






import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A__     :      List[Any]             =      get_logger(__name__)

# Datasets
A__     :      Union[str, Any]             =      'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
A__     :      Dict             =      'https://cdn-datasets.huggingface.co/datasets/datasets'
A__     :      str             =      'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

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

# Hub
A__     :      int             =      os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
A__     :      str             =      HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
A__     :      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__     :      List[str]             =      {'1', 'ON', 'YES', 'TRUE'}
A__     :      List[Any]             =      ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
A__     :      List[str]             =      version.parse(importlib_metadata.version('dill'))
A__     :      int             =      version.parse(importlib_metadata.version('pandas'))
A__     :      Optional[Any]             =      version.parse(importlib_metadata.version('pyarrow'))

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

A__     :      List[Any]             =      '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__     :      List[str]             =      importlib.util.find_spec('torch') is not None
							if TORCH_AVAILABLE:
														try:
																					A__     :      Union[str, 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__     :      int             =      '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__     :      Optional[int]             =      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__     :      Any             =      version.parse(importlib_metadata.version(package))
																					except importlib_metadata.PackageNotFoundError:
																												continue
																					else:
																												break
														else:
																					A__     :      Tuple             =      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__     :      Any             =      False
														else:
																					logger.info(F"TensorFlow version {TF_VERSION} available.")
else:
							logger.info('Disabling Tensorflow because USE_TORCH is set')


A__     :      Union[str, Any]             =      'N/A'
A__     :      str             =      False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
							A__     :      Union[str, Any]             =      importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
							if JAX_AVAILABLE:
														try:
																					A__     :      str             =      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__     :      str             =      os.environ.get('USE_BEAM', 'AUTO').upper()
A__     :      List[Any]             =      'N/A'
A__     :      Tuple             =      False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
							try:
														A__     :      str             =      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__     :      Optional[int]             =      importlib.util.find_spec('sqlalchemy') is not None

# Optional tools for feature decoding
A__     :      List[str]             =      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__     :      List[Any]             =      importlib.util.find_spec('rarfile') is not None
A__     :      Union[str, Any]             =      importlib.util.find_spec('zstandard') is not None
A__     :      Any             =      importlib.util.find_spec('lz4') is not None
A__     :      int             =      importlib.util.find_spec('py7zr') is not None


# Cache location
A__     :      Tuple             =      '~/.cache'
A__     :      Dict             =      os.getenv('XDG_CACHE_HOME', DEFAULT_XDG_CACHE_HOME)
A__     :      Dict             =      os.path.join(XDG_CACHE_HOME, 'huggingface')
A__     :      Tuple             =      os.path.expanduser(os.getenv('HF_HOME', DEFAULT_HF_CACHE_HOME))

A__     :      str             =      os.path.join(HF_CACHE_HOME, 'datasets')
A__     :      List[Any]             =      Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

A__     :      Optional[int]             =      os.path.join(HF_CACHE_HOME, 'metrics')
A__     :      Optional[int]             =      Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))

A__     :      List[str]             =      os.path.join(HF_CACHE_HOME, 'modules')
A__     :      List[Any]             =      Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

A__     :      Optional[int]             =      'downloads'
A__     :      Optional[int]             =      os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A__     :      int             =      Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

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

# Download count for the website
A__     :      List[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__     :      List[Any]             =      10_00

# Size of the preloaded record batch in `Dataset.__iter__`
A__     :      int             =      10

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

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A__     :      Dict             =      '500MB'

# Parquet configuration
A__     :      Optional[Any]             =      1_00
A__     :      List[Any]             =      1_00
A__     :      Tuple             =      1_00

# Offline mode
A__     :      Optional[Any]             =      os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES

# In-memory
A__     :      Union[str, Any]             =      0  # Disabled
A__     :      Tuple             =      float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A__     :      str             =      'dataset.arrow'
A__     :      List[str]             =      'indices.arrow'
A__     :      List[Any]             =      'state.json'
A__     :      int             =      'dataset_info.json'
A__     :      Tuple             =      'dataset_infos.json'
A__     :      int             =      'LICENSE'
A__     :      Union[str, Any]             =      'metric_info.json'
A__     :      List[Any]             =      'dataset_dict.json'

A__     :      Optional[Any]             =      'datasets_modules'

A__     :      Dict             =      2_55

# Streaming
A__     :      Optional[Any]             =      20
A__     :      str             =      5

# Datasets without script
A__     :      Optional[int]             =      2_00
A__     :      Optional[Any]             =      10
A__     :      Any             =      2_00

# Progress bars
A__     :      Any             =      0.05  # 20 progress updates per sec












import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


A__     :      Dict             =			'none'
A__     :      Optional[Any]             =			'none'






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      value.split(lowerCamelCase_    )
				return len(lowerCamelCase_    ) > 1 and any((len(lowerCamelCase_    ) == 3 for x in split)    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								return value
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								return float(lowerCamelCase_    )
				if not isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								raise ValueError('''Argument value is not a string. Can\'t parse it as float'''    )
				lowercase__       			=      value

				try:
								# Example: 1,000.7
								if "." in sanitized and "," in sanitized:
												return float(sanitized.replace(''','''		,       ''''''    )    )
								# 1,000
								if "," in sanitized and _split_thousands(''','''		,       lowerCamelCase_    ):
												return float(sanitized.replace(''','''		,       ''''''    )    )
								# 5,5556
								if "," in sanitized and sanitized.count(''','''    ) == 1 and not _split_thousands(''','''		,       lowerCamelCase_    ):
												return float(sanitized.replace(''','''		,       '''.'''    )    )
								# 0.0.0.1
								if sanitized.count('''.'''    ) > 1:
												return float(sanitized.replace('''.'''		,       ''''''    )    )
								# 0,0,0,1
								if sanitized.count(''','''    ) > 1:
												return float(sanitized.replace(''','''		,       ''''''    )    )
								return float(lowerCamelCase_    )
				except ValueError:
								# Avoid adding the sanitized value in the error message.
								raise ValueError('''Unable to convert value to float'''    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				if answer is None:
								return None
				try:
								lowercase__       			=      convert_to_float(lowerCamelCase_    )
								if isinstance(lowerCamelCase_		,       lowerCamelCase_    ) and math.isnan(lowerCamelCase_    ):
												return None
								return value
				except ValueError:
								return answer.lower()


