






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_   : Optional[Any]   			= {
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Optional[Any]   			= ["""PerceiverFeatureExtractor"""]
  UpperCAmelCase_   : List[str]   			= ["""PerceiverImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Union[str, Any]   			= [
      """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PerceiverForImageClassificationConvProcessing""",
      """PerceiverForImageClassificationFourier""",
      """PerceiverForImageClassificationLearned""",
      """PerceiverForMaskedLM""",
      """PerceiverForMultimodalAutoencoding""",
      """PerceiverForOpticalFlow""",
      """PerceiverForSequenceClassification""",
      """PerceiverLayer""",
      """PerceiverModel""",
      """PerceiverPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
  from .tokenization_perceiver import PerceiverTokenizer

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_perceiver import PerceiverFeatureExtractor
    from .image_processing_perceiver import PerceiverImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_perceiver import (
        PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PerceiverForImageClassificationConvProcessing,
        PerceiverForImageClassificationFourier,
        PerceiverForImageClassificationLearned,
        PerceiverForMaskedLM,
        PerceiverForMultimodalAutoencoding,
        PerceiverForOpticalFlow,
        PerceiverForSequenceClassification,
        PerceiverLayer,
        PerceiverModel,
        PerceiverPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_   : Dict   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




import random






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= [], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase_		)
										elif element > pivot:
															greater.append(UpperCamelCase_		)
										else:
															equal.append(UpperCamelCase_		)
					return less, equal, greater






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					if index >= len(UpperCamelCase_		) or index < 0:
										return None

					SCREAMING_SNAKE_CASE__													= items[random.randint(0	,	len(UpperCamelCase_		) - 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= _partition(UpperCamelCase_	,	UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase_	,	UpperCamelCase_		)
					# must be in larger
					else:
										return quick_select(UpperCamelCase_	,	index - (m + count)		)





