





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 			_a						(				_lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   KandinskyVaaPriorPipeline
				UpperCamelCase								=   ['''prompt''']
				UpperCamelCase								=   ['''prompt''', '''negative_prompt''']
				UpperCamelCase								=   [
				    '''num_images_per_prompt''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				UpperCamelCase								=   False






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				List[Any]:




										'''simple docstring'''



										return 3_2






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Dict:




										'''simple docstring'''



										return 3_2






				@property
				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										return self.time_input_dim






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										return self.time_input_dim * 4






				@property
				def     snake_case     (			self		:   int							)     ->				Optional[Any]:




										'''simple docstring'''



										return 1_0_0






				@property
				def     snake_case     (			self		:   Any							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)
										return tokenizer






				@property
				def     snake_case     (			self		:   Dict							)     ->				Any:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase      : int         =   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=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=1_0_0_0,			)
										return CLIPTextModelWithProjection(lowerCAmelCase__							)






				@property
				def     snake_case     (			self		:   Any							)     ->				List[Any]:




										'''simple docstring'''



										torch.manual_seed(0							)

										_UpperCamelCase      : Any         =   {
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 1_2,
										    '''embedding_dim''': self.text_embedder_hidden_size,
										    '''num_layers''': 1,
										}

										_UpperCamelCase      : Union[str, Any]         =   PriorTransformer(**lowerCAmelCase__							)
										# 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
										_UpperCamelCase      : List[str]         =   nn.Parameter(torch.ones(model.clip_std.shape							)							)
										return model






				@property
				def     snake_case     (			self		:   List[Any]							)     ->				Tuple:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size,			image_size=2_2_4,			projection_dim=self.text_embedder_hidden_size,			intermediate_size=3_7,			num_attention_heads=4,			num_channels=3,			num_hidden_layers=5,			patch_size=1_4,			)

										_UpperCamelCase      : Any         =   CLIPVisionModelWithProjection(lowerCAmelCase__							)
										return model






				@property
				def     snake_case     (			self		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   CLIPImageProcessor(
										    crop_size=2_2_4,			do_center_crop=lowerCAmelCase__,			do_normalize=lowerCAmelCase__,			do_resize=lowerCAmelCase__,			image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],			image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],			resample=3,			size=2_2_4,			)

										return image_processor






				def     snake_case     (			self		:   List[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.dummy_prior
										_UpperCamelCase      : Tuple         =   self.dummy_image_encoder
										_UpperCamelCase      : Optional[int]         =   self.dummy_text_encoder
										_UpperCamelCase      : Optional[int]         =   self.dummy_tokenizer
										_UpperCamelCase      : str         =   self.dummy_image_processor

										_UpperCamelCase      : Tuple         =   UnCLIPScheduler(
										    variance_type='''fixed_small_log''',			prediction_type='''sample''',			num_train_timesteps=1_0_0_0,			clip_sample=lowerCAmelCase__,			clip_sample_range=10.0,			)

										_UpperCamelCase      : str         =   {
										    '''prior''': prior,
										    '''image_encoder''': image_encoder,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''scheduler''': scheduler,
										    '''image_processor''': image_processor,
										}

										return components






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   Union[str, Any]=0							)     ->				str:




										'''simple docstring'''



										if str(lowerCAmelCase__							).startswith('''mps'''							):
																_UpperCamelCase      : List[Any]         =   torch.manual_seed(lowerCAmelCase__							)
										else:
																_UpperCamelCase      : str         =   torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   {
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs






				def     snake_case     (			self		:   Optional[int]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   '''cpu'''

										_UpperCamelCase      : Optional[int]         =   self.get_dummy_components()

										_UpperCamelCase      : List[Any]         =   self.pipeline_class(**lowerCAmelCase__							)
										_UpperCamelCase      : str         =   pipe.to(lowerCAmelCase__							)

										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   pipe(**self.get_dummy_inputs(lowerCAmelCase__							)							)
										_UpperCamelCase      : int         =   output.image_embeds

										_UpperCamelCase      : Dict         =   pipe(
										    **self.get_dummy_inputs(lowerCAmelCase__							),			return_dict=lowerCAmelCase__,			)[0]

										_UpperCamelCase      : int         =   image[0, -1_0:]
										_UpperCamelCase      : Optional[int]         =   image_from_tuple[0, -1_0:]

										assert image.shape == (1, 3_2)

										_UpperCamelCase      : Tuple         =   np.array(
										    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]							)

										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     snake_case     (			self		:   Optional[int]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   torch_device == '''cpu'''
										_UpperCamelCase      : List[Any]         =   True
										_UpperCamelCase      : Optional[int]         =   False

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






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




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   torch_device == '''cpu'''
										_UpperCamelCase      : List[Any]         =   False

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










"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)



