




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def __init__(				self					:  List[Any]						,							A_					:  int						,							A_					:  List[str]=7						,							A_					:  Dict=3						,							A_					:  List[str]=10						,							A_					:  str=18						,							A_					:  str=30						,							A_					:  List[str]=400						,							A_					:  Tuple=True						,							A_					:  Dict=None						,							A_					:  Optional[int]=True						,							A_					:  Any=[0.5, 0.5, 0.5]						,							A_					:  Any=[0.5, 0.5, 0.5]						,							A_					:  Optional[Any]=None						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 18}
       lowerCamelCase_		     =    crop_size if crop_size is not None else {'height': 18, 'width': 18}

       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    num_channels
       lowerCamelCase_		     =    num_frames
       lowerCamelCase_		     =    image_size
       lowerCamelCase_		     =    min_resolution
       lowerCamelCase_		     =    max_resolution
       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean
       lowerCamelCase_		     =    image_std
       lowerCamelCase_		     =    crop_size





 def 				a__ (				self					:  str  )   ->	Any:

       """simple docstring"""






       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }







@require_torch
@require_vision
class   A(						UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						VivitImageProcessor if is_vision_available() else None

 def 				a__ (				self					:  Dict  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    VivitImageProcessingTester(self  )

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

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def 				a__ (				self					:  Optional[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'image_mean'  )  )
       self.assertTrue(hasattr(A_						,							'image_std'  )  )
       self.assertTrue(hasattr(A_						,							'do_normalize'  )  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'do_center_crop'  )  )
       self.assertTrue(hasattr(A_						,							'size'  )  )

 def 				a__ (				self					:  List[str]  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size						,							{'shortest_edge': 18}  )
       self.assertEqual(image_processor.crop_size						,							{'height': 18, 'width': 18}  )

       lowerCamelCase_		     =    self.image_processing_class.from_dict(self.image_processor_dict						,							size=42						,							crop_size=84  )
       self.assertEqual(image_processor.size						,							{'shortest_edge': 42}  )
       self.assertEqual(image_processor.crop_size						,							{'height': 84, 'width': 84}  )

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

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # create random PIL videos
       lowerCamelCase_		     =    prepare_video_inputs(self.image_processor_tester						,							equal_resolution=A_  )
       for video in video_inputs:
             self.assertIsInstance(A_						,							A_  )
             self.assertIsInstance(video[0]						,							Image.Image  )

       # Test not batched input
       lowerCamelCase_		     =    image_processing(video_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_videos.shape						,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_		     =    image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_videos.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

 def 				a__ (				self					:  str  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       lowerCamelCase_		     =    prepare_video_inputs(self.image_processor_tester						,							equal_resolution=A_						,							numpify=A_  )
       for video in video_inputs:
             self.assertIsInstance(A_						,							A_  )
             self.assertIsInstance(video[0]						,							np.ndarray  )

       # Test not batched input
       lowerCamelCase_		     =    image_processing(video_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_videos.shape						,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_		     =    image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_videos.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)





 def 				a__ (				self					:  str  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       lowerCamelCase_		     =    prepare_video_inputs(self.image_processor_tester						,							equal_resolution=A_						,							torchify=A_  )
       for video in video_inputs:
             self.assertIsInstance(A_						,							A_  )
             self.assertIsInstance(video[0]						,							torch.Tensor  )

       # Test not batched input
       lowerCamelCase_		     =    image_processing(video_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_videos.shape						,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_		     =    image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_videos.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)









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_  )



