

"""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








"""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,
}





