"""simple docstring"""





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
       import onnxruntime as ort


class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					List[Any]										=						"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"


      def 	_UpperCAmelCase (     self	,    a=0							) ->      Optional[Any]:
             lowercase__    :	Optional[int]     					=			floats_tensor((1, 3, 1_2_8, 1_2_8)	,    rng=random.Random(a							)							)
             lowercase__    :	List[Any]     					=			np.random.RandomState(a							)
             lowercase__    :	int     					=			{
                 'prompt': 'A painting of a squirrel eating a burger',
                 'image': image,
                 'generator': generator,
                 'num_inference_steps': 3,
                 'strength': 0.75,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,    provider='CPUExecutionProvider'							)
             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	int     					=			self.get_dummy_inputs()
             lowercase__    :	Optional[Any]     					=			pipe(**a							).images
             lowercase__    :	int     					=			image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 1_2_8, 1_2_8, 3)
             lowercase__    :	Union[str, Any]     					=			np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]							)
             assert np.abs(image_slice - expected_slice							).max() < 1e-1


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Union[str, Any]     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,    provider='CPUExecutionProvider'							)
             lowercase__    :	Optional[Any]     					=			PNDMScheduler.from_config(pipe.scheduler.config	,    skip_prk_steps=a							)
             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	List[Any]     					=			self.get_dummy_inputs()
             lowercase__    :	str     					=			pipe(**a							).images
             lowercase__    :	Union[str, Any]     					=			image[0, -3:, -3:, -1]

             assert image.shape == (1, 1_2_8, 1_2_8, 3)
             lowercase__    :	Union[str, Any]     					=			np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]							)

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


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	int     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,    provider='CPUExecutionProvider'							)
             lowercase__    :	int     					=			LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
             pipe.set_progress_bar_config(disable=a							)

             # warmup pass to apply optimizations
             lowercase__    :	str     					=			pipe(**self.get_dummy_inputs()							)

             lowercase__    :	Optional[int]     					=			self.get_dummy_inputs()
             lowercase__    :	int     					=			pipe(**a							).images
             lowercase__    :	str     					=			image[0, -3:, -3:, -1]

             assert image.shape == (1, 1_2_8, 1_2_8, 3)
             lowercase__    :	str     					=			np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]							)

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


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Union[str, Any]     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,    provider='CPUExecutionProvider'							)
             lowercase__    :	Optional[int]     					=			EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	List[str]     					=			self.get_dummy_inputs()
             lowercase__    :	Tuple     					=			pipe(**a							).images
             lowercase__    :	Union[str, Any]     					=			image[0, -3:, -3:, -1]

             assert image.shape == (1, 1_2_8, 1_2_8, 3)
             lowercase__    :	List[Any]     					=			np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]							)

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


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,    provider='CPUExecutionProvider'							)
             lowercase__    :	Optional[Any]     					=			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	str     					=			self.get_dummy_inputs()
             lowercase__    :	int     					=			pipe(**a							).images
             lowercase__    :	Union[str, Any]     					=			image[0, -3:, -3:, -1]

             assert image.shape == (1, 1_2_8, 1_2_8, 3)
             lowercase__    :	Optional[int]     					=			np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]							)

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


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Tuple     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,    provider='CPUExecutionProvider'							)
             lowercase__    :	Optional[Any]     					=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	Optional[Any]     					=			self.get_dummy_inputs()
             lowercase__    :	Tuple     					=			pipe(**a							).images
             lowercase__    :	Union[str, Any]     					=			image[0, -3:, -3:, -1]

             assert image.shape == (1, 1_2_8, 1_2_8, 3)
             lowercase__    :	List[Any]     					=			np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]							)

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





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


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return (
                 "CUDAExecutionProvider",
                 {
                     "gpu_mem_limit": "15000000000",  # 15GB
                     "arena_extend_strategy": "kSameAsRequested",
                 },
             )


      @property
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Tuple     					=			ort.SessionOptions()
             lowercase__    :	List[Any]     					=			False
             return options


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'							)
             lowercase__    :	Optional[int]     					=			init_image.resize((7_6_8, 5_1_2)							)
             # using the PNDM scheduler by default
             lowercase__    :	List[str]     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
                 'CompVis/stable-diffusion-v1-4'	,    revision='onnx'	,    safety_checker=a	,    feature_extractor=a	,    provider=self.gpu_provider	,    sess_options=self.gpu_options	,    )
             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	int     					=			'A fantasy landscape, trending on artstation'

             lowercase__    :	int     					=			np.random.RandomState(0							)
             lowercase__    :	str     					=			pipe(
                 prompt=a	,    image=a	,    strength=0.75	,    guidance_scale=7.5	,    num_inference_steps=1_0	,    generator=a	,    output_type='np'	,    )
             lowercase__    :	List[str]     					=			output.images
             lowercase__    :	List[Any]     					=			images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 7_6_8, 3)
             lowercase__    :	Optional[Any]     					=			np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]							)
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Dict     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/img2img/sketch-mountains-input.jpg'							)
             lowercase__    :	Optional[int]     					=			init_image.resize((7_6_8, 5_1_2)							)
             lowercase__    :	Optional[Any]     					=			LMSDiscreteScheduler.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    subfolder='scheduler'	,    revision='onnx'							)
             lowercase__    :	Optional[Any]     					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    revision='onnx'	,    scheduler=a	,    safety_checker=a	,    feature_extractor=a	,    provider=self.gpu_provider	,    sess_options=self.gpu_options	,    )
             pipe.set_progress_bar_config(disable=a							)

             lowercase__    :	str     					=			'A fantasy landscape, trending on artstation'

             lowercase__    :	Any     					=			np.random.RandomState(0							)
             lowercase__    :	Optional[int]     					=			pipe(
                 prompt=a	,    image=a	,    strength=0.75	,    guidance_scale=7.5	,    num_inference_steps=2_0	,    generator=a	,    output_type='np'	,    )
             lowercase__    :	str     					=			output.images
             lowercase__    :	Tuple     					=			images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

             assert images.shape == (1, 5_1_2, 7_6_8, 3)
             lowercase__    :	List[str]     					=			np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]							)
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


"""simple docstring"""





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image
else:


       class   UpperCAmelCase_			:


             @staticmethod
             def 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			hashlib.mda(image.tobytes()  )
       return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			DepthEstimationPipeline(model=a	,    image_processor=a							)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[Any]     					=			depth_estimator(
                 [
                     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
                     'http://images.cocodataset.org/val2017/000000039769.jpg',
                     # RGBA
                     dataset[0]['file'],
                     # LA
                     dataset[1]['file'],
                     # L
                     dataset[2]['file'],
                 ]							)
             self.assertEqual(
                 [
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                 ]	,    a	,    )


      @require_tf
      @unittest.skip('Depth estimation is not implemented in TF'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	Optional[Any]     					=			hashimage(outputs['depth']							)

             # This seems flaky.
             # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
             self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)	,    29.304							)
             self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)	,    2.662							)


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # This is highly irregular to have no small tests.
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)

