
'''simple docstring'''



def A (__lowerCamelCase :Any					,      __lowerCamelCase :str					,      __lowerCamelCase :List[Any]					,      __lowerCamelCase :int					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Any						):
						if index == r:
												for j in range(__lowerCamelCase						):
																		print(data[j]					,      end=""" """						)
												print(""" """						)
												return
						#  When no more elements are there to put in data[]
						if i >= n:
												return
						# current is included, put next at next location
						_lowerCAmelCase    		=					arr[i]
						combination_util(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      index + 1					,      __lowerCamelCase					,      i + 1						)
						# current is excluded, replace it with
						# next (Note that i+1 is passed, but
						# index is not changed)
						combination_util(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      i + 1						)
						# The main function that prints all combinations
						# of size r in arr[] of size n. This function
						# mainly uses combinationUtil()


def A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :str					,      __lowerCamelCase :int						):
						# A temporary array to store all combination one by one
						_lowerCAmelCase    		=					[0] * r
						# Print all combination using temporary array 'data[]'
						combination_util(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      0					,      __lowerCamelCase					,      0						)


if __name__ == "__main__":
						# Driver code to check the function above
						_lowercase          =						[10, 20, 30, 40, 50]
						print_combination(arr, len(arr), 3)
						# This code is contributed by Ambuj sahu


'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
