code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import importlib
  import os
  import platform
  from pathlib import Path
  
  from packaging import version
  
  from .utils.logging import get_logger
  
  
  UpperCAmelCase__      =							get_logger(__name__)
  
  # Datasets
  UpperCAmelCase__      =							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'''
  UpperCAmelCase__      =							'''https://cdn-datasets.huggingface.co/datasets/datasets'''
  UpperCAmelCase__      =							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'''
  
  # Metrics
  UpperCAmelCase__      =							'''https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'''
  UpperCAmelCase__      =							'''https://cdn-datasets.huggingface.co/datasets/metric'''
  UpperCAmelCase__      =							'''https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'''
  
  # Hub
  UpperCAmelCase__      =							os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
  UpperCAmelCase__      =							HF_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
  UpperCAmelCase__      =							'''main'''
  
  UpperCAmelCase__      =							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__      =							{'''1''', '''ON''', '''YES''', '''TRUE'''}
  UpperCAmelCase__      =							ENV_VARS_TRUE_VALUES.union({'''AUTO'''})
  
  
  # Imports
  UpperCAmelCase__      =							version.parse(importlib_metadata.version('''dill'''))
  UpperCAmelCase__      =							version.parse(importlib_metadata.version('''pandas'''))
  UpperCAmelCase__      =							version.parse(importlib_metadata.version('''pyarrow'''))
  
  UpperCAmelCase__      =							os.environ.get('''USE_TF''', '''AUTO''').upper()
  UpperCAmelCase__      =							os.environ.get('''USE_TORCH''', '''AUTO''').upper()
  UpperCAmelCase__      =							os.environ.get('''USE_JAX''', '''AUTO''').upper()
  
  UpperCAmelCase__      =							'''N/A'''
  UpperCAmelCase__      =							False
  
  if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
  					UpperCAmelCase__      =							importlib.util.find_spec('''torch''') is not None
  					if TORCH_AVAILABLE:
  										try:
  															UpperCAmelCase__      =							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__      =							'''N/A'''
  UpperCAmelCase__      =							False
  
  if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
  					UpperCAmelCase__      =							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__      =							version.parse(importlib_metadata.version(package))
  															except importlib_metadata.PackageNotFoundError:
  																				continue
  															else:
  																				break
  										else:
  															UpperCAmelCase__      =							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__      =							False
  										else:
  															logger.info(F"""TensorFlow version {TF_VERSION} available.""")
  else:
  					logger.info('''Disabling Tensorflow because USE_TORCH is set''')
  
  
  UpperCAmelCase__      =							'''N/A'''
  UpperCAmelCase__      =							False
  
  if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
  					UpperCAmelCase__      =							importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
  					if JAX_AVAILABLE:
  										try:
  															UpperCAmelCase__      =							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__      =							os.environ.get('''USE_BEAM''', '''AUTO''').upper()
  UpperCAmelCase__      =							'''N/A'''
  UpperCAmelCase__      =							False
  if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
  					try:
  										UpperCAmelCase__      =							version.parse(importlib_metadata.version('''apache_beam'''))
  										UpperCAmelCase__      =							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__      =							importlib.util.find_spec('''sqlalchemy''') is not None
  
  # Optional tools for feature decoding
  UpperCAmelCase__      =							importlib.util.find_spec('''PIL''') is not None
  UpperCAmelCase__      =							importlib.util.find_spec('''soundfile''') is not None and version.parse(
      importlib.import_module('''soundfile''').__libsndfile_version__
  ) >= version.parse('''1.0.31''')
  UpperCAmelCase__      =							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__      =							importlib.util.find_spec('''rarfile''') is not None
  UpperCAmelCase__      =							importlib.util.find_spec('''zstandard''') is not None
  UpperCAmelCase__      =							importlib.util.find_spec('''lz4''') is not None
  UpperCAmelCase__      =							importlib.util.find_spec('''py7zr''') is not None
  
