


"""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 	A_       (     _snake_case      ,  unittest.TestCase   ):





  """simple docstring"""

  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 							lowercase_     (						self						)				->					Optional[int]:
     return 32
  @property
  def 							lowercase_     (						self						)				->					List[Any]:
     return 32
  @property
  def 							lowercase_     (						self						)				->					Optional[int]:
     return self.time_input_dim
  @property
  def 							lowercase_     (						self						)				->					List[str]:
     return self.time_input_dim * 4
  @property
  def 							lowercase_     (						self						)				->					int:
     return 1_00
  @property
  def 							lowercase_     (						self						)				->					str:
     a   :	Dict						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
     return tokenizer
  @property
  def 							lowercase_     (						self						)				->					Dict:
     torch.manual_seed(0						)
     a   :	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-0_5  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=10_00  ,					)
     return CLIPTextModelWithProjection(snake_case_						)
  @property
  def 							lowercase_     (						self						)				->					Dict:
     torch.manual_seed(0						)

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

     a   :	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
     a   :	Optional[int]						=    nn.Parameter(torch.ones(model.clip_std.shape						)						)
     return model
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     torch.manual_seed(0						)
     a   :	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  ,					)

     a   :	Optional[Any]						=    CLIPVisionModelWithProjection(snake_case_						)
     return model
  @property
  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[Any]						=    CLIPImageProcessor(
         crop_size=2_24  ,					do_center_crop=snake_case_  ,					do_normalize=snake_case_  ,					do_resize=snake_case_  ,					image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,					image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,					resample=3  ,					size=2_24  ,					)

     return image_processor
  def 							lowercase_     (						self						)				->					Any:
     a   :	Optional[int]						=    self.dummy_prior
     a   :	str						=    self.dummy_image_encoder
     a   :	Any						=    self.dummy_text_encoder
     a   :	List[str]						=    self.dummy_tokenizer
     a   :	List[str]						=    self.dummy_image_processor

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

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

     return components
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=0						)				->					Dict:
     if str(snake_case_						).startswith('mps'						):
        a   :	str						=    torch.manual_seed(snake_case_						)
     else:
        a   :	List[str]						=    torch.Generator(device=snake_case_						).manual_seed(snake_case_						)

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

     a   :	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 							lowercase_     (						self						)				->					Optional[int]:
     a   :	Tuple						=    "cpu"

     a   :	str						=    self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=snake_case_						)

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

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

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

     assert image.shape == (1, 32)

     a   :	List[Any]						=    np.array(
         [
             0.107_1284,
             1.333_0271,
             0.6126_0223,
             -0.669_1065,
             -0.384_6852,
             -1.030_3661,
             0.2271_6111,
             0.0334_8901,
             0.3004_0675,
             -0.2480_5029,
         ]						)

     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 							lowercase_     (						self						)				->					List[Any]:
     a   :	List[Any]						=    torch_device == "cpu"
     a   :	Union[str, Any]						=    True
     a   :	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 							lowercase_     (						self						)				->					Dict:
     a   :	Union[str, Any]						=    torch_device == "cpu"
     a   :	Optional[int]						=    False

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







"""simple docstring"""
from ..utils import requires_backends


_a   :      List[str]       =				None






class   _UpperCAmelCase					:
    def __init__(     self    ,     *snake_case_    ,     **snake_case_  ):
      requires_backends(self    ,     ["detectron2"]  )


    @classmethod
    def        lowerCamelCase__						(     cls    ,     *snake_case_    ,     **snake_case_  ):
      requires_backends(cls    ,     ["detectron2"]  )

