

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a_         =      get_logger(__name__)

# Datasets
a_         =      'https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets'
a_         =      'https://cdn-datasets.huggingface.co/datasets/datasets'
a_         =      'https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}'

# Metrics
a_         =      'https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics'
a_         =      'https://cdn-datasets.huggingface.co/datasets/metric'
a_         =      'https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}'

# Hub
a_         =      os.environ.get('HF_ENDPOINT', 'https://huggingface.co')
a_         =      HF_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
a_         =      'main'

a_         =      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_         =      {'1', 'ON', 'YES', 'TRUE'}
a_         =      ENV_VARS_TRUE_VALUES.union({'AUTO'})


# Imports
a_         =      version.parse(importlib_metadata.version('dill'))
a_         =      version.parse(importlib_metadata.version('pandas'))
a_         =      version.parse(importlib_metadata.version('pyarrow'))

a_         =      os.environ.get('USE_TF', 'AUTO').upper()
a_         =      os.environ.get('USE_TORCH', 'AUTO').upper()
a_         =      os.environ.get('USE_JAX', 'AUTO').upper()

a_         =      'N/A'
a_         =      False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
			a_         =      importlib.util.find_spec('torch') is not None
			if TORCH_AVAILABLE:
						try:
									a_         =      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_         =      'N/A'
a_         =      False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
			a_         =      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_         =      version.parse(importlib_metadata.version(package))
									except importlib_metadata.PackageNotFoundError:
												continue
									else:
												break
						else:
									a_         =      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_         =      False
						else:
									logger.info(F'''TensorFlow version {TF_VERSION} available.''')
else:
			logger.info('Disabling Tensorflow because USE_TORCH is set')


a_         =      'N/A'
a_         =      False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
			a_         =      importlib.util.find_spec('jax') is not None and importlib.util.find_spec('jaxlib') is not None
			if JAX_AVAILABLE:
						try:
									a_         =      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_         =      os.environ.get('USE_BEAM', 'AUTO').upper()
a_         =      'N/A'
a_         =      False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
			try:
						a_         =      version.parse(importlib_metadata.version('apache_beam'))
						a_         =      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_         =      importlib.util.find_spec('sqlalchemy') is not None

# Optional tools for feature decoding
a_         =      importlib.util.find_spec('PIL') is not None
a_         =      importlib.util.find_spec('soundfile') is not None and version.parse(
    importlib.import_module('soundfile').__libsndfile_version__
) >= version.parse('1.0.31')
a_         =      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_         =      importlib.util.find_spec('rarfile') is not None
a_         =      importlib.util.find_spec('zstandard') is not None
a_         =      importlib.util.find_spec('lz4') is not None
a_         =      importlib.util.find_spec('py7zr') is not None


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

a_         =      os.path.join(HF_CACHE_HOME, 'datasets')
a_         =      Path(os.getenv('HF_DATASETS_CACHE', DEFAULT_HF_DATASETS_CACHE))

a_         =      os.path.join(HF_CACHE_HOME, 'metrics')
a_         =      Path(os.getenv('HF_METRICS_CACHE', DEFAULT_HF_METRICS_CACHE))

a_         =      os.path.join(HF_CACHE_HOME, 'modules')
a_         =      Path(os.getenv('HF_MODULES_CACHE', DEFAULT_HF_MODULES_CACHE))

a_         =      'downloads'
a_         =      os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
a_         =      Path(os.getenv('HF_DATASETS_DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))

a_         =      'extracted'
a_         =      os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
a_         =      Path(os.getenv('HF_DATASETS_EXTRACTED_DATASETS_PATH', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
a_         =      (
    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_         =      1000

# Size of the preloaded record batch in `Dataset.__iter__`
a_         =      10

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

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

# Parquet configuration
a_         =      100
a_         =      100
a_         =      100

# Offline mode
a_         =      os.environ.get('HF_DATASETS_OFFLINE', 'AUTO').upper() in ENV_VARS_TRUE_VALUES

# In-memory
a_         =      0  # Disabled
a_         =      float(os.environ.get('HF_DATASETS_IN_MEMORY_MAX_SIZE', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
a_         =      'dataset.arrow'
a_         =      'indices.arrow'
a_         =      'state.json'
a_         =      'dataset_info.json'
a_         =      'dataset_infos.json'
a_         =      'LICENSE'
a_         =      'metric_info.json'
a_         =      'dataset_dict.json'

a_         =      'datasets_modules'

a_         =      255

# Streaming
a_         =      20
a_         =      5

# Datasets without script
a_         =      200
a_         =      10
a_         =      200

# Progress bars
a_         =      0.05  # 20 progress updates per sec


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_         =      logging.get_logger(__name__)

a_         =      {
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='mpnet'
			def __init__(     self							:	Optional[int] , a							:	Dict=3_0527 , a							:	Optional[int]=768 , a							:	List[str]=12 , a							:	List[str]=12 , a							:	str=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Optional[int]=0.1 , a							:	Optional[Any]=512 , a							:	List[Any]=0.02 , a							:	str=1e-12 , a							:	Optional[Any]=32 , a							:	int=1 , a							:	Union[str, Any]=0 , a							:	List[str]=2 , **a							:	Optional[int] , )							->    Union[str, Any]:

				"""simple docstring"""

				super().__init__(pad_token_id=a , bos_token_id=a , eos_token_id=a , **a		)

				SCREAMING_SNAKE_CASE				: Tuple			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_size
				SCREAMING_SNAKE_CASE				: int			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Any			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Tuple			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[str]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Dict			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Any			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    relative_attention_num_buckets