A__     :      Tuple             =			{
    'text': lambda x: x,
    'real': convert_to_float,
}
class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2
				lowercase__    							=	3
				lowercase__    							=	4
				lowercase__    							=	5



class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2



@dataclasses.dataclass
class 				_UpperCAmelCase :

				"""simple docstring"""




				lowercase__    							=	42
				lowercase__    							=	42
				lowercase__    							=	42



A__     :      str             =			re.compile(r'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return list(_TOKENIZER.findall(x.lower()    )    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				if operator == _Operator.EQUALS:
								return src == tgt
				elif operator == _Operator.GREATER:
								return src > tgt
				elif operator == _Operator.LESSER:
								return src < tgt
				raise ValueError(F"""Unknown operator: {operator}"""    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      table['''types''']
				return _TYPE_CONVERTER[types[column]](lowerCamelCase_    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return isinstance(lowerCamelCase_		,       lowerCamelCase_    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				for cond in conditions:
								lowercase__       			=      row[cond.column]

								lowercase__       			=      _parse_value(lowerCamelCase_		,       cond.column		,       cond.cmp_value    )

								if _is_string(lowerCamelCase_    ) and _is_string(lowerCamelCase_    ):
												lowercase__       			=      _normalize_for_match(lowerCamelCase_    )
												lowercase__       			=      _normalize_for_match(lowerCamelCase_    )

								if not isinstance(lowerCamelCase_		,       type(lowerCamelCase_    )    ):
												raise ValueError('''Type difference {} != {}'''.format(type(lowerCamelCase_    )		,       type(lowerCamelCase_    )    )    )

								if not _compare(cond.operator		,       lowerCamelCase_		,       lowerCamelCase_    ):
												return False
				return True






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				if not answer_coordinates:
								if aggregation_op == _Aggregation.COUNT:
												return 0.0
								else:
												return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
				if aggregation_op == _Aggregation.COUNT:
								return float(len(lowerCamelCase_    )    )

				# If we have just one answer, if float returns it or try a conversion.
				lowercase__       			=      [table['''rows'''][i][j] for (i, j) in answer_coordinates]
				if len(lowerCamelCase_    ) == 1:
								try:
												return convert_to_float(values[0]    )
								except ValueError as e:
												if aggregation_op != _Aggregation.NONE:
																raise e

				if aggregation_op == _Aggregation.NONE:
								return None

				# Other aggregation only support numeric values. Bail out if we have strings.
				if not all((isinstance(lowerCamelCase_		,       (int, float)    ) for v in values)    ):
								return None

				if aggregation_op == _Aggregation.SUM:
								return float(sum(lowerCamelCase_    )    )
				elif aggregation_op == _Aggregation.AVERAGE:
								return sum(lowerCamelCase_    ) / len(lowerCamelCase_    )
				else:
								raise ValueError(F"""Unknown aggregation: {aggregation_op}"""    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      sql_query['''agg''']
				if aggregation_op_index >= 3:
								lowercase__       			=      _Aggregation(lowerCamelCase_    )
				else:
								lowercase__       			=      _Aggregation.NONE

				lowercase__       			=      sql_query['''sel''']
				lowercase__       			=      [
				    _Condition(lowerCamelCase_		,       _Operator(lowerCamelCase_    )		,       lowerCamelCase_    )
				    for column, operator, cmp_value in zip(
				        sql_query['''conds''']['''column_index''']		,       sql_query['''conds''']['''operator_index''']		,       sql_query['''conds''']['''condition''']    )
				]

				lowercase__       			=      []
				for row in range(len(table['''rows''']    )    ):
								if _respect_conditions(lowerCamelCase_		,       table['''rows'''][row]		,       lowerCamelCase_    ):
												indices.append((row, target_column)    )

				if not indices:
								return [], aggregation_op

				if len(lowerCamelCase_    ) == 1:
								return indices, aggregation_op

				# Parsing of MIN/MAX.
				if aggregation_op_index in (1, 2):
								lowercase__       			=      {2: min, 1: max}
								lowercase__       			=      [(table['''rows'''][i][j], index) for index, (i, j) in enumerate(lowerCamelCase_    )]
								lowercase__       			=      functools.reduce(operators[sql_query['''agg''']]		,       lowerCamelCase_    )

								lowercase__       			=      [indices[reduced[1]]]
								return ret, _Aggregation.NONE

				return indices, aggregation_op






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				if float_answer is not None:
								return [str(lowerCamelCase_    )]
				return [str(table['''real_rows'''][r][c]    ) for r, c in answer_coordinates]






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__       			=      _get_answer_coordinates(lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_float_answer(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_answer_text(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				# keep the original data the same with TaPas
				if len(lowerCamelCase_    ) == 0:
								lowercase__       			=      [EMPTY_ANSWER]
				return answer_text




