






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 							SCREAMING_SNAKE_CASE__		:





						"""simple docstring"""






						@staticmethod
						def  _lowercase  (      *__A  :						Any							,	**__A  :						Union[str, Any] ):
							pass







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Image     ):
	snake_case__				:   Tuple     =						hashlib.mda(image.tobytes()     )
	return m.hexdigest()





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





					"""simple docstring"""






					a_			=	MODEL_FOR_DEPTH_ESTIMATION_MAPPING



					def  _lowercase  (      self  :						Dict							,	__A  :						str							,	__A  :						int							,	__A  :						List[Any] ):
						snake_case__				:   Tuple     =						DepthEstimationPipeline(model=__A							,	image_processor=__A )
						return depth_estimator, [
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						]



					def  _lowercase  (      self  :						Any							,	__A  :						List[Any]							,	__A  :						List[str] ):
						snake_case__				:   Optional[int]     =						depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )}							,	__A )
						import datasets

						snake_case__				:   Any     =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,	"image"							,	split="test" )
						snake_case__				:   Optional[int]     =						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  _lowercase  (      self  :						int ):
						pass



					@slow
					@require_torch
					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						"Intel/dpt-large"
						snake_case__				:   Dict     =						pipeline("depth-estimation"							,	model=__A )
						snake_case__				:   Optional[int]     =						depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
						snake_case__				:   List[str]     =						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  _lowercase  (      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" )











from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



