



from typing import TYPE_CHECKING

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


_UpperCAmelCase	       =	{"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase	       =	["""GLPNFeatureExtractor"""]
    _UpperCAmelCase	       =	["""GLPNImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase	       =	[
        """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GLPNForDepthEstimation""",
        """GLPNLayer""",
        """GLPNModel""",
        """GLPNPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_glpn import GLPNFeatureExtractor
        from .image_processing_glpn import GLPNImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_glpn import (
            GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
            GLPNForDepthEstimation,
            GLPNLayer,
            GLPNModel,
            GLPNPreTrainedModel,
        )


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	)



