



'''simple docstring'''





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






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




  a_							      =RePaintPipeline
  a_							      =IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
  a_							      =PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  a_							      =IMAGE_INPAINTING_BATCH_PARAMS
  a_							      =False




  def 		_lowercase  (				self   :			str     )      ->      Tuple:
    torch.manual_seed(0     )
    torch.manual_seed(0     )
    __lowerCamelCase : List[Any]					 =				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')    ,			)
    __lowerCamelCase : int					 =				RePaintScheduler()
    __lowerCamelCase : Any					 =				{'unet': unet, 'scheduler': scheduler}
    return components




  def 		_lowercase  (				self   :			int    ,			_a   :			Union[str, Any]    ,			_a   :			Optional[Any]=0     )      ->      int:
    if str(_a     ).startswith('mps'     ):
      __lowerCamelCase : Tuple					 =				torch.manual_seed(_a     )
    else:
      __lowerCamelCase : Dict					 =				torch.Generator(device=_a     ).manual_seed(_a     )
    __lowerCamelCase : Any					 =				np.random.RandomState(_a     ).standard_normal((1, 3, 32, 32)     )
    __lowerCamelCase : List[Any]					 =				torch.from_numpy(_a     ).to(device=_a    ,			dtype=torch.floataa     )
    __lowerCamelCase : Any					 =				(image > 0).to(device=_a    ,			dtype=torch.floataa     )
    __lowerCamelCase : Union[str, Any]					 =				{
        'image': image,
        'mask_image': mask,
        'generator': generator,
        'num_inference_steps': 5,
        'eta': 0.0,
        'jump_length': 2,
        'jump_n_sample': 2,
        'output_type': 'numpy',
    }
    return inputs




  def 		_lowercase  (				self   :			List[Any]     )      ->      Any:
    __lowerCamelCase : List[Any]					 =				'cpu'  # ensure determinism for the device-dependent torch.Generator
    __lowerCamelCase : Dict					 =				self.get_dummy_components()
    __lowerCamelCase : Union[str, Any]					 =				RePaintPipeline(**_a     )
    __lowerCamelCase : Tuple					 =				sd_pipe.to(_a     )
    sd_pipe.set_progress_bar_config(disable=_a     )

    __lowerCamelCase : Optional[int]					 =				self.get_dummy_inputs(_a     )
    __lowerCamelCase : Dict					 =				sd_pipe(**_a     ).images
    __lowerCamelCase : Optional[int]					 =				image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __lowerCamelCase : Any					 =				np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]     )

    assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3




  @skip_mps
  def 		_lowercase  (				self   :			List[Any]     )      ->      Union[str, Any]:
    return super().test_save_load_local()




  @unittest.skip('non-deterministic pipeline'     )
  def 		_lowercase  (				self   :			Optional[int]     )      ->      List[Any]:
    return super().test_inference_batch_single_identical()




  @skip_mps
  def 		_lowercase  (				self   :			Tuple     )      ->      Tuple:
    return super().test_dict_tuple_outputs_equivalent()




  @skip_mps
  def 		_lowercase  (				self   :			Tuple     )      ->      Optional[Any]:
    return super().test_save_load_optional_components()




  @skip_mps
  def 		_lowercase  (				self   :			Tuple     )      ->      Union[str, Any]:
    return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  def 		_lowercase  (				self   :			List[str]     )      ->      Dict:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




  def 		_lowercase  (				self   :			Optional[Any]     )      ->      Any:
    __lowerCamelCase : int					 =				load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
        'repaint/celeba_hq_256.png'     )
    __lowerCamelCase : List[Any]					 =				load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'     )
    __lowerCamelCase : Dict					 =				load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
        'repaint/celeba_hq_256_result.npy'     )

    __lowerCamelCase : Union[str, Any]					 =				'google/ddpm-ema-celebahq-256'
    __lowerCamelCase : Tuple					 =				UNetaDModel.from_pretrained(_a     )
    __lowerCamelCase : List[Any]					 =				RePaintScheduler.from_pretrained(_a     )

    __lowerCamelCase : Tuple					 =				RePaintPipeline(unet=_a    ,			scheduler=_a     ).to(_a     )
    repaint.set_progress_bar_config(disable=_a     )
    repaint.enable_attention_slicing()

    __lowerCamelCase : str					 =				torch.manual_seed(0     )
    __lowerCamelCase : Tuple					 =				repaint(
        _a    ,			_a    ,			num_inference_steps=250    ,			eta=0.0    ,			jump_length=10    ,			jump_n_sample=10    ,			generator=_a    ,			output_type='np'    ,			)
    __lowerCamelCase : str					 =				output.images[0]

    assert image.shape == (256, 256, 3)
    assert np.abs(expected_image - image     ).mean() < 1e-2











'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  @slow
  def 		_lowercase  (				self   :			str     )      ->      List[Any]:
    __lowerCamelCase : str					 =				CamembertModel.from_pretrained('camembert-base'     )
    model.to(_a     )

    __lowerCamelCase : List[Any]					 =				torch.tensor(
        [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]    ,			device=_a    ,			dtype=torch.long    ,			)  # J'aime le camembert !
    with torch.no_grad():
      __lowerCamelCase : int					 =				model(_a     )['last_hidden_state']
    __lowerCamelCase : Optional[int]					 =				torch.Size((1, 10, 768)     )
    self.assertEqual(output.shape    ,			_a     )
    # compare the actual values for a slice.
    __lowerCamelCase : Tuple					 =				torch.tensor(
        [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,			device=_a    ,			dtype=torch.float    ,			)
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(torch.allclose(output[:, :3, :3]    ,			_a    ,			atol=1e-4     )     )






