



"""simple docstring"""

import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


lowercase__    :  List[Any]							    =	importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
     from .safilesystem import SaFileSystem  # noqa: F401

lowercase__    :  List[compression.BaseCompressedFileFileSystem]							    =	[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
     if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
          warnings.warn(f'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
     fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)




def __lowercase			(							_a    ):
    if "://" in dataset_path:
        snake_case_ :     str				=	dataset_path.split('''://'''    )[1]
    return dataset_path




def __lowercase			(							_a    ):
    if fs is not None and fs.protocol != "file":
        return True
    else:
        return False




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Union[str, Any]				=	not is_remote_filesystem(_a    )
    if is_local:
        # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
        shutil.move(fs._strip_protocol(_a    )		,			fs._strip_protocol(_a    )    )
    else:
        fs.mv(_a		,			_a		,			recursive=_a    )






def __lowercase			(							):
    if hasattr(fsspec.asyn		,			'''reset_lock'''    ):
        # for future fsspec>2022.05.0
        fsspec.asyn.reset_lock()
    else:
        snake_case_ :     Any				=	None
        snake_case_ :     Tuple				=	None
        snake_case_ :     Optional[Any]				=	threading.Lock()










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__    :  List[Any]							    =	{
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Any							    =	['''DistilBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  int							    =	[
         '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DistilBertForMaskedLM''',
         '''DistilBertForMultipleChoice''',
         '''DistilBertForQuestionAnswering''',
         '''DistilBertForSequenceClassification''',
         '''DistilBertForTokenClassification''',
         '''DistilBertModel''',
         '''DistilBertPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Dict							    =	[
         '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFDistilBertForMaskedLM''',
         '''TFDistilBertForMultipleChoice''',
         '''TFDistilBertForQuestionAnswering''',
         '''TFDistilBertForSequenceClassification''',
         '''TFDistilBertForTokenClassification''',
         '''TFDistilBertMainLayer''',
         '''TFDistilBertModel''',
         '''TFDistilBertPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Tuple							    =	[
         '''FlaxDistilBertForMaskedLM''',
         '''FlaxDistilBertForMultipleChoice''',
         '''FlaxDistilBertForQuestionAnswering''',
         '''FlaxDistilBertForSequenceClassification''',
         '''FlaxDistilBertForTokenClassification''',
         '''FlaxDistilBertModel''',
         '''FlaxDistilBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

     lowercase__    :  Union[str, Any]							    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