  
  # Cache location
  UpperCAmelCase__      =							'''~/.cache'''
  UpperCAmelCase__      =							os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
  UpperCAmelCase__      =							os.path.join(XDG_CACHE_HOME, '''huggingface''')
  UpperCAmelCase__      =							os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))
  
  UpperCAmelCase__      =							os.path.join(HF_CACHE_HOME, '''datasets''')
  UpperCAmelCase__      =							Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))
  
  UpperCAmelCase__      =							os.path.join(HF_CACHE_HOME, '''metrics''')
  UpperCAmelCase__      =							Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))
  
  UpperCAmelCase__      =							os.path.join(HF_CACHE_HOME, '''modules''')
  UpperCAmelCase__      =							Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))
  
  UpperCAmelCase__      =							'''downloads'''
  UpperCAmelCase__      =							os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
  UpperCAmelCase__      =							Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))
  
  UpperCAmelCase__      =							'''extracted'''
  UpperCAmelCase__      =							os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
  UpperCAmelCase__      =							Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))
  
  # Download count for the website
  UpperCAmelCase__      =							(
      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__      =							1_0_0_0
  
  # Size of the preloaded record batch in `Dataset.__iter__`
  UpperCAmelCase__      =							1_0
  
  # Pickling tables works only for small tables (<4GiB)
  # For big tables, we write them on disk instead
  UpperCAmelCase__      =							4 << 3_0
  
  # Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
  UpperCAmelCase__      =							'''500MB'''
  
  # Parquet configuration
  UpperCAmelCase__      =							1_0_0
  UpperCAmelCase__      =							1_0_0
  UpperCAmelCase__      =							1_0_0
  
  # Offline mode
  UpperCAmelCase__      =							os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES
  
  # In-memory
  UpperCAmelCase__      =							0  # Disabled
  UpperCAmelCase__      =							float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))
  
  # File names
  UpperCAmelCase__      =							'''dataset.arrow'''
  UpperCAmelCase__      =							'''indices.arrow'''
  UpperCAmelCase__      =							'''state.json'''
  UpperCAmelCase__      =							'''dataset_info.json'''
  UpperCAmelCase__      =							'''dataset_infos.json'''
  UpperCAmelCase__      =							'''LICENSE'''
  UpperCAmelCase__      =							'''metric_info.json'''
  UpperCAmelCase__      =							'''dataset_dict.json'''
  
  UpperCAmelCase__      =							'''datasets_modules'''
  
  UpperCAmelCase__      =							2_5_5
  
  # Streaming
  UpperCAmelCase__      =							2_0
  UpperCAmelCase__      =							5
  
  # Datasets without script
  UpperCAmelCase__      =							2_0_0
  UpperCAmelCase__      =							1_0
  UpperCAmelCase__      =							2_0_0
  
  # Progress bars
  UpperCAmelCase__      =							0.05  # 20 progress updates per sec
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import Any
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Any  )			->		int:
  						__A=  []
  						__A=  0
  			def __str__(     self       : Tuple  )			->		Dict:
  						__A=  '<' + str(self.stack  )[1:-1] + '>'
  						return printed
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
  						self.stack.append(lowerCAmelCase_  )
  						__A=  self.length + 1
  			def 				lowerCAmelCase    (     self       : int  )			->		Any:
  						self.rotate(1  )
  						__A=  self.stack[self.length - 1]
  						__A=  self.stack[:-1]
  						self.rotate(self.length - 1  )
  						__A=  self.length - 1
  						return dequeued
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
  						for _ in range(lowerCAmelCase_  ):
  									__A=  self.stack[0]
  									__A=  self.stack[1:]
  									self.put(lowerCAmelCase_  )
  									__A=  self.length - 1
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
  						__A=  self.get()
  						self.put(lowerCAmelCase_  )
  						self.rotate(self.length - 1  )
  						return front
  			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
  						return self.length
  
  
  
  