
"""simple docstring"""
import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			Union[str, Any]	=       KandinskyVaaPriorEmbaEmbPipeline
    __lowercase				:			Optional[Any]	=       ["""prompt""", """image"""]
    __lowercase				:			List[str]	=       ["""prompt""", """image"""]
    __lowercase				:			int	=       [
        """num_images_per_prompt""",
        """strength""",
        """generator""",
        """num_inference_steps""",
        """latents""",
        """negative_prompt""",
        """guidance_scale""",
        """output_type""",
        """return_dict""",
    ]
    __lowercase				:			Optional[Any]	=       False
    @property
    def        lowerCamelCase__						(     self  ):
      return 32
    @property
    def        lowerCamelCase__						(     self  ):
      return 32
    @property
    def        lowerCamelCase__						(     self  ):
      return self.time_input_dim
    @property
    def        lowerCamelCase__						(     self  ):
      return self.time_input_dim * 4
    @property
    def        lowerCamelCase__						(     self  ):
      return 1_00
    @property
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )
      return tokenizer
    @property
    def        lowerCamelCase__						(     self  ):
      torch.manual_seed(0  )
      _snake_case  :    Tuple            = CLIPTextConfig(
          bos_token_id=0    ,     eos_token_id=2    ,     hidden_size=self.text_embedder_hidden_size    ,     projection_dim=self.text_embedder_hidden_size    ,     intermediate_size=37    ,     layer_norm_eps=1E-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=10_00    ,     )
      return CLIPTextModelWithProjection(snake_case_  )
    @property
    def        lowerCamelCase__						(     self  ):
      torch.manual_seed(0  )

      _snake_case  :    Tuple            = {
          "num_attention_heads": 2,
          "attention_head_dim": 12,
          "embedding_dim": self.text_embedder_hidden_size,
          "num_layers": 1,
      }

      _snake_case  :    Optional[int]            = PriorTransformer(**snake_case_  )
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _snake_case  :    Optional[int]            = nn.Parameter(torch.ones(model.clip_std.shape  )  )
      return model
    @property
    def        lowerCamelCase__						(     self  ):
      torch.manual_seed(0  )
      _snake_case  :    int            = CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size    ,     image_size=2_24    ,     projection_dim=self.text_embedder_hidden_size    ,     intermediate_size=37    ,     num_attention_heads=4    ,     num_channels=3    ,     num_hidden_layers=5    ,     patch_size=14    ,     )

      _snake_case  :    Optional[Any]            = CLIPVisionModelWithProjection(snake_case_  )
      return model
    @property
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = CLIPImageProcessor(
          crop_size=2_24    ,     do_center_crop=snake_case_    ,     do_normalize=snake_case_    ,     do_resize=snake_case_    ,     image_mean=[0.48145466, 0.4578275, 0.40821073]    ,     image_std=[0.26862954, 0.26130258, 0.27577711]    ,     resample=3    ,     size=2_24    ,     )

      return image_processor
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = self.dummy_prior
      _snake_case  :    str            = self.dummy_image_encoder
      _snake_case  :    Any            = self.dummy_text_encoder
      _snake_case  :    List[str]            = self.dummy_tokenizer
      _snake_case  :    List[str]            = self.dummy_image_processor

      _snake_case  :    Tuple            = UnCLIPScheduler(
          variance_type="fixed_small_log"    ,     prediction_type="sample"    ,     num_train_timesteps=10_00    ,     clip_sample=snake_case_    ,     clip_sample_range=10.0    ,     )

      _snake_case  :    List[Any]            = {
          "prior": prior,
          "image_encoder": image_encoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "scheduler": scheduler,
          "image_processor": image_processor,
      }

      return components
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    str            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    List[str]            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    Union[str, Any]            = floats_tensor((1, 3, 64, 64)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )
      _snake_case  :    str            = image.cpu().permute(0    ,     2    ,     3    ,     1  )[0]
      _snake_case  :    Union[str, Any]            = Image.fromarray(np.uinta(snake_case_  )  ).convert("RGB"  ).resize((2_56, 2_56)  )

      _snake_case  :    Optional[Any]            = {
          "prompt": "horse",
          "image": init_image,
          "strength": 0.5,
          "generator": generator,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = "cpu"

      _snake_case  :    str            = self.get_dummy_components()

      _snake_case  :    List[str]            = self.pipeline_class(**snake_case_  )
      _snake_case  :    List[Any]            = pipe.to(snake_case_  )

      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    Dict            = pipe(**self.get_dummy_inputs(snake_case_  )  )
      _snake_case  :    Union[str, Any]            = output.image_embeds

      _snake_case  :    Optional[int]            = pipe(
          **self.get_dummy_inputs(snake_case_  )    ,     return_dict=snake_case_    ,     )[0]

      _snake_case  :    str            = image[0, -10:]
      _snake_case  :    Optional[Any]            = image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _snake_case  :    List[Any]            = np.array(
          [
              0.1071284,
              1.3330271,
              0.61260223,
              -0.6691065,
              -0.3846852,
              -1.0303661,
              0.22716111,
              0.03348901,
              0.30040675,
              -0.24805029,
          ]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
    @skip_mps
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = torch_device == "cpu"
      _snake_case  :    Union[str, Any]            = True
      _snake_case  :    Any            = False

      self._test_inference_batch_single_identical(
          test_max_difference=snake_case_    ,     relax_max_difference=snake_case_    ,     test_mean_pixel_difference=snake_case_    ,     )


    @skip_mps
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = torch_device == "cpu"
      _snake_case  :    Optional[int]            = False

      self._test_attention_slicing_forward_pass(
          test_max_difference=snake_case_    ,     test_mean_pixel_difference=snake_case_    ,     )



'''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 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       torch.manual_seed(0      )
       torch.manual_seed(0      )
       __A													=      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')						,					)
       __A													=      RePaintScheduler()
       __A													=      {'unet': unet, 'scheduler': scheduler}
       return components






 def 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					dtype=torch.floataa      )
       __A													=      {
           '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	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      sd_pipe(**_A      ).images
       __A													=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __A													=      np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]      )

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






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'      )
       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
       __A													=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'      )

       __A													=      'google/ddpm-ema-celebahq-256'
       __A													=      UNetaDModel.from_pretrained(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

       __A													=      torch.manual_seed(0      )
       __A													=      repaint(
           _A						,					_A						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=_A						,					output_type='np'						,					)
       __A													=      output.images[0]

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






