
'''simple docstring'''

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


snake_case									=    False




class    lowerCAmelCase       (     unittest.TestCase  ):


 def    _A	(    self  : Optional[int]			,							a__  : List[Any]=32    ):
  '''simple docstring'''

  set_seed(0    )
  lowerCAmelCase__     :   Optional[int]     		=	UNetaDModel(sample_size=a__			,							in_channels=3			,							out_channels=3    )
  lowerCAmelCase__     :   List[Any]     		=	torch.optim.SGD(model.parameters()			,							lr=0.0001    )
  return model, optimizer


 @slow
 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
  lowerCAmelCase__     :   Union[str, Any]     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_start=0.0001			,							beta_end=0.02			,							beta_schedule="linear"			,							clip_sample=a__			,							)
  lowerCAmelCase__     :   Optional[Any]     		=	DDIMScheduler(
      num_train_timesteps=1000			,							beta_start=0.0001			,							beta_end=0.02			,							beta_schedule="linear"			,							clip_sample=a__			,							)

  assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

  # shared batches for DDPM and DDIM
  set_seed(0    )
  lowerCAmelCase__     :   List[str]     		=	[torch.randn((4, 3, 32, 32)    ).clip(-1			,							1    ).to(a__    ) for _ in range(4    )]
  lowerCAmelCase__     :   Optional[int]     		=	[torch.randn((4, 3, 32, 32)    ).to(a__    ) for _ in range(4    )]
  lowerCAmelCase__     :   List[Any]     		=	[torch.randint(0			,							1000			,							(4,)    ).long().to(a__    ) for _ in range(4    )]

  # train with a DDPM scheduler
  lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	self.get_model_optimizer(resolution=32    )
  model.train().to(a__    )
  for i in range(4    ):
   optimizer.zero_grad()
   lowerCAmelCase__     :   Optional[Any]     		=	ddpm_scheduler.add_noise(clean_images[i]			,							noise[i]			,							timesteps[i]    )
   lowerCAmelCase__     :   int     		=	model(a__			,							timesteps[i]    ).sample
   lowerCAmelCase__     :   Union[str, Any]     		=	torch.nn.functional.mse_loss(a__			,							noise[i]    )
   loss.backward()
   optimizer.step()
  del model, optimizer

  # recreate the model and optimizer, and retry with DDIM
  lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	self.get_model_optimizer(resolution=32    )
  model.train().to(a__    )
  for i in range(4    ):
   optimizer.zero_grad()
   lowerCAmelCase__     :   str     		=	ddim_scheduler.add_noise(clean_images[i]			,							noise[i]			,							timesteps[i]    )
   lowerCAmelCase__     :   Any     		=	model(a__			,							timesteps[i]    ).sample
   lowerCAmelCase__     :   Optional[Any]     		=	torch.nn.functional.mse_loss(a__			,							noise[i]    )
   loss.backward()
   optimizer.step()
  del model, optimizer

  self.assertTrue(torch.allclose(a__			,							a__			,							atol=1e-5    )    )
  self.assertTrue(torch.allclose(a__			,							a__			,							atol=1e-5    )    )







'''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       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   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    _A	(    self  : Any    ):
  '''simple docstring'''

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





