






"""simple docstring"""



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

A_    =							logging.get_logger(__name__)






@add_end_docstrings(UpperCamelCase		)
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						List[Any]			,    *snake_case      :						str			,    **snake_case      :						List[str]							):
									'''simple docstring'''





									super().__init__(*snake_case			,    **snake_case							)
									requires_backends(self			,    """vision"""							)
									self.check_model_type(snake_case							)


							def __call__(			self      :						Dict			,    snake_case      :						Union[str, List[str], "Image.Image", List["Image.Image"]]			,    **snake_case      :						Optional[Any]							):
									'''simple docstring'''





									return super().__call__(snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    **snake_case      :						Tuple							):
									'''simple docstring'''





									return {}, {}, {}


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						List[Any]							):
									'''simple docstring'''





									A__						:  List[str]		  =  load_image(snake_case							)
									A__						:  str		  =  image.size
									A__						:  int		  =  self.image_processor(images=snake_case			,    return_tensors=self.framework							)
									return model_inputs


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Union[str, Any]							):
									'''simple docstring'''





									A__						:  str		  =  self.model(**snake_case							)
									return model_outputs


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Dict							):
									'''simple docstring'''





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














"""simple docstring"""



import baseaa




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->bytes:
						return baseaa.baaencode(string.encode("""utf-8"""      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					bytes      )		->str:
						return baseaa.baadecode(UpperCAmelCase__      ).decode("""utf-8"""      )


if __name__ == "__main__":
						A_    =							'''Hello World!'''
						A_    =							baseaa_encode(test)
						print(encoded)

						A_    =							baseaa_decode(encoded)
						print(decoded)






