





'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					(PNDMScheduler,)
     _UpperCAmelCase										=					(('''num_inference_steps''', 50),)







     def     UpperCamelCase			( self    ,   **A__    ) ->	Tuple:
      snake_case				=			{
          '''num_train_timesteps''': 10_00,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
      }

      config.update(**A__    )
      return config







     def     UpperCamelCase			( self    ,   A__=0    ,   **A__    ) ->	Optional[int]:
      snake_case				=			dict(self.forward_default_kwargs    )
      snake_case				=			kwargs.pop('''num_inference_steps'''    ,   A__    )
      snake_case				=			self.dummy_sample
      snake_case				=			0.1 * sample
      snake_case				=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

       snake_case				=			scheduler.step_prk(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample
       snake_case				=			new_scheduler.step_prk(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample

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

       snake_case				=			scheduler.step_plms(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample
       snake_case				=			new_scheduler.step_plms(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample

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







     def     UpperCamelCase			( self    ) ->	Dict:
      pass







     def     UpperCamelCase			( self    ,   A__=0    ,   **A__    ) ->	Optional[Any]:
      snake_case				=			dict(self.forward_default_kwargs    )
      snake_case				=			kwargs.pop('''num_inference_steps'''    ,   A__    )
      snake_case				=			self.dummy_sample
      snake_case				=			0.1 * sample
      snake_case				=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

      for scheduler_class in self.scheduler_classes:
       snake_case				=			self.get_scheduler_config()
       snake_case				=			scheduler_class(**A__    )
       scheduler.set_timesteps(A__    )

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

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

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

       snake_case				=			scheduler.step_prk(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample
       snake_case				=			new_scheduler.step_prk(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample

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

       snake_case				=			scheduler.step_plms(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample
       snake_case				=			new_scheduler.step_plms(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample

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







     def     UpperCamelCase			( self    ,   **A__    ) ->	Any:
      snake_case				=			self.scheduler_classes[0]
      snake_case				=			self.get_scheduler_config(**A__    )
      snake_case				=			scheduler_class(**A__    )

      snake_case				=			10
      snake_case				=			self.dummy_model()
      snake_case				=			self.dummy_sample_deter
      scheduler.set_timesteps(A__    )

      for i, t in enumerate(scheduler.prk_timesteps    ):
       snake_case				=			model(A__    ,   A__    )
       snake_case				=			scheduler.step_prk(A__    ,   A__    ,   A__    ).prev_sample

      for i, t in enumerate(scheduler.plms_timesteps    ):
       snake_case				=			model(A__    ,   A__    )
       snake_case				=			scheduler.step_plms(A__    ,   A__    ,   A__    ).prev_sample

      return sample







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			dict(self.forward_default_kwargs    )

      snake_case				=			kwargs.pop('''num_inference_steps'''    ,   A__    )

      for scheduler_class in self.scheduler_classes:
       snake_case				=			self.get_scheduler_config()
       snake_case				=			scheduler_class(**A__    )

       snake_case				=			self.dummy_sample
       snake_case				=			0.1 * sample

       if num_inference_steps is not None and hasattr(A__    ,   '''set_timesteps'''    ):
        scheduler.set_timesteps(A__    )
       elif num_inference_steps is not None and not hasattr(A__    ,   '''set_timesteps'''    ):
        snake_case				=			num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       snake_case				=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
       snake_case				=			dummy_past_residuals[:]

       snake_case				=			scheduler.step_prk(A__    ,   0    ,   A__    ,   **A__    ).prev_sample
       snake_case				=			scheduler.step_prk(A__    ,   1    ,   A__    ,   **A__    ).prev_sample

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

       snake_case				=			scheduler.step_plms(A__    ,   0    ,   A__    ,   **A__    ).prev_sample
       snake_case				=			scheduler.step_plms(A__    ,   1    ,   A__    ,   **A__    ).prev_sample

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







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      for timesteps in [1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=A__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=A__    )

      snake_case				=			self.scheduler_classes[0]
      snake_case				=			self.get_scheduler_config(steps_offset=1    )
      snake_case				=			scheduler_class(**A__    )
      scheduler.set_timesteps(10    )
      assert torch.equal(
          scheduler.timesteps    ,   torch.LongTensor(
              [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]    )    ,   )







     def     UpperCamelCase			( self    ) ->	Any:
      for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]    ,   [0.0_0_2, 0.0_2]    ):
       self.check_over_configs(beta_start=A__    ,   beta_end=A__    )







     def     UpperCamelCase			( self    ) ->	Dict:
      for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=A__    )







     def     UpperCamelCase			( self    ) ->	Dict:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=A__    )







     def     UpperCamelCase			( self    ) ->	List[str]:
      for t in [1, 5, 10]:
       self.check_over_forward(time_step=A__    )







     def     UpperCamelCase			( self    ) ->	Dict:
      for t, num_inference_steps in zip([1, 5, 10]    ,   [10, 50, 1_00]    ):
       self.check_over_forward(num_inference_steps=A__    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
      snake_case				=			27

      for scheduler_class in self.scheduler_classes:
       snake_case				=			self.dummy_sample
       snake_case				=			0.1 * sample

       snake_case				=			self.get_scheduler_config()
       snake_case				=			scheduler_class(**A__    )

       scheduler.set_timesteps(A__    )

       # before power of 3 fix, would error on first step, so we only need to do two
       for i, t in enumerate(scheduler.prk_timesteps[:2]    ):
        snake_case				=			scheduler.step_prk(A__    ,   A__    ,   A__    ).prev_sample







     def     UpperCamelCase			( self    ) ->	Dict:
      with self.assertRaises(A__    ):
       snake_case				=			self.scheduler_classes[0]
       snake_case				=			self.get_scheduler_config()
       snake_case				=			scheduler_class(**A__    )

       scheduler.step_plms(self.dummy_sample    ,   1    ,   self.dummy_sample    ).prev_sample







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.full_loop()
      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 1_9_8.1_3_1_8    ) < 1e-2
      assert abs(result_mean.item() - 0.2_5_8_0    ) < 1e-3







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.full_loop(prediction_type='''v_prediction'''    )
      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 6_7.3_9_8_6    ) < 1e-2
      assert abs(result_mean.item() - 0.0_8_7_8    ) < 1e-3







     def     UpperCamelCase			( self    ) ->	Dict:
      # We specify different beta, so that the first alpha is 0.99
      snake_case				=			self.full_loop(set_alpha_to_one=A__    ,   beta_start=0.0_1    )
      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 2_3_0.0_3_9_9    ) < 1e-2
      assert abs(result_mean.item() - 0.2_9_9_5    ) < 1e-3







     def     UpperCamelCase			( self    ) ->	List[str]:
      # We specify different beta, so that the first alpha is 0.99
      snake_case				=			self.full_loop(set_alpha_to_one=A__    ,   beta_start=0.0_1    )
      snake_case				=			torch.sum(torch.abs(A__    )    )
      snake_case				=			torch.mean(torch.abs(A__    )    )

      assert abs(result_sum.item() - 1_8_6.9_4_8_2    ) < 1e-2
      assert abs(result_mean.item() - 0.2_4_3_4    ) < 1e-3








'''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 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			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    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			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				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			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    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			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     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

