


"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    KandinskyVaaImgaImgPipeline
     __UpperCamelCase     =    ['''image_embeds''', '''negative_image_embeds''', '''image''']
     __UpperCamelCase     =    [
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     __UpperCamelCase     =    [
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     __UpperCamelCase     =    False






     @property
     def    lowerCamelCase__   (		self     :    str ):
           return 3_2






     @property
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           return 3_2






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






     @property
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           return self.time_input_dim * 4






     @property
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           return 1_0_0






     @property
     def    lowerCamelCase__   (		self     :    Any ):
           torch.manual_seed(0 )

           lowerCAmelCase    :		Optional[Any]				=      {
               '''in_channels''': 4,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 8,
               '''addition_embed_type''': '''image''',
               '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
               '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
               '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
               '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
               '''layers_per_block''': 1,
               '''encoder_hid_dim''': self.text_embedder_hidden_size,
               '''encoder_hid_dim_type''': '''image_proj''',
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': None,
           }

           lowerCAmelCase    :		List[str]				=      UNetaDConditionModel(**UpperCamelCase_ )
           return model






     @property
     def    lowerCamelCase__   (		self     :    str ):
           return {
               "block_out_channels": [3_2, 6_4],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 1_2,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }






     @property
     def    lowerCamelCase__   (		self     :    List[Any] ):
           torch.manual_seed(0 )
           lowerCAmelCase    :		int				=      VQModel(**self.dummy_movq_kwargs )
           return model






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		Dict				=      self.dummy_unet
           lowerCAmelCase    :		Any				=      self.dummy_movq

           lowerCAmelCase    :		Dict				=      {
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_schedule''': '''linear''',
               '''beta_start''': 0.00_085,
               '''beta_end''': 0.012,
               '''clip_sample''': False,
               '''set_alpha_to_one''': False,
               '''steps_offset''': 0,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
           }

           lowerCAmelCase    :		Optional[int]				=      DDIMScheduler(**UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Dict=0 ):
           lowerCAmelCase    :		Dict				=      floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(seed + 1 ) ).to(
               UpperCamelCase_ )
           # create init_image
           lowerCAmelCase    :		List[Any]				=      floats_tensor((1, 3, 6_4, 6_4)    , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      image.cpu().permute(0    , 2    , 3    , 1 )[0]
           lowerCAmelCase    :		Tuple				=      Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )

           if str(UpperCamelCase_ ).startswith('''mps''' ):
                 lowerCAmelCase    :		Optional[Any]				=      torch.manual_seed(UpperCamelCase_ )
           else:
                 lowerCAmelCase    :		int				=      torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      {
               '''image''': init_image,
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 6_4,
               '''width''': 6_4,
               '''num_inference_steps''': 1_0,
               '''guidance_scale''': 7.0,
               '''strength''': 0.2,
               '''output_type''': '''np''',
           }
           return inputs






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		int				=      '''cpu'''

           lowerCAmelCase    :		int				=      self.get_dummy_components()

           lowerCAmelCase    :		Optional[Any]				=      self.pipeline_class(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      pipe.to(UpperCamelCase_ )

           pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           lowerCAmelCase    :		Any				=      pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
           lowerCAmelCase    :		Tuple				=      output.images

           lowerCAmelCase    :		str				=      pipe(
               **self.get_dummy_inputs(UpperCamelCase_ )    , return_dict=UpperCamelCase_    , )[0]

           lowerCAmelCase    :		Union[str, Any]				=      image[0, -3:, -3:, -1]
           lowerCAmelCase    :		Dict				=      image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 6_4, 6_4, 3)

           lowerCAmelCase    :		Tuple				=      np.array(
               [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
           assert (
               np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
           ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



