
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

__lowerCAmelCase					:			Optional[int]			      =		logging.get_logger(__name__)


@add_end_docstrings(lowerCAmelCase_      )
class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      def __init__(      self		:       Union[str, Any]				,       *_snake_case		:       Optional[Any]				,       **_snake_case		:       int				):
          super().__init__(*_snake_case				,       **_snake_case				)
          requires_backends(self				,       '''vision'''				)
          self.check_model_type(_snake_case				)




      def __call__(      self		:       str				,       _snake_case		:       Union[str, List[str], "Image.Image", List["Image.Image"]]				,       **_snake_case		:       Optional[int]				):
          return super().__call__(_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Union[str, Any]				,       **_snake_case		:       Union[str, Any]				):
          return {}, {}, {}




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       List[str]				):
          __lowercase		:			List[Any]	    =       load_image(_snake_case				)
          __lowercase		:			Dict	    =       image.size
          __lowercase		:			Tuple	    =       self.image_processor(images=_snake_case				,       return_tensors=self.framework				)
          return model_inputs




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       self.model(**_snake_case				)
          return model_outputs




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




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @property
      def 		snake_case_			(      self		:       Union[str, Any]				):
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       ort.SessionOptions()
          __lowercase		:			List[Any]	    =       False
          return options




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
          __lowercase		:			Optional[Any]	    =       load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''				)

          # using the PNDM scheduler by default
          __lowercase		:			Union[str, Any]	    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''				,       revision='''onnx'''				,       safety_checker=_snake_case				,       feature_extractor=_snake_case				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Tuple	    =       '''A red cat sitting on a park bench'''

          __lowercase		:			int	    =       np.random.RandomState(0				)
          __lowercase		:			int	    =       pipe(
              prompt=_snake_case				,       image=_snake_case				,       mask_image=_snake_case				,       strength=0.75				,       guidance_scale=7.5				,       num_inference_steps=15				,       generator=_snake_case				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image				).max() < 1E-2


