"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :	List[Any] 					=     logging.get_logger(__name__)

_a   :	int 					=     {
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    Optional[int]	      =	"vivit"






     def __init__(	self       ,  a__=224       ,  a__=32       ,  a__=[2, 16, 16]       ,  a__=3       ,  a__=768       ,  a__=12       ,  a__=12       ,  a__=3072       ,  a__="gelu_fast"       ,  a__=0.0       ,  a__=0.0       ,  a__=0.0_2       ,  a__=1e-06       ,  a__=True       ,  **a__       ,  ):
       _lowerCAmelCase		: str						    =  hidden_size
       _lowerCAmelCase		: str						    =  num_hidden_layers
       _lowerCAmelCase		: List[str]						    =  num_attention_heads
       _lowerCAmelCase		: int						    =  intermediate_size
       _lowerCAmelCase		: List[Any]						    =  hidden_act
       _lowerCAmelCase		: Union[str, Any]						    =  hidden_dropout_prob
       _lowerCAmelCase		: List[Any]						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: str						    =  initializer_range
       _lowerCAmelCase		: List[Any]						    =  layer_norm_eps

       _lowerCAmelCase		: Optional[Any]						    =  image_size
       _lowerCAmelCase		: Union[str, Any]						    =  num_frames
       _lowerCAmelCase		: Optional[Any]						    =  tubelet_size
       _lowerCAmelCase		: Any						    =  num_channels
       _lowerCAmelCase		: int						    =  qkv_bias

       super().__init__(**a__						)





"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_a   :	Union[str, Any] 					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_a   :	int 					=     '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Dict=8			) ->   Any:
  _lowerCAmelCase		: List[Any]						    =  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCAmelCase		: Optional[Any]						    =  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Any=512    ,_lowerCamelCase		: Dict=512			) ->   List[Any]:
  _lowerCAmelCase		: Any						    =  pil_image.resize((w, h)    ,resample=Image.BICUBIC    ,reducing_gap=1			)
  _lowerCAmelCase		: Dict						    =  np.array(pil_image.convert("""RGB"""			)			)
  _lowerCAmelCase		: List[str]						    =  arr.astype(np.floataa			) / 1_27.5 - 1
  _lowerCAmelCase		: int						    =  np.transpose(_lowerCamelCase    ,[2, 0, 1]			)
  _lowerCAmelCase		: Optional[Any]						    =  torch.from_numpy(_lowerCamelCase			).unsqueeze(0			)
  return image






class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()

       self.register_modules(
           unet=a__       ,  scheduler=a__       ,  movq=a__       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  2 ** (len(self.movq.config.block_out_channels						) - 1)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       # get the original timestep using init_timestep
       _lowerCAmelCase		: Optional[Any]						    =  min(int(num_inference_steps * strength						)       ,  a__						)

       _lowerCAmelCase		: List[Any]						    =  max(num_inference_steps - init_timestep       ,  0						)
       _lowerCAmelCase		: Dict						    =  self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=None						):
       if not isinstance(a__       ,  (torch.Tensor, PIL.Image.Image, list)						):
         raise ValueError(
             F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__						)}"						)

       _lowerCAmelCase		: Union[str, Any]						    =  image.to(device=a__       ,  dtype=a__						)

       _lowerCAmelCase		: int						    =  batch_size * num_images_per_prompt

       if image.shape[1] == 4:
         _lowerCAmelCase		: int						    =  image

       else:
         if isinstance(a__       ,  a__						) and len(a__						) != batch_size:
           raise ValueError(
               F"You have passed a list of generators of length {len(a__						)}, but requested an effective batch"
               F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

         elif isinstance(a__       ,  a__						):
           _lowerCAmelCase		: Optional[int]						    =  [
               self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(a__						)
           ]
           _lowerCAmelCase		: Optional[int]						    =  torch.cat(a__       ,  dim=0						)
         else:
           _lowerCAmelCase		: List[Any]						    =  self.movq.encode(a__						).latent_dist.sample(a__						)

         _lowerCAmelCase		: Dict						    =  self.movq.config.scaling_factor * init_latents

       _lowerCAmelCase		: str						    =  torch.cat([init_latents]       ,  dim=0						)

       _lowerCAmelCase		: Dict						    =  init_latents.shape
       _lowerCAmelCase		: str						    =  randn_tensor(a__       ,  generator=a__       ,  device=a__       ,  dtype=a__						)

       # get latents
       _lowerCAmelCase		: Optional[Any]						    =  self.scheduler.add_noise(a__       ,  a__       ,  a__						)

       _lowerCAmelCase		: int						    =  init_latents

       return latents






     def 					__A						(	self       ,  a__=0						):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

       _lowerCAmelCase		: str						    =  torch.device(F"cuda:{gpu_id}"						)

       _lowerCAmelCase		: int						    =  [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(a__       ,  a__						)






     def 					__A						(	self       ,  a__=0						):
       if is_accelerate_available() and is_accelerate_version(""">="""       ,  """0.17.0.dev0"""						):
         from accelerate import cpu_offload_with_hook
       else:
         raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""						)

