

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				:
						'''simple docstring'''







						@staticmethod
						def 				__UpperCamelCase			(      *a				:			Any   ,   **a				:			str						)				->							Any:

							"""simple docstring"""

							pass

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   str					  =							hashlib.mda(image.tobytes())
	return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MODEL_FOR_DEPTH_ESTIMATION_MAPPING
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			int   ,   a				:			Any   ,   a				:			Any						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[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				:			int   ,   a				:			List[str]   ,   a				:			List[Any]						)				->							str:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE :   Tuple					  =							datasets.load_dataset("hf-internal-testing/fixtures_image_utils"   ,   "image"   ,   split="test"						)
				SCREAMING_SNAKE_CASE :   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				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				pass
			@slow
			@require_torch
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"Intel/dpt-large"
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							pipeline("depth-estimation"   ,   model=a						)
				SCREAMING_SNAKE_CASE :   str					  =							depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				SCREAMING_SNAKE_CASE :   Union[str, 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				:			Tuple						)				->							Union[str, Any]:

				"""simple docstring"""

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


import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							UNetaDModel(
				    block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")   ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")   ,   )
				return model






			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

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






@slow
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

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