
"""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

UpperCAmelCase__   :					Union[str, Any]												=				logging.get_logger(__name__)



@add_end_docstrings(_SCREAMING_SNAKE_CASE						)
class 			lowerCAmelCase_		(_SCREAMING_SNAKE_CASE						):


    """simple docstring"""



    def __init__(self     , *SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	Tuple:



           """simple docstring"""




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



    def __call__(self     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	Dict:



           """simple docstring"""




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



    def 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	Dict:



           """simple docstring"""




           return {}, {}, {}



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     load_image(A_					)
           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     image.size
           SCREAMING_SNAKE_CASE__		: Any     =     self.image_processor(images=A_     , return_tensors=self.framework					)
           return model_inputs



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: str     =     self.model(**A_					)
           return model_outputs



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: int     =     model_outputs.predicted_depth
           SCREAMING_SNAKE_CASE__		: Optional[Any]     =     torch.nn.functional.interpolate(
               predicted_depth.unsqueeze(1					)     , size=self.image_size[::-1]     , mode="""bicubic"""     , align_corners=A_					)
           SCREAMING_SNAKE_CASE__		: List[Any]     =     prediction.squeeze().cpu().numpy()
           SCREAMING_SNAKE_CASE__		: Dict     =     (output * 2_55 / np.max(A_					)).astype("""uint8"""					)
           SCREAMING_SNAKE_CASE__		: List[Any]     =     Image.fromarray(A_					)
           SCREAMING_SNAKE_CASE__		: Optional[int]     =     {}
           SCREAMING_SNAKE_CASE__		: List[str]     =     predicted_depth
           SCREAMING_SNAKE_CASE__		: List[Any]     =     depth
           return output_dict






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


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

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




