



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	       =	{'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase	       =	[
        '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ViTMSNModel''',
        '''ViTMSNForImageClassification''',
        '''ViTMSNPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_msn import (
            VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMSNForImageClassification,
            ViTMSNModel,
            ViTMSNPreTrainedModel,
        )

else:
    import sys

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








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = "arrow",      **SCREAMING_SNAKE_CASE_,      )					->		int:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      keep_in_memory=SCREAMING_SNAKE_CASE_,      streaming=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Any						     =     load_from_cache_file
          UpperCamelCase   :  Any						     =     file_format
          UpperCamelCase   :  str						     =     Spark(
              df=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      working_dir=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






       def  snake_case_	(      self	)					->		Tuple:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split	)
          UpperCamelCase   :  Dict						     =     None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,      file_format=self._file_format,      )
          return self.builder.as_dataset(split=self.split	)



