


from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Any    =							''
						SCREAMING_SNAKE_CASE__					:     Optional[Any]    =							'hf-legacy'  # "hf://"" is reserved for hffs




						def __init__( self      ,  __lowercase = None      ,  __lowercase = None      ,  **__lowercase      ,  )    ->			int:
										super().__init__(self      ,  **SCREAMING_SNAKE_CASE_							)
										lowerCAmelCase_       :					Dict			 = repo_info
										lowerCAmelCase_       :					Union[str, Any]			 = token
										lowerCAmelCase_       :					str			 = None




						def   lowercase_       ( self							)    ->			Optional[Any]:
										if self.dir_cache is None:
														lowerCAmelCase_       :					List[str]			 = {}
														for hf_file in self.repo_info.siblings:
																		# TODO(QL): add sizes
																		lowerCAmelCase_       :					Tuple			 = {
																		    '''name''': hf_file.rfilename,
																		    '''size''': None,
																		    '''type''': '''file''',
																		}
																		self.dir_cache.update(
																		    {
																		        str(SCREAMING_SNAKE_CASE_							): {'''name''': str(SCREAMING_SNAKE_CASE_							), '''size''': None, '''type''': '''directory'''}
																		        for d in list(PurePosixPath(hf_file.rfilename							).parents							)[:-1]
																		    }							)




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase = "rb"      ,  **__lowercase      ,  )    ->			Optional[Any]:
										if not isinstance(self.repo_info      ,  SCREAMING_SNAKE_CASE_							):
														raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""							)
										lowerCAmelCase_       :					int			 = hf_hub_url(self.repo_info.id      ,  SCREAMING_SNAKE_CASE_      ,  revision=self.repo_info.sha							)
										return fsspec.open(
										    SCREAMING_SNAKE_CASE_      ,  mode=SCREAMING_SNAKE_CASE_      ,  headers=get_authentication_headers_for_url(SCREAMING_SNAKE_CASE_      ,  use_auth_token=self.token							)      ,  client_kwargs={'''trust_env''': True}      ,  ).open()




						def   lowercase_       ( self      ,  __lowercase      ,  **__lowercase							)    ->			int:
										self._get_dirs()
										lowerCAmelCase_       :					Tuple			 = self._strip_protocol(SCREAMING_SNAKE_CASE_							)
										if path in self.dir_cache:
														return self.dir_cache[path]
										else:
														raise FileNotFoundError(SCREAMING_SNAKE_CASE_							)




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase=False      ,  **__lowercase							)    ->			Optional[int]:
										self._get_dirs()
										lowerCAmelCase_       :					Optional[Any]			 = PurePosixPath(path.strip('''/'''							)							)
										lowerCAmelCase_       :					List[str]			 = {}
										for p, f in self.dir_cache.items():
														lowerCAmelCase_       :					Dict			 = PurePosixPath(p.strip('''/'''							)							)
														lowerCAmelCase_       :					List[Any]			 = p.parent
														if root == path:
																		lowerCAmelCase_       :					int			 = f
										lowerCAmelCase_       :					Optional[Any]			 = list(paths.values()							)
										if detail:
														return out
										else:
														return sorted(f['''name'''] for f in out							)

from typing import TYPE_CHECKING

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


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




