



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					str:
   UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     image.size
   UpperCamelCase     ,					UpperCamelCase   :  str						     =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   UpperCamelCase   :  Optional[Any]						     =     image.resize((w, h)	,					resample=PIL_INTERPOLATION['lanczos']     )
   UpperCamelCase   :  Any						     =     np.array(snake_case__     ).astype(np.floataa     ) / 255.0
   UpperCamelCase   :  List[Any]						     =     image[None].transpose(0	,					3	,					1	,					2     )
   UpperCamelCase   :  int						     =     torch.from_numpy(snake_case__     )
   return 2.0 * image - 1.0



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          super().__init__()
          self.register_modules(vqvae=SCREAMING_SNAKE_CASE_,      unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_	)






       @torch.no_grad()
       def __call__(      self,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 100,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "pil",      SCREAMING_SNAKE_CASE_ = True,      )					->		Union[Tuple, ImagePipelineOutput]:
          if isinstance(SCREAMING_SNAKE_CASE_,      PIL.Image.Image	):
             UpperCamelCase   :  Union[str, Any]						     =     1
          elif isinstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	):
             UpperCamelCase   :  int						     =     image.shape[0]
          else:
             raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(SCREAMING_SNAKE_CASE_	)}"""	)

          if isinstance(SCREAMING_SNAKE_CASE_,      PIL.Image.Image	):
             UpperCamelCase   :  Optional[int]						     =     preprocess(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase     ,					UpperCamelCase   :  str						     =     image.shape[-2:]

          # in_channels should be 6: 3 for latents, 3 for low resolution image
          UpperCamelCase   :  Any						     =     (batch_size, self.unet.config.in_channels // 2, height, width)
          UpperCamelCase   :  Union[str, Any]						     =     next(self.unet.parameters()	).dtype

          UpperCamelCase   :  Union[str, Any]						     =     randn_tensor(SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      device=self.device,      dtype=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Union[str, Any]						     =     image.to(device=self.device,      dtype=SCREAMING_SNAKE_CASE_	)

          # set timesteps and move to the correct device
          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,      device=self.device	)
          UpperCamelCase   :  Optional[int]						     =     self.scheduler.timesteps

          # scale the initial noise by the standard deviation required by the scheduler
          UpperCamelCase   :  Optional[Any]						     =     latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          UpperCamelCase   :  List[str]						     =     'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
          UpperCamelCase   :  List[str]						     =     {}
          if accepts_eta:
             UpperCamelCase   :  Optional[int]						     =     eta

          for t in self.progress_bar(SCREAMING_SNAKE_CASE_	):
             # concat latents and low resolution image in the channel dimension.
             UpperCamelCase   :  int						     =     torch.cat([latents, image],      dim=1	)
             UpperCamelCase   :  Tuple						     =     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
             # predict the noise residual
             UpperCamelCase   :  Any						     =     self.unet(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	).sample
             # compute the previous noisy sample x_t -> x_t-1
             UpperCamelCase   :  Dict						     =     self.scheduler.step(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	).prev_sample

          # decode the image latents with the VQVAE
          UpperCamelCase   :  str						     =     self.vqvae.decode(SCREAMING_SNAKE_CASE_	).sample
          UpperCamelCase   :  Optional[int]						     =     torch.clamp(SCREAMING_SNAKE_CASE_,      -1.0,      1.0	)
          UpperCamelCase   :  Optional[Any]						     =     image / 2 + 0.5
          UpperCamelCase   :  Optional[Any]						     =     image.cpu().permute(0,      2,      3,      1	).numpy()

          if output_type == "pil":
             UpperCamelCase   :  Optional[Any]						     =     self.numpy_to_pil(SCREAMING_SNAKE_CASE_	)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)








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  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     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	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     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  snake_case_	(      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'	)



