  		else:
  			logger.info(f"""TensorFlow version {TF_VERSION} available.""")
  else:
  	logger.info('Disabling Tensorflow because USE_TORCH is set')
  
  
  __A  :					Any												=	'N/A'
  __A  :					List[str]												=	False
  
  if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
  	__A  :					Any												=	importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
  	if JAX_AVAILABLE:
  		try:
  			__A  :					Union[str, Any]												=	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  :					Optional[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[Any]												=	version.parse(importlib_metadata.version('apache_beam'))
  		__A  :					Any												=	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  :					List[Any]												=	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  :					Tuple												=	importlib.util.find_spec('soundfile') is not None and version.parse(
      importlib.import_module('soundfile').__libsndfile_version__
  ) >= version.parse('1.0.31')
  __A  :					Union[str, 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  :					List[Any]												=	importlib.util.find_spec('zstandard') is not None
  __A  :					Union[str, Any]												=	importlib.util.find_spec('lz4') is not None
  __A  :					List[str]												=	importlib.util.find_spec('py7zr') is not None
  
  
  # Cache location
  __A  :					str												=	'~/.cache'
  __A  :					List[Any]												=	os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
  __A  :					Any												=	os.path.join(XDG_CACHE_HOME, 'huggingface')
  __A  :					Optional[Any]												=	os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))
  
  __A  :					Dict												=	os.path.join(HF_CACHE_HOME, 'datasets')
  __A  :					Any												=	Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))
  
  __A  :					Dict												=	os.path.join(HF_CACHE_HOME, 'metrics')
  __A  :					Dict												=	Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))
  
  __A  :					Optional[int]												=	os.path.join(HF_CACHE_HOME, 'modules')
  __A  :					List[Any]												=	Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))
  
  __A  :					str												=	'downloads'
  __A  :					List[str]												=	os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
  __A  :					Optional[Any]												=	Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))
  
  __A  :					Any												=	'extracted'
  __A  :					Dict												=	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  :					int												=	(
      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]												=	10_00
  
  # Size of the preloaded record batch in `Dataset.__iter__`
  __A  :					List[str]												=	10
  
  # Pickling tables works only for small tables (<4GiB)
  # For big tables, we write them on disk instead
  __A  :					List[Any]												=	4 << 30
  
  # 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  :					Optional[Any]												=	1_00
  __A  :					Dict												=	1_00
  __A  :					Any												=	1_00
  
  # Offline mode
  __A  :					int												=	os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES
  
  # In-memory
  __A  :					List[str]												=	0  # Disabled
  __A  :					str												=	float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))
  
  # File names
  __A  :					Union[str, Any]												=	'dataset.arrow'
  __A  :					Any												=	'indices.arrow'
  __A  :					int												=	'state.json'
  __A  :					Optional[Any]												=	'dataset_info.json'
  __A  :					Optional[Any]												=	'dataset_infos.json'
  __A  :					Optional[Any]												=	'LICENSE'
  __A  :					Optional[int]												=	'metric_info.json'
  __A  :					Dict												=	'dataset_dict.json'
  
  __A  :					Any												=	'datasets_modules'
  
  __A  :					Optional[Any]												=	2_55
  
  # Streaming
  __A  :					Tuple												=	20
  __A  :					Optional[Any]												=	5
  
  # Datasets without script
  __A  :					Any												=	2_00
  __A  :					int												=	10
  __A  :					str												=	2_00
  
  # Progress bars
  __A  :					Optional[int]												=	0.05  # 20 progress updates per sec
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import os
  
  from ...utils import logging
  from .utils import DataProcessor, InputExample
  
  
  __A  :					List[Any]												=	logging.get_logger(__name__)
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Any=None):
  							A    =      language
  							A    =      train_language
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self.language if self.train_language is None else self.train_language
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      F"""train-{i}"""
  												A    =      line[0]
  												A    =      line[1]
  												A    =      "contradiction" if line[2] == "contradictory" else line[2]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  "XNLI-1.0/xnli.test.tsv"))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      line[0]
  												if language != self.language:
  																	continue
  												A    =      F"""test-{i}"""
  												A    =      line[6]
  												A    =      line[7]
  												A    =      line[1]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							return ["contradiction", "entailment", "neutral"]
  
  __A  :					Optional[int]												=	{
      'xnli': XnliProcessor,
  }
  
  __A  :					Union[str, Any]												=	{
      'xnli': 'classification',
  }
  
  __A  :					Tuple												=	{
      'xnli': 3,
  }
  
  
  
  
  
  