





'''simple docstring'''




from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

lowercase__       : int         =       logging.get_logger(__name__)


@add_end_docstrings(a__				)
class 					SCREAMING_SNAKE_CASE       (a__				):


     def __init__(		self		,      *_UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






          super().__init__(*_UpperCAmelCase		,      **_UpperCAmelCase)
          requires_backends(self		,      'vision')
          self.check_model_type(_UpperCAmelCase)


     def __call__(		self		,      _UpperCAmelCase		,      **_UpperCAmelCase):

          '''simple docstring'''






          return super().__call__(_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






          return {}, {}, {}


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					str						=			load_image(_UpperCAmelCase)
          __A		:					Optional[int]						=			image.size
          __A		:					Union[str, Any]						=			self.image_processor(images=_UpperCAmelCase		,      return_tensors=self.framework)
          return model_inputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			self.model(**_UpperCAmelCase)
          return model_outputs


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[str]						=			model_outputs.predicted_depth
          __A		:					Optional[int]						=			torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,      size=self.image_size[::-1]		,      mode='bicubic'		,      align_corners=_UpperCAmelCase)
          __A		:					List[str]						=			prediction.squeeze().cpu().numpy()
          __A		:					Tuple						=			(output * 255 / np.max(_UpperCAmelCase)).astype('uint8')
          __A		:					int						=			Image.fromarray(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			{}
          __A		:					Tuple						=			predicted_depth
          __A		:					List[str]						=			depth
          return output_dict







import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =UNetaDModel(
              block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=3,      out_channels=3,      down_block_types=('DownBlock2D', 'AttnDownBlock2D'),      up_block_types=('AttnUpBlock2D', 'UpBlock2D'),      )
          return model





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              0
          ]

          _lowercase              =image[0, -3:, -3:, -1]
          _lowercase              =image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _lowercase              =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2






@slow
@require_torch
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

          _lowercase              =image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          _lowercase              =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


