



'''simple docstring'''




from typing import TYPE_CHECKING

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


a   :			Dict					  =    {
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a   :			Dict					  =    ["""PerceiverFeatureExtractor"""]
							a   :			str					  =    ["""PerceiverImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a   :			Optional[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

							a   :			Tuple					  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import functools



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[int]       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   len(_lowerCAmelCase     )

 @functools.cache
 def min_distance(_lowerCAmelCase							,   _lowerCAmelCase     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase	:					Union[str, Any]       	=   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1							,   _lowerCAmelCase     )							,   1 + min_distance(_lowerCAmelCase							,   indexa + 1     )							,   diff + min_distance(indexa + 1							,   indexa + 1     )							,   )

 return min_distance(0							,   0     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
