


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      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Optional[int]							      =    """"""
							__lowercase:		Union[str, Any]							      =    """hf-legacy"""  # "hf://"" is reserved for hffs



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      Optional[DatasetInfo] = None		,       UpperCAmelCase_							:      Optional[str] = None		,       **UpperCAmelCase_							:      str		,       )					->List[Any]:



														"""simple docstring"""



														super().__init__(self		,       **UpperCAmelCase_       )
														snake_case_           =		repo_info
														snake_case_           =		token
														snake_case_           =		None



							def 		lowerCAmelCase       (				self							:      int       )					->List[Any]:



														"""simple docstring"""



														if self.dir_cache is None:
																					snake_case_           =		{}
																					for hf_file in self.repo_info.siblings:
																												# TODO(QL): add sizes
																												snake_case_           =		{
																												    """name""": hf_file.rfilename,
																												    """size""": None,
																												    """type""": """file""",
																												}
																												self.dir_cache.update(
																												    {
																												        str(UpperCAmelCase_       ): {"""name""": str(UpperCAmelCase_       ), """size""": None, """type""": """directory"""}
																												        for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
																												    }       )



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str = "rb"		,       **UpperCAmelCase_							:      List[Any]		,       )					->Union[str, Any]:



														"""simple docstring"""



														if not isinstance(self.repo_info		,       UpperCAmelCase_       ):
																					raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""       )
														snake_case_           =		hf_hub_url(self.repo_info.id		,       UpperCAmelCase_		,       revision=self.repo_info.sha       )
														return fsspec.open(
														    UpperCAmelCase_		,       mode=UpperCAmelCase_		,       headers=get_authentication_headers_for_url(UpperCAmelCase_		,       use_auth_token=self.token       )		,       client_kwargs={"""trust_env""": True}		,       ).open()



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Tuple		,       **UpperCAmelCase_							:      List[Any]       )					->Dict:



														"""simple docstring"""



														self._get_dirs()
														snake_case_           =		self._strip_protocol(UpperCAmelCase_       )
														if path in self.dir_cache:
																					return self.dir_cache[path]
														else:
																					raise FileNotFoundError(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Union[str, Any]=False		,       **UpperCAmelCase_							:      Dict       )					->List[str]:



														"""simple docstring"""



														self._get_dirs()
														snake_case_           =		PurePosixPath(path.strip("""/"""       )       )
														snake_case_           =		{}
														for p, f in self.dir_cache.items():
																					snake_case_           =		PurePosixPath(p.strip("""/"""       )       )
																					snake_case_           =		p.parent
																					if root == path:
																												snake_case_           =		f
														snake_case_           =		list(paths.values()       )
														if detail:
																					return out
														else:
																					return sorted(f["""name"""] for f in out       )







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    [
				    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegatronBertForCausalLM',
				    'MegatronBertForMaskedLM',
				    'MegatronBertForMultipleChoice',
				    'MegatronBertForNextSentencePrediction',
				    'MegatronBertForPreTraining',
				    'MegatronBertForQuestionAnswering',
				    'MegatronBertForSequenceClassification',
				    'MegatronBertForTokenClassification',
				    'MegatronBertModel',
				    'MegatronBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_megatron_bert import (
								    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegatronBertForCausalLM,
								    MegatronBertForMaskedLM,
								    MegatronBertForMultipleChoice,
								    MegatronBertForNextSentencePrediction,
								    MegatronBertForPreTraining,
								    MegatronBertForQuestionAnswering,
								    MegatronBertForSequenceClassification,
								    MegatronBertForTokenClassification,
								    MegatronBertModel,
								    MegatronBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    int		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



