




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_						(			*lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Optional[int]      )     ->			Union[str, Any]:
         pass



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	):




 """simple docstring"""







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


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_UpperCamelCase      (				unittest.TestCase		):
      UpperCAmelCase_        =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Union[str, Any]      )     ->			Tuple:
       UpperCAmelCase__							 =						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   :Tuple ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :List[str]      )     ->			str:
       UpperCAmelCase__							 =						depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )} ,      _a      )
       import datasets

       UpperCAmelCase__							 =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils" ,      "image" ,      split="test"      )
       UpperCAmelCase__							 =						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   :str      )     ->			str:
       pass
      @slow
      @require_torch
      def 	UpperCAmelCase_						(			self   :int      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						"""Intel/dpt-large"""
       UpperCAmelCase__							 =						pipeline("depth-estimation" ,      model=_a      )
       UpperCAmelCase__							 =						depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"      )
       UpperCAmelCase__							 =						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_62      )







      @require_torch
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			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"      )











import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		(IPNDMScheduler,)
      UpperCAmelCase_        =		(("""num_inference_steps""", 50),)
      def 	UpperCAmelCase_						(			self   :List[str] ,      **lowerCamelCase   :List[Any]      )     ->			List[str]:
       UpperCAmelCase__							 =						{"num_train_timesteps": 1000}
       config.update(**lowerCamelCase      )
       return config
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Union[str, Any]=0 ,      **lowerCamelCase   :str      )     ->			str:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )
        # copy over dummy past residuals
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         new_scheduler.set_timesteps(lowerCamelCase      )
         # copy over dummy past residuals
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Tuple:
       pass
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[str]=0 ,      **lowerCamelCase   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )
       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						self.dummy_sample
       UpperCAmelCase__							 =						0.1 * sample
       UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )
        scheduler.set_timesteps(lowerCamelCase      )

        # copy over dummy past residuals (must be after setting timesteps)
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        if time_step is None:
         UpperCAmelCase__							 =						scheduler.timesteps[len(scheduler.timesteps      ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase      )
         UpperCAmelCase__							 =						scheduler_class.from_pretrained(lowerCamelCase      )
         # copy over dummy past residuals
         new_scheduler.set_timesteps(lowerCamelCase      )

         # copy over dummy past residual (must be after setting timesteps)
         UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						new_scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"
      def 	UpperCAmelCase_						(			self   :int ,      **lowerCamelCase   :Any      )     ->			int:
       UpperCAmelCase__							 =						self.scheduler_classes[0]
       UpperCAmelCase__							 =						self.get_scheduler_config(**lowerCamelCase      )
       UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						10
       UpperCAmelCase__							 =						self.dummy_model()
       UpperCAmelCase__							 =						self.dummy_sample_deter
       scheduler.set_timesteps(lowerCamelCase      )

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       for i, t in enumerate(scheduler.timesteps      ):
        UpperCAmelCase__							 =						model(lowerCamelCase ,      lowerCamelCase      )
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      ).prev_sample

       return sample
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						dict(self.forward_default_kwargs      )

       UpperCAmelCase__							 =						kwargs.pop("num_inference_steps" ,      lowerCamelCase      )

       for scheduler_class in self.scheduler_classes:
        UpperCAmelCase__							 =						self.get_scheduler_config()
        UpperCAmelCase__							 =						scheduler_class(**lowerCamelCase      )

        UpperCAmelCase__							 =						self.dummy_sample
        UpperCAmelCase__							 =						0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase ,      "set_timesteps"      ):
         scheduler.set_timesteps(lowerCamelCase      )
        elif num_inference_steps is not None and not hasattr(lowerCamelCase ,      "set_timesteps"      ):
         UpperCAmelCase__							 =						num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        UpperCAmelCase__							 =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        UpperCAmelCase__							 =						dummy_past_residuals[:]

        UpperCAmelCase__							 =						scheduler.timesteps[5]
        UpperCAmelCase__							 =						scheduler.timesteps[6]

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )

        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample
        UpperCAmelCase__							 =						scheduler.step(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      **lowerCamelCase      ).prev_sample

        self.assertEqual(output_a.shape ,      sample.shape      )
        self.assertEqual(output_a.shape ,      output_a.shape      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Tuple:
       for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase ,      time_step=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       for t, num_inference_steps in zip([1, 5, 10] ,      [10, 50, 100]      ):
        self.check_over_forward(num_inference_steps=lowerCamelCase ,      time_step=lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :Any      )     ->			Dict:
       UpperCAmelCase__							 =						self.full_loop()
       UpperCAmelCase__							 =						torch.mean(torch.abs(lowerCamelCase      )      )

       assert abs(result_mean.item() - 254_0529      ) < 10





