


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 : List[str]		=				logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__				)
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

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






					def __call__(				self ,       snake_case_ ,       **snake_case_						)   ->			Any:







								'''simple docstring'''

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






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								return {}, {}, {}






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   load_image(snake_case_						)
								__lowercase 					=   image.size
								__lowercase 					=   self.image_processor(images=snake_case_ ,       return_tensors=self.framework						)
								return model_inputs






					def A      (				self ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   self.model(**snake_case_						)
								return model_outputs






					def A      (				self ,       snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   model_outputs.predicted_depth
								__lowercase 					=   torch.nn.functional.interpolate(
								    predicted_depth.unsqueeze(1						) ,       size=self.image_size[::-1] ,       mode='''bicubic''' ,       align_corners=snake_case_						)
								__lowercase 					=   prediction.squeeze().cpu().numpy()
								__lowercase 					=   (output * 2_5_5 / np.max(snake_case_						)).astype('''uint8'''						)
								__lowercase 					=   Image.fromarray(snake_case_						)
								__lowercase 					=   {}
								__lowercase 					=   predicted_depth
								__lowercase 					=   depth
								return output_dict






# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)


