






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_UpperCamelCase			:	Tuple								=logging.get_logger(__name__)






@add_end_docstrings(__snake_case							)
class 						UpperCAmelCase__ (							__snake_case							):







		def __init__(					self       ,*A__       ,**A__		):
						super().__init__(*A__       ,**A__		)
						requires_backends(self       ,'''vision'''		)
						self.check_model_type(A__		)







		def __call__(					self       ,A__       ,**A__		):
						return super().__call__(A__       ,**A__		)







		def  A__      (					self       ,**A__		):
						return {}, {}, {}







		def  A__      (					self       ,A__		):
						_A	:				Any   =      load_image(A__		)
						_A	:				List[Any]   =      image.size
						_A	:				Union[str, Any]   =      self.image_processor(images=A__       ,return_tensors=self.framework		)
						return model_inputs







		def  A__      (					self       ,A__		):
						_A	:				Any   =      self.model(**A__		)
						return model_outputs







		def  A__      (					self       ,A__		):
						_A	:				int   =      model_outputs.predicted_depth
						_A	:				int   =      torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1		)       ,size=self.image_size[::-1]       ,mode='''bicubic'''       ,align_corners=A__		)
						_A	:				Union[str, Any]   =      prediction.squeeze().cpu().numpy()
						_A	:				List[str]   =      (output * 255 / np.max(A__		)).astype('''uint8'''		)
						_A	:				List[Any]   =      Image.fromarray(A__		)
						_A	:				Dict   =      {}
						_A	:				Tuple   =      predicted_depth
						_A	:				Tuple   =      depth
						return output_dict











def  a__ (__lowercase							:int					,  __lowercase							:int   )		-> int:
				return abs(__lowercase   ) if a == 0 else greatest_common_divisor(b % a					,  __lowercase   )



def  a__ (__lowercase							:int					,  __lowercase							:int   )		-> int:
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								_A  ,				_A	:				Union[str, Any]   =      y, x % y
				return abs(__lowercase   )



def  a__ ()		-> Optional[Any]:
				try:
								_A	:				Optional[Any]   =      input('''Enter two integers separated by comma (,): '''   ).split(''','''   )
								_A	:				List[Any]   =      int(nums[0]   )
								_A	:				Optional[int]   =      int(nums[1]   )
								print(
								    f"""greatest_common_divisor({num_a}, {num_a}) = """
								    f"""{greatest_common_divisor(__lowercase					,  __lowercase   )}"""   )
								print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowercase					,  __lowercase   )}"""   )
				except (IndexError, UnboundLocalError, ValueError):
								print('''Wrong input'''   )


if __name__ == "__main__":
	main()



