
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

lowercase_ 				=			logging.get_logger(__name__)





@add_end_docstrings(lowerCAmelCase__       )
class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     def __init__(				self							:							str     ,						*_A							:							Dict     ,						**_A							:							Tuple ):







       """simple docstring"""




       super().__init__(*_A     ,						**_A )
       requires_backends(self     ,						'''vision''' )
       self.check_model_type(_A )



     def __call__(				self							:							Dict     ,						_A							:							Union[str, List[str], "Image.Image", List["Image.Image"]]     ,						**_A							:							int ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						**_A							:							List[Any] ):







       """simple docstring"""




       return {}, {}, {}



     def  UpperCAmelCase__							(				self							:							Optional[int]     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		load_image(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		image.size
       __SCREAMING_SNAKE_CASE      :	Any					=		self.image_processor(images=_A     ,						return_tensors=self.framework )
       return model_inputs



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		self.model(**_A )
       return model_outputs




     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							Optional[Any] ):







       """simple docstring"""




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


def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



