
"""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 __lowerCAmelCase					:




					"""simple docstring"""







					@staticmethod
					def   lowerCamelCase__  (					*_snake_case    :       Dict							, **_snake_case    :       Any						)   ->			int:







										"""simple docstring"""
										pass
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    hashlib.mda(image.tobytes()					)
					return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					MODEL_FOR_DEPTH_ESTIMATION_MAPPING




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Any							, _snake_case    :       List[str]							, _snake_case    :       List[Any]						)   ->			Dict:







								"""simple docstring"""
								A_								=    DepthEstimationPipeline(model=_snake_case							, image_processor=_snake_case						)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Optional[int]							, _snake_case    :       Dict						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor						), "depth": ANY(Image.Image						)}							, _snake_case						)
								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						)},
								    ]							, _snake_case							, )




			@require_tf
			@unittest.skip("Depth estimation is not implemented in TF"						)
			def   lowerCamelCase__  (					self    :       Dict						)   ->			Optional[int]:







								"""simple docstring"""
								pass




			@slow
			@require_torch
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    "Intel/dpt-large"
								A_								=    pipeline("depth-estimation"							, model=_snake_case						)
								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()						)							, 2_9.3_0_4						)
								self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()						)							, 2.6_6_2						)






			@require_torch
			def   lowerCamelCase__  (					self    :       int						)   ->			Union[str, Any]:







								"""simple docstring"""
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"						)






"""simple docstring"""





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL




