


"""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 snake_case      :







				@staticmethod
				def  a_ (					*a__      :       Tuple    ,   **a__      :       List[str]		)       ->						Tuple:





								'''simple docstring'''







								pass






def      a__ (				__lowercase   )	->	str:
				_A					  =			hashlib.mda(image.tobytes()   )
				return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class snake_case      (  unittest.TestCase):
		__UpperCamelCase           =			MODEL_FOR_DEPTH_ESTIMATION_MAPPING







		def  a_ (					self      :       Dict    ,   a__      :       Tuple    ,   a__      :       Optional[int]    ,   a__      :       str		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			DepthEstimationPipeline(model=a__    ,   image_processor=a__		)
						return depth_estimator, [
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						]







		def  a_ (					self      :       Tuple    ,   a__      :       Optional[int]    ,   a__      :       Dict		)       ->						int:





						'''simple docstring'''







						_A					  =			depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
						self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}    ,   a__		)
						import datasets

						_A					  =			datasets.load_dataset("hf-internal-testing/fixtures_image_utils"    ,   "image"    ,   split="test"		)
						_A					  =			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  a_ (					self      :       List[str]		)       ->						Optional[int]:





						'''simple docstring'''







						pass







		@slow
		@require_torch
		def  a_ (					self      :       Optional[Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			"Intel/dpt-large"
						_A					  =			pipeline("depth-estimation"    ,   model=a__		)
						_A					  =			depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
						_A					  =			hashimage(outputs["depth"]		)

						# This seems flaky.
						# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
						self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)    ,   29.3_04		)
						self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)    ,   2.6_6_2		)







		@require_torch
		def  a_ (					self      :       Optional[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)



"""simple docstring"""


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_       							=    False
class snake_case      (  unittest.TestCase):
		pass





@nightly
@require_torch_gpu
class snake_case      (  unittest.TestCase):







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







		def  a_ (					self      :       Tuple		)       ->						Any:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a__		)
										_A					  =			VersatileDiffusionPipeline.from_pretrained(a__    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			generator.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt="first prompt"    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type="numpy"    ,   ).images

						assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"







		def  a_ (					self      :       Optional[int]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,   torch_dtype=torch.floataa		)
						pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"cyberpunk 2077"
						_A					  =			load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.dual_guided(
						    prompt=a__    ,   image=a__    ,   text_to_image_strength=0.7_5    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"    ,   ).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]		)

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

						_A					  =			"A painting of a squirrel eating a burger "
						_A					  =			torch.manual_seed(0		)
						_A					  =			pipe.text_to_image(
						    prompt=a__    ,   generator=a__    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]		)

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

						_A					  =			pipe.image_variation(a__    ,   generator=a__    ,   output_type="numpy"		).images

						_A					  =			image[0, 2_53:2_56, 2_53:2_56, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						_A					  =			np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]		)

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