





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase        =				{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase        =				['ConditionalDetrFeatureExtractor']
     _UpperCAmelCase        =				['ConditionalDetrImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase        =				[
         'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConditionalDetrForObjectDetection',
         'ConditionalDetrForSegmentation',
         'ConditionalDetrModel',
         'ConditionalDetrPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_conditional_detr import (
         CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConditionalDetrConfig,
         ConditionalDetrOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
          from .image_processing_conditional_detr import ConditionalDetrImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_conditional_detr import (
              CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConditionalDetrForObjectDetection,
              ConditionalDetrForSegmentation,
              ConditionalDetrModel,
              ConditionalDetrPreTrainedModel,
          )

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 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: pyspark.sql.DataFrame					,   _SCREAMING_SNAKE_CASE: Optional[NamedSplit] = None					,   _SCREAMING_SNAKE_CASE: Optional[Features] = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: str = None					,   _SCREAMING_SNAKE_CASE: bool = False					,   _SCREAMING_SNAKE_CASE: str = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: str = "arrow"					,   **_SCREAMING_SNAKE_CASE: Union[str, Any]					,   )       -> Any:




              """simple docstring"""


              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_          =  load_from_cache_file
              UpperCamelCase_          =  file_format
              UpperCamelCase_          =  Spark(
                  df=_SCREAMING_SNAKE_CASE					,   features=_SCREAMING_SNAKE_CASE					,   cache_dir=_SCREAMING_SNAKE_CASE					,   working_dir=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )



       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


              if self.streaming:
                     return self.builder.as_streaming_dataset(split=self.split		)
              UpperCamelCase_          =  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		)

