




'''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

_SCREAMING_SNAKE_CASE							:       List[Any]						=			logging.get_logger(__name__)




@add_end_docstrings(lowercase_							)
class 			_snake_case		(     lowercase_							):




   def __init__( self    ,							*a__    ,							**a__							)    ->    str:

          '''simple docstring'''




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




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

          '''simple docstring'''




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




   def        lowerCAmelCase__    ( self    ,							**a__							)    ->    List[str]:

          '''simple docstring'''




          return {}, {}, {}




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    str:

          '''simple docstring'''




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




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    Any:

          '''simple docstring'''




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




   def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[Any]:

          '''simple docstring'''




          snake_case_      =				model_outputs.predicted_depth
          snake_case_      =				torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1							)    ,							size=self.image_size[::-1]    ,							mode="bicubic"    ,							align_corners=a__							)
          snake_case_      =				prediction.squeeze().cpu().numpy()
          snake_case_      =				(output * 255 / np.max(a__							)).astype("uint8"							)
          snake_case_      =				Image.fromarray(a__							)
          snake_case_      =				{}
          snake_case_      =				predicted_depth
          snake_case_      =				depth
          return output_dict










'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




@slow
@require_torch_gpu
class 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
										snake_case_      =				torch.manual_seed(0							)
										snake_case_      =				pipe(
										    image=a__    ,							generator=a__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type="numpy"    ,							).images

										snake_case_      =				image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




