



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__						,		lowerCAmelCase__					:    List[str]												=					image.size
				lowerCAmelCase__						,		lowerCAmelCase__					:    List[Any]												=					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				lowerCAmelCase__					:    Union[str, Any]												=					image.resize((w, h)	,resample=PIL_INTERPOLATION['''lanczos'''])
				lowerCAmelCase__					:    str												=					np.array(lowerCamelCase_).astype(np.floataa) / 255.0
				lowerCAmelCase__					:    Any												=					image[None].transpose(0	,3	,1	,2)
				lowerCAmelCase__					:    Tuple												=					torch.from_numpy(lowerCamelCase_)
				return 2.0 * image - 1.0



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,)					->      Dict:

						"""simple docstring"""






						super().__init__()
						self.register_modules(vqvae=__lowerCamelCase		,unet=__lowerCamelCase		,scheduler=__lowerCamelCase			)



		@torch.no_grad()
		def __call__(self		,__lowerCamelCase = None		,__lowerCamelCase = 1		,__lowerCamelCase = 1_00		,__lowerCamelCase = 0.0		,__lowerCamelCase = None		,__lowerCamelCase = "pil"		,__lowerCamelCase = True		,)					->      Union[Tuple, ImagePipelineOutput]:

						"""simple docstring"""






						if isinstance(__lowerCamelCase		,PIL.Image.Image			):
										lowerCAmelCase__					:    Optional[int]												=					1
						elif isinstance(__lowerCamelCase		,torch.Tensor			):
										lowerCAmelCase__					:    List[str]												=					image.shape[0]
						else:
										raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase			)}"""			)

						if isinstance(__lowerCamelCase		,PIL.Image.Image			):
										lowerCAmelCase__					:    Tuple												=					preprocess(__lowerCamelCase			)

						lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					image.shape[-2:]

						# in_channels should be 6: 3 for latents, 3 for low resolution image
						lowerCAmelCase__					:    Union[str, Any]												=					(batch_size, self.unet.config.in_channels // 2, height, width)
						lowerCAmelCase__					:    Tuple												=					next(self.unet.parameters()			).dtype

						lowerCAmelCase__					:    Union[str, Any]												=					randn_tensor(__lowerCamelCase		,generator=__lowerCamelCase		,device=self.device		,dtype=__lowerCamelCase			)

						lowerCAmelCase__					:    Any												=					image.to(device=self.device		,dtype=__lowerCamelCase			)

						# set timesteps and move to the correct device
						self.scheduler.set_timesteps(__lowerCamelCase		,device=self.device			)
						lowerCAmelCase__					:    Tuple												=					self.scheduler.timesteps

						# scale the initial noise by the standard deviation required by the scheduler
						lowerCAmelCase__					:    str												=					latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						lowerCAmelCase__					:    str												=					'''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
						lowerCAmelCase__					:    Dict												=					{}
						if accepts_eta:
										lowerCAmelCase__					:    Dict												=					eta

						for t in self.progress_bar(__lowerCamelCase			):
										# concat latents and low resolution image in the channel dimension.
										lowerCAmelCase__					:    Tuple												=					torch.cat([latents, image]		,dim=1			)
										lowerCAmelCase__					:    Optional[Any]												=					self.scheduler.scale_model_input(__lowerCamelCase		,__lowerCamelCase			)
										# predict the noise residual
										lowerCAmelCase__					:    Optional[int]												=					self.unet(__lowerCamelCase		,__lowerCamelCase			).sample
										# compute the previous noisy sample x_t -> x_t-1
										lowerCAmelCase__					:    str												=					self.scheduler.step(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,**__lowerCamelCase			).prev_sample

						# decode the image latents with the VQVAE
						lowerCAmelCase__					:    List[str]												=					self.vqvae.decode(__lowerCamelCase			).sample
						lowerCAmelCase__					:    Optional[int]												=					torch.clamp(__lowerCamelCase		,-1.0		,1.0			)
						lowerCAmelCase__					:    Union[str, Any]												=					image / 2 + 0.5
						lowerCAmelCase__					:    List[str]												=					image.cpu().permute(0		,2		,3		,1			).numpy()

						if output_type == "pil":
										lowerCAmelCase__					:    int												=					self.numpy_to_pil(__lowerCamelCase			)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=__lowerCamelCase			)






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 	lowerCamelCase__      (							lowerCamelCase__				,    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			)					->      Any:

						"""simple docstring"""






						return 32



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

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return self.time_input_dim



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

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						return 1_00



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

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
						return tokenizer



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    str												=					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(__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						torch.manual_seed(0			)

						lowerCAmelCase__					:    List[str]												=					{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 12,
						    '''embedding_dim''': self.text_embedder_hidden_size,
						    '''num_layers''': 1,
						}

						lowerCAmelCase__					:    List[str]												=					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
						lowerCAmelCase__					:    int												=					nn.Parameter(torch.ones(model.clip_std.shape			)			)
						return model



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

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Any												=					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__					:    int												=					CLIPVisionModelWithProjection(__lowerCamelCase			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					CLIPImageProcessor(
						    crop_size=2_24		,do_center_crop=__lowerCamelCase		,do_normalize=__lowerCamelCase		,do_resize=__lowerCamelCase		,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 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.dummy_prior
						lowerCAmelCase__					:    List[Any]												=					self.dummy_image_encoder
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    Optional[int]												=					self.dummy_tokenizer
						lowerCAmelCase__					:    str												=					self.dummy_image_processor

						lowerCAmelCase__					:    Union[str, Any]												=					UnCLIPScheduler(
						    variance_type='''fixed_small_log'''		,prediction_type='''sample'''		,num_train_timesteps=10_00		,clip_sample=__lowerCamelCase		,clip_sample_range=10.0		,)

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

						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      int:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''cpu'''

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

						lowerCAmelCase__					:    int												=					self.pipeline_class(**__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					pipe.to(__lowerCamelCase			)

						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Union[str, Any]												=					output.image_embeds

						lowerCAmelCase__					:    Tuple												=					pipe(
						    **self.get_dummy_inputs(__lowerCamelCase			)		,return_dict=__lowerCamelCase		,)[0]

						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -10:]
						lowerCAmelCase__					:    str												=					image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCAmelCase__					:    int												=					np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

						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			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    List[Any]												=					True
						lowerCAmelCase__					:    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 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    int												=					False

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

