
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,
)


__lowerCAmelCase			:     Dict           =   logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCAmelCase			:     Any           =   '\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 a__    (       A_,     A_,     A_=8					):






  '''simple docstring'''





  __magic_name__				     =       height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __magic_name__				     =       width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor

def a__    (       A_,     A_=512,     A_=512					):






  '''simple docstring'''





  __magic_name__				     =       pil_image.resize((w, h),     resample=Image.BICUBIC,     reducing_gap=1					)
  __magic_name__				     =       np.array(pil_image.convert("""RGB"""					)					)
  __magic_name__				     =       arr.astype(np.floataa					) / 127.5 - 1
  __magic_name__				     =       np.transpose(A_,     [2, 0, 1]					)
  __magic_name__				     =       torch.from_numpy(A_					).unsqueeze(0					)
  return image





class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  def __init__( self			: Optional[Any]			,							UpperCamelCase__			: UNetaDConditionModel			,							UpperCamelCase__			: DDPMScheduler			,							UpperCamelCase__			: VQModel			,							)			->	int:







    """simple docstring"""






    super().__init__()

    self.register_modules(
        unet=UpperCamelCase__			,							scheduler=UpperCamelCase__			,							movq=UpperCamelCase__			,							)
    __magic_name__				     =       2 ** (len(self.movq.config.block_out_channels    ) - 1)
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Optional[int]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       min(int(num_inference_steps * strength    )			,							UpperCamelCase__    )

    __magic_name__				     =       max(num_inference_steps - init_timestep			,							0    )
    __magic_name__				     =       self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Tuple=None    )			->	Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__			,							(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(UpperCamelCase__    )}'''    )

    __magic_name__				     =       image.to(device=UpperCamelCase__			,							dtype=UpperCamelCase__    )

    __magic_name__				     =       batch_size * num_images_per_prompt

    if image.shape[1] == 4:
      __magic_name__				     =       image

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

      elif isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
        __magic_name__				     =       [
            self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(UpperCamelCase__    )
        ]
        __magic_name__				     =       torch.cat(UpperCamelCase__			,							dim=0    )
      else:
        __magic_name__				     =       self.movq.encode(UpperCamelCase__    ).latent_dist.sample(UpperCamelCase__    )

      __magic_name__				     =       self.movq.config.scaling_factor * init_latents

    __magic_name__				     =       torch.cat([init_latents]			,							dim=0    )

    __magic_name__				     =       init_latents.shape
    __magic_name__				     =       randn_tensor(UpperCamelCase__			,							generator=UpperCamelCase__			,							device=UpperCamelCase__			,							dtype=UpperCamelCase__    )

    # get latents
    __magic_name__				     =       self.scheduler.add_noise(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )

    __magic_name__				     =       init_latents

    return latents
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: Tuple=0    )			->	List[Any]:







    """simple docstring"""






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

    __magic_name__				     =       torch.device(F'''cuda:{gpu_id}'''    )

    __magic_name__				     =       [
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
      if cpu_offloaded_model is not None:
        cpu_offload(UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: Any=0    )			->	str:







    """simple docstring"""






    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."""    )

    __magic_name__				     =       torch.device(F'''cuda:{gpu_id}'''    )

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

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

    # We'll offload the last model manually.
    __magic_name__				     =       hook
  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 							_lowercase							( self			: List[str]    )			->	List[str]:







    """simple docstring"""






    if not hasattr(self.unet			,							"""_hf_hook"""    ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(UpperCamelCase__			,							"""_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(UpperCamelCase__    )
  def __call__( self			: Optional[int]			,							UpperCamelCase__			: Union[torch.FloatTensor, List[torch.FloatTensor]]			,							UpperCamelCase__			: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]			,							UpperCamelCase__			: Union[torch.FloatTensor, List[torch.FloatTensor]]			,							UpperCamelCase__			: int = 512			,							UpperCamelCase__			: int = 512			,							UpperCamelCase__			: int = 100			,							UpperCamelCase__			: float = 4.0			,							UpperCamelCase__			: float = 0.3			,							UpperCamelCase__			: int = 1			,							UpperCamelCase__			: Optional[Union[torch.Generator, List[torch.Generator]]] = None			,							UpperCamelCase__			: Optional[str] = "pil"			,							UpperCamelCase__			: bool = True			,							)			->	int:







    """simple docstring"""






    __magic_name__				     =       self._execution_device

    __magic_name__				     =       guidance_scale > 1.0

    if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
      __magic_name__				     =       torch.cat(UpperCamelCase__			,							dim=0    )
    __magic_name__				     =       image_embeds.shape[0]
    if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
      __magic_name__				     =       torch.cat(UpperCamelCase__			,							dim=0    )

    if do_classifier_free_guidance:
      __magic_name__				     =       image_embeds.repeat_interleave(UpperCamelCase__			,							dim=0    )
      __magic_name__				     =       negative_image_embeds.repeat_interleave(UpperCamelCase__			,							dim=0    )

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

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

    __magic_name__				     =       torch.cat([prepare_image(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    ) for i in image]			,							dim=0    )
    __magic_name__				     =       image.to(dtype=image_embeds.dtype			,							device=UpperCamelCase__    )

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

      __magic_name__				     =       {"""image_embeds""": image_embeds}
      __magic_name__				     =       self.unet(
          sample=UpperCamelCase__			,							timestep=UpperCamelCase__			,							encoder_hidden_states=UpperCamelCase__			,							added_cond_kwargs=UpperCamelCase__			,							return_dict=UpperCamelCase__			,							)[0]

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

      # compute the previous noisy sample x_t -> x_t-1
      __magic_name__				     =       self.scheduler.step(
          UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							generator=UpperCamelCase__			,							)[0]

    # post-processing
    __magic_name__				     =       self.movq.decode(UpperCamelCase__			,							force_not_quantize=UpperCamelCase__    )["""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"]:
      __magic_name__				     =       image * 0.5 + 0.5
      __magic_name__				     =       image.clamp(0			,							1    )
      __magic_name__				     =       image.cpu().permute(0			,							2			,							3			,							1    ).float().numpy()

    if output_type == "pil":
      __magic_name__				     =       self.numpy_to_pil(UpperCamelCase__    )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=UpperCamelCase__    )



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase			:     int           =   subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase			:     Any           =   (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
)

__lowerCAmelCase			:     str           =   '|'.join(sys.argv[1:])
__lowerCAmelCase			:     Tuple           =   re.compile(RF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase			:     Union[str, Any]           =   [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')

