





from typing import List, Optional, Union

import torch

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,
)


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

_snake_case  :   Optional[Any]						   =  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'

def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]=8     ):
   __lowerCAmelCase							     =     height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   __lowerCAmelCase							     =     width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class     _UpperCAmelCase   (   snake_case_   ):

   """simple docstring"""


   def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		UNetaDConditionModel ,  lowerCAmelCase_			:		DDPMScheduler ,  lowerCAmelCase_			:		VQModel ,  )							->						str:
      super().__init__()

      self.register_modules(
          unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_ ,  movq=lowerCAmelCase_ ,  )
      __lowerCAmelCase							     =     2 ** (len(self.movq.config.block_out_channels					) - 1)






   def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		Optional[int]					)							->						Tuple:





      if latents is None:
         __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=lowerCAmelCase_ ,  dtype=lowerCAmelCase_					)
      else:
         if latents.shape != shape:
            raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""					)
         __lowerCAmelCase							     =     latents.to(lowerCAmelCase_					)

      __lowerCAmelCase							     =     latents * scheduler.init_noise_sigma
      return latents






   def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		List[str]=0					)							->						Dict:





      if is_accelerate_available():
         from accelerate import cpu_offload
      else:
         raise ImportError('Please install accelerate via `pip install accelerate`'					)

      __lowerCAmelCase							     =     torch.device(f"""cuda:{gpu_id}"""					)

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






   def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Any=0					)							->						Tuple:





      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							     =     torch.device(f"""cuda:{gpu_id}"""					)

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

      __lowerCAmelCase							     =     None
      for cpu_offloaded_model in [self.unet, self.movq]:
         __lowerCAmelCase							     =     cpu_offload_with_hook(lowerCAmelCase_ ,  lowerCAmelCase_ ,  prev_module_hook=lowerCAmelCase_					)

      # We'll offload the last model manually.
      __lowerCAmelCase							     =     hook






   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def     lowercase		(    self			:		str					)							->						Optional[int]:





      if not hasattr(self.unet ,  '_hf_hook'					):
         return self.device
      for module in self.unet.modules():
         if (
             hasattr(lowerCAmelCase_ ,  '_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(lowerCAmelCase_					)
   def __call__(    self			:		int ,  lowerCAmelCase_			:		Union[torch.FloatTensor, List[torch.FloatTensor]] ,  lowerCAmelCase_			:		Union[torch.FloatTensor, List[torch.FloatTensor]] ,  lowerCAmelCase_			:		int = 5_1_2 ,  lowerCAmelCase_			:		int = 5_1_2 ,  lowerCAmelCase_			:		int = 1_0_0 ,  lowerCAmelCase_			:		float = 4.0 ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  lowerCAmelCase_			:		Optional[torch.FloatTensor] = None ,  lowerCAmelCase_			:		Optional[str] = "pil" ,  lowerCAmelCase_			:		bool = True ,  )							->						Dict:
      __lowerCAmelCase							     =     self._execution_device

      __lowerCAmelCase							     =     guidance_scale > 1.0

      if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
         __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_ ,  dim=0					)
      __lowerCAmelCase							     =     image_embeds.shape[0] * num_images_per_prompt
      if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
         __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_ ,  dim=0					)

      if do_classifier_free_guidance:
         __lowerCAmelCase							     =     image_embeds.repeat_interleave(lowerCAmelCase_ ,  dim=0					)
         __lowerCAmelCase							     =     negative_image_embeds.repeat_interleave(lowerCAmelCase_ ,  dim=0					)

      __lowerCAmelCase							     =     torch.cat([negative_image_embeds, image_embeds] ,  dim=0					).to(dtype=self.unet.dtype ,  device=lowerCAmelCase_					)

      self.scheduler.set_timesteps(lowerCAmelCase_ ,  device=lowerCAmelCase_					)
      __lowerCAmelCase							     =     self.scheduler.timesteps

      __lowerCAmelCase							     =     self.unet.config.in_channels

      __lowerCAmelCase							     =     downscale_height_and_width(lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.movq_scale_factor					)

      # create initial latent
      __lowerCAmelCase							     =     self.prepare_latents(
          (batch_size, num_channels_latents, height, width) ,  image_embeds.dtype ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.scheduler ,  )

      for i, t in enumerate(self.progress_bar(lowerCAmelCase_					)					):
         # expand the latents if we are doing classifier free guidance
         __lowerCAmelCase							     =     torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

         __lowerCAmelCase							     =     {'image_embeds': image_embeds}
         __lowerCAmelCase							     =     self.unet(
             sample=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  encoder_hidden_states=lowerCAmelCase_ ,  added_cond_kwargs=lowerCAmelCase_ ,  return_dict=lowerCAmelCase_ ,  )[0]

         if do_classifier_free_guidance:
            __lowerCAmelCase							     =     noise_pred.split(latents.shape[1] ,  dim=1					)
            __lowerCAmelCase							     =     noise_pred.chunk(2					)
            __lowerCAmelCase							     =     variance_pred.chunk(2					)
            __lowerCAmelCase							     =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            __lowerCAmelCase							     =     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							     =     noise_pred.split(latents.shape[1] ,  dim=1					)

         # compute the previous noisy sample x_t -> x_t-1
         __lowerCAmelCase							     =     self.scheduler.step(
             lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  )[0]
      # post-processing
      __lowerCAmelCase							     =     self.movq.decode(lowerCAmelCase_ ,  force_not_quantize=lowerCAmelCase_					)['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							     =     image * 0.5 + 0.5
         __lowerCAmelCase							     =     image.clamp(0 ,  1					)
         __lowerCAmelCase							     =     image.cpu().permute(0 ,  2 ,  3 ,  1					).float().numpy()

      if output_type == "pil":
         __lowerCAmelCase							     =     self.numpy_to_pil(lowerCAmelCase_					)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCAmelCase_					)








import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Optional[int]:
     __lowerCAmelCase							     =     1_0






  def     lowercase		(    self			:		int					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     [1, 2, 3, 4]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						List[str]:
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Any					)							->						Optional[Any]:
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		List[str]					)							->						Any:
     __lowerCAmelCase							     =     'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)
     self.assertEqual(lowerCAmelCase_ ,  []					)






  def     lowercase		(    self			:		Any					)							->						str:
     __lowerCAmelCase							     =     ''
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)
     self.assertEqual(lowerCAmelCase_ ,  []					)
     self.assertEqual(lowerCAmelCase_ ,  []					)






  def     lowercase		(    self			:		int					)							->						int:
     __lowerCAmelCase							     =     (
         'It was the year of Our Lord one thousand seven hundred and '
         'seventy-five\n\nSpiritual revelations were conceded to England '
         'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
     )
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)

     __lowerCAmelCase							     =     [
         'It was the year of Our Lord one thousand seven hundred and seventy-five.',
         'Spiritual revelations were conceded to England at that favoured period, as at this.',
     ]
     self.assertEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     ['It was the best of times.']
     self.assertEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						Any:
     __lowerCAmelCase							     =     torch.tensor([1, 2, 3, 4]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  0					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  2_3					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		str					)							->						List[Any]:
     __lowerCAmelCase							     =     torch.tensor([8, 2, 3, 4, 1, 1, 1]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  1					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     1_0_1
     __lowerCAmelCase							     =     torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]					)
     __lowerCAmelCase							     =     torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]					)

     __lowerCAmelCase							     =     compute_token_type_ids(lowerCAmelCase_ ,  lowerCAmelCase_					)
     np.testing.assert_array_equal(lowerCAmelCase_ ,  lowerCAmelCase_					)

