





"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCamelCase_	 ={"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""ViltFeatureExtractor"""]
						UpperCamelCase_	 =["""ViltImageProcessor"""]
						UpperCamelCase_	 =["""ViltProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ViltForImageAndTextRetrieval""",
						    """ViltForImagesAndTextClassification""",
						    """ViltForTokenClassification""",
						    """ViltForMaskedLM""",
						    """ViltForQuestionAnswering""",
						    """ViltLayer""",
						    """ViltModel""",
						    """ViltPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_vilt import ViltFeatureExtractor
												from .image_processing_vilt import ViltImageProcessor
												from .processing_vilt import ViltProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vilt import (
												    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViltForImageAndTextRetrieval,
												    ViltForImagesAndTextClassification,
												    ViltForMaskedLM,
												    ViltForQuestionAnswering,
												    ViltForTokenClassification,
												    ViltLayer,
												    ViltModel,
												    ViltPreTrainedModel,
												)


else:
						import sys

						UpperCamelCase_	 =_LazyModule(__name__, globals()["""__file__"""], _import_structure)










"""simple docstring"""




def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   List[str]      =       []
						_UpperCamelCase     :   Union[str, Any]      =       1

						for i in range(len(_lowercase  )  ):
												if i + 1 < len(_lowercase  ) and text[i] == text[i + 1]:
																		count += 1
												else:
																		encoded.append((text[i], count)  )
																		_UpperCamelCase     :   int      =       1

						return encoded





def 			a_							(				_lowercase  ):
						return "".join(char * length for char, length in encoded  )


if __name__ == "__main__":
						from doctest import testmod

						testmod(name="""run_length_encode""", verbose=True)
						testmod(name="""run_length_decode""", verbose=True)



