'''simple docstring'''

import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       int      	=					KandinskyVaaPriorPipeline
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					["prompt"]
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					["prompt", "negative_prompt"]
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					[
     "num_images_per_prompt",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					False
 @property
 def a					(      self   :	List[str]							)  ->    str:
  return 32
 @property
 def a					(      self   :	Dict							)  ->    Optional[Any]:
  return 32
 @property
 def a					(      self   :	Tuple							)  ->    Union[str, Any]:
  return self.time_input_dim
 @property
 def a					(      self   :	List[Any]							)  ->    int:
  return self.time_input_dim * 4
 @property
 def a					(      self   :	List[Any]							)  ->    Dict:
  return 1_00
 @property
 def a					(      self   :	int							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
  return tokenizer
 @property
 def a					(      self   :	Optional[int]							)  ->    List[Any]:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						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(UpperCAmelCase__							)
 @property
 def a					(      self   :	int							)  ->    str:
  torch.manual_seed(0							)

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

  __lowerCAmelCase											=						PriorTransformer(**UpperCAmelCase__							)
  # 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
  __lowerCAmelCase											=						nn.Parameter(torch.ones(model.clip_std.shape							)							)
  return model
 @property
 def a					(      self   :	List[Any]							)  ->    Union[str, Any]:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						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      ,       )

  __lowerCAmelCase											=						CLIPVisionModelWithProjection(UpperCAmelCase__							)
  return model
 @property
 def a					(      self   :	Union[str, Any]							)  ->    Optional[int]:
  __lowerCAmelCase											=						CLIPImageProcessor(
      crop_size=2_24      ,       do_center_crop=UpperCAmelCase__      ,       do_normalize=UpperCAmelCase__      ,       do_resize=UpperCAmelCase__      ,       image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]      ,       image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]      ,       resample=3      ,       size=2_24      ,       )

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

  __lowerCAmelCase											=						UnCLIPScheduler(
      variance_type="""fixed_small_log"""      ,       prediction_type="""sample"""      ,       num_train_timesteps=10_00      ,       clip_sample=UpperCAmelCase__      ,       clip_sample_range=1_0.0      ,       )

  __lowerCAmelCase											=						{
      """prior""": prior,
      """image_encoder""": image_encoder,
      """text_encoder""": text_encoder,
      """tokenizer""": tokenizer,
      """scheduler""": scheduler,
      """image_processor""": image_processor,
  }

  return components
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0							)  ->    int:
  if str(UpperCAmelCase__							).startswith("""mps"""							):
   __lowerCAmelCase											=						torch.manual_seed(UpperCAmelCase__							)
  else:
   __lowerCAmelCase											=						torch.Generator(device=UpperCAmelCase__							).manual_seed(UpperCAmelCase__							)
  __lowerCAmelCase											=						{
      """prompt""": """horse""",
      """generator""": generator,
      """guidance_scale""": 4.0,
      """num_inference_steps""": 2,
      """output_type""": """np""",
  }
  return inputs
 def a					(      self   :	Tuple							)  ->    List[str]:
  __lowerCAmelCase											=						"""cpu"""

  __lowerCAmelCase											=						self.get_dummy_components()

  __lowerCAmelCase											=						self.pipeline_class(**UpperCAmelCase__							)
  __lowerCAmelCase											=						pipe.to(UpperCAmelCase__							)

  pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

  __lowerCAmelCase											=						pipe(**self.get_dummy_inputs(UpperCAmelCase__							)							)
  __lowerCAmelCase											=						output.image_embeds

  __lowerCAmelCase											=						pipe(
      **self.get_dummy_inputs(UpperCAmelCase__							)      ,       return_dict=UpperCAmelCase__      ,       )[0]

  __lowerCAmelCase											=						image[0, -10:]
  __lowerCAmelCase											=						image_from_tuple[0, -10:]

  assert image.shape == (1, 32)

  __lowerCAmelCase											=						np.array(
      [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]							)

  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 a					(      self   :	Any							)  ->    str:
  __lowerCAmelCase											=						torch_device == """cpu"""
  __lowerCAmelCase											=						True
  __lowerCAmelCase											=						False

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





 @skip_mps
 def a					(      self   :	List[str]							)  ->    Dict:
  __lowerCAmelCase											=						torch_device == """cpu"""
  __lowerCAmelCase											=						False

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










"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




@slow
@require_torch_gpu
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]   )

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