       _lowerCAmelCase		: Optional[int]						    =  torch.device(F"cuda:{gpu_id}"						)

       if self.device.type != "cpu":
         self.to("""cpu"""       ,  silence_dtype_warnings=a__						)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       _lowerCAmelCase		: List[str]						    =  None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  cpu_offload_with_hook(a__       ,  a__       ,  prev_module_hook=a__						)

       # We'll offload the last model manually.
       _lowerCAmelCase		: Tuple						    =  hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__A						(	self						):
       if not hasattr(self.unet       ,  """_hf_hook"""						):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(a__       ,  """_hf_hook"""						)
             and hasattr(module._hf_hook       ,  """execution_device"""						)
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device						)
       return self.device






     @torch.no_grad()
     @replace_example_docstring(a__						)
     def __call__(	self       ,  a__       ,  a__       ,  a__       ,  a__ = 512       ,  a__ = 512       ,  a__ = 100       ,  a__ = 4.0       ,  a__ = 0.3       ,  a__ = 1       ,  a__ = None       ,  a__ = "pil"       ,  a__ = True       ,  ):
       _lowerCAmelCase		: Dict						    =  self._execution_device

       _lowerCAmelCase		: Optional[Any]						    =  guidance_scale > 1.0

       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Dict						    =  torch.cat(a__       ,  dim=0						)
       _lowerCAmelCase		: Dict						    =  image_embeds.shape[0]
       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: List[Any]						    =  torch.cat(a__       ,  dim=0						)

       if do_classifier_free_guidance:
         _lowerCAmelCase		: int						    =  image_embeds.repeat_interleave(a__       ,  dim=0						)
         _lowerCAmelCase		: Any						    =  negative_image_embeds.repeat_interleave(a__       ,  dim=0						)

       _lowerCAmelCase		: Optional[int]						    =  torch.cat([negative_image_embeds, image_embeds]       ,  dim=0						).to(dtype=self.unet.dtype       ,  device=a__						)

       if not isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Any						    =  [image]
       if not all(isinstance(a__       ,  (PIL.Image.Image, torch.Tensor)						) for i in image						):
         raise ValueError(
             F"Input is in incorrect format: {[type(a__						) for i in image]}. Currently, we only support  PIL image and pytorch tensor"						)

       _lowerCAmelCase		: Tuple						    =  torch.cat([prepare_image(a__       ,  a__       ,  a__						) for i in image]       ,  dim=0						)
       _lowerCAmelCase		: Union[str, Any]						    =  image.to(dtype=image_embeds.dtype       ,  device=a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  self.movq.encode(a__						)["""latents"""]
       _lowerCAmelCase		: Tuple						    =  latents.repeat_interleave(a__       ,  dim=0						)
       self.scheduler.set_timesteps(a__       ,  device=a__						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  self.get_timesteps(a__       ,  a__       ,  a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  downscale_height_and_width(a__       ,  a__       ,  self.movq_scale_factor						)
       _lowerCAmelCase		: List[str]						    =  self.prepare_latents(
           a__       ,  a__       ,  a__       ,  a__       ,  image_embeds.dtype       ,  a__       ,  a__						)
       for i, t in enumerate(self.progress_bar(a__						)						):
         # expand the latents if we are doing classifier free guidance
         _lowerCAmelCase		: List[str]						    =  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

         _lowerCAmelCase		: int						    =  {"""image_embeds""": image_embeds}
         _lowerCAmelCase		: List[str]						    =  self.unet(
             sample=a__       ,  timestep=a__       ,  encoder_hidden_states=a__       ,  added_cond_kwargs=a__       ,  return_dict=a__       ,  )[0]

         if do_classifier_free_guidance:
           _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  noise_pred.split(latents.shape[1]       ,  dim=1						)
           _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  noise_pred.chunk(2						)
           _lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  variance_pred.chunk(2						)
           _lowerCAmelCase		: List[str]						    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
           _lowerCAmelCase		: List[str]						    =  torch.cat([noise_pred, variance_pred_text]       ,  dim=1						)

         if not (
             hasattr(self.scheduler.config       ,  """variance_type"""						)
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
           _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  noise_pred.split(latents.shape[1]       ,  dim=1						)

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase		: List[str]						    =  self.scheduler.step(
             a__       ,  a__       ,  a__       ,  generator=a__       ,  )[0]

       # post-processing
       _lowerCAmelCase		: int						    =  self.movq.decode(a__       ,  force_not_quantize=a__						)["""sample"""]

       if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"						)

       if output_type in ["np", "pil"]:
         _lowerCAmelCase		: List[Any]						    =  image * 0.5 + 0.5
         _lowerCAmelCase		: Any						    =  image.clamp(0       ,  1						)
         _lowerCAmelCase		: Tuple						    =  image.cpu().permute(0       ,  2       ,  3       ,  1						).float().numpy()

       if output_type == "pil":
         _lowerCAmelCase		: List[str]						    =  self.numpy_to_pil(a__						)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__						)