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






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		List[str]				=      load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )

           lowerCAmelCase    :		List[str]				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
           lowerCAmelCase    :		Union[str, Any]				=      '''A red cartoon frog, 4k'''

           lowerCAmelCase    :		Any				=      KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''    , torch_dtype=torch.floataa )
           pipe_prior.to(UpperCamelCase_ )

           lowerCAmelCase    :		Union[str, Any]				=      KandinskyVaaImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''    , torch_dtype=torch.floataa )
           lowerCAmelCase    :		Dict				=      pipeline.to(UpperCamelCase_ )

           pipeline.set_progress_bar_config(disable=UpperCamelCase_ )

           lowerCAmelCase    :		Optional[Any]				=      torch.Generator(device='''cpu''' ).manual_seed(0 )
           lowerCAmelCase,					lowerCAmelCase    :		Dict				=      pipe_prior(
               UpperCamelCase_    , generator=UpperCamelCase_    , num_inference_steps=5    , negative_prompt=''''''    , ).to_tuple()

           lowerCAmelCase    :		List[str]				=      pipeline(
               image=UpperCamelCase_    , image_embeds=UpperCamelCase_    , negative_image_embeds=UpperCamelCase_    , generator=UpperCamelCase_    , num_inference_steps=1_0_0    , height=7_6_8    , width=7_6_8    , strength=0.2    , output_type='''np'''    , )

           lowerCAmelCase    :		Dict				=      output.images[0]

           assert image.shape == (7_6_8, 7_6_8, 3)

           assert_mean_pixel_difference(UpperCamelCase_    , UpperCamelCase_ )








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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  TransformeraDModel						,							A_					:  AutoencoderKL						,							A_					:  KarrasDiffusionSchedulers						,							A_					:  Optional[Dict[int, str]] = None						,							)   ->	str:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_						,							vae=A_						,							scheduler=A_  )

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_		     =    {}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','  ):
                         lowerCamelCase_		     =    int(A_  )
             lowerCamelCase_		     =    dict(sorted(self.labels.items()  )  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, List[str]]  )   ->	List[int]:

       """simple docstring"""






       if not isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    list(A_  )

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(				self					:  Any						,							A_					:  List[int]						,							A_					:  float = 4.0						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    len(A_  )
       lowerCamelCase_		     =    self.transformer.config.sample_size
       lowerCamelCase_		     =    self.transformer.config.in_channels

       lowerCamelCase_		     =    randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)						,							generator=A_						,							device=self.device						,							dtype=self.transformer.dtype						,							)
       lowerCamelCase_		     =    torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

       lowerCamelCase_		     =    torch.tensor(A_						,							device=self.device  ).reshape(-1  )
       lowerCamelCase_		     =    torch.tensor([1000] * batch_size						,							device=self.device  )
       lowerCamelCase_		     =    torch.cat([class_labels, class_null]						,							0  ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             if guidance_scale > 1:
                   lowerCamelCase_		     =    latent_model_input[: len(A_  ) // 2]
                   lowerCamelCase_		     =    torch.cat([half, half]						,							dim=0  )
             lowerCamelCase_		     =    self.scheduler.scale_model_input(A_						,							A_  )

             lowerCamelCase_		     =    t
             if not torch.is_tensor(A_  ):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_		     =    latent_model_input.device.type == 'mps'
                   if isinstance(A_						,							A_  ):
                         lowerCamelCase_		     =    torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_		     =    torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_		     =    torch.tensor([timesteps]						,							dtype=A_						,							device=latent_model_input.device  )
             elif len(timesteps.shape  ) == 0:
                   lowerCamelCase_		     =    timesteps[None].to(latent_model_input.device  )
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_		     =    timesteps.expand(latent_model_input.shape[0]  )
             # predict noise model_output
             lowerCamelCase_		     =    self.transformer(
                 A_						,							timestep=A_						,							class_labels=A_  ).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							len(A_  ) // 2						,							dim=0  )

                   lowerCamelCase_		     =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_		     =    torch.cat([half_eps, half_eps]						,							dim=0  )

                   lowerCamelCase_		     =    torch.cat([eps, rest]						,							dim=1  )

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							A_						,							dim=1  )
             else:
                   lowerCamelCase_		     =    noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_     ,				lowerCamelCase_		     =    latent_model_input.chunk(2						,							dim=0  )
       else:
             lowerCamelCase_		     =    latent_model_input

       lowerCamelCase_		     =    1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_		     =    self.vae.decode(A_  ).sample

       lowerCamelCase_		     =    (samples / 2 + 0.5).clamp(0						,							1  )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_		     =    samples.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()

       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(A_  )

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_  )



