"""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        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				KandinskyVaaPriorPipeline
					snake_case__       					=				["prompt"]
					snake_case__       					=				["prompt", "negative_prompt"]
					snake_case__       					=				[
					    "num_images_per_prompt",
					    "generator",
					    "num_inference_steps",
					    "latents",
					    "negative_prompt",
					    "guidance_scale",
					    "output_type",
					    "return_dict",
					]
					snake_case__       					=				False







					@property
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									return 32







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									return 32







					@property
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									return self.time_input_dim







					@property
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									return self.time_input_dim * 4







					@property
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									return 100







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
									return tokenizer







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									torch.manual_seed(0    )
									UpperCAmelCase__    =							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=1_000					,)
									return CLIPTextModelWithProjection(lowerCamelCase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			int    ):
									torch.manual_seed(0    )

									UpperCAmelCase__    =							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									UpperCAmelCase__    =							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__    =							nn.Parameter(torch.ones(model.clip_std.shape    )    )
									return model







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									torch.manual_seed(0    )
									UpperCAmelCase__    =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size					,image_size=224					,projection_dim=self.text_embedder_hidden_size					,intermediate_size=37					,num_attention_heads=4					,num_channels=3					,num_hidden_layers=5					,patch_size=14					,)

									UpperCAmelCase__    =							CLIPVisionModelWithProjection(lowerCamelCase__    )
									return model







					@property
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							CLIPImageProcessor(
									    crop_size=224					,do_center_crop=lowerCamelCase__					,do_normalize=lowerCamelCase__					,do_resize=lowerCamelCase__					,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=224					,)

									return image_processor







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							self.dummy_prior
									UpperCAmelCase__    =							self.dummy_image_encoder
									UpperCAmelCase__    =							self.dummy_text_encoder
									UpperCAmelCase__    =							self.dummy_tokenizer
									UpperCAmelCase__    =							self.dummy_image_processor

									UpperCAmelCase__    =							UnCLIPScheduler(
									    variance_type='fixed_small_log'					,prediction_type='sample'					,num_train_timesteps=1_000					,clip_sample=lowerCamelCase__					,clip_sample_range=1_0.0					,)

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

									return components







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Any=0    ):
									if str(lowerCamelCase__    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'prompt': 'horse',
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							'cpu'

									UpperCAmelCase__    =							self.get_dummy_components()

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

									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

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

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

									assert image.shape == (1, 32)

									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							torch_device == 'cpu'
									UpperCAmelCase__    =							True
									UpperCAmelCase__    =							False

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







					@skip_mps
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							torch_device == 'cpu'
									UpperCAmelCase__    =							False

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




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__						:						Union[str, Any]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Any    ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )

									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained(lowerCamelCase__					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							generator.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									assert np.abs(image - new_image    ).sum() < 1e-5, "Models don't have the same forward pass"







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'cyberpunk 2077'
									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]    )

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

									UpperCAmelCase__    =							'A painting of a squirrel eating a burger '
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.text_to_image(
									    prompt=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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

									UpperCAmelCase__    =							pipe.image_variation(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]    )

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



