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

lowerCamelCase_						     =					'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''



def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	int	,		__a      :	Any=8						):



     '''simple docstring'''




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



def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	Tuple=512	,		__a      :	Union[str, Any]=512						):



     '''simple docstring'''




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




class      __A(    lowerCamelCase_			):

  """simple docstring"""







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

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       # get the original timestep using init_timestep
       UpperCamelCase__				=    min(int(num_inference_steps * strength						)	,      __snake_case						)

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

       return timesteps, num_inference_steps - t_start







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None						):
       if not isinstance(__snake_case	,      (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(__snake_case						)}"						)

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

       UpperCamelCase__				=    batch_size * num_images_per_prompt

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

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

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

            UpperCamelCase__				=    self.movq.config.scaling_factor * init_latents

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

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

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

       UpperCamelCase__				=    init_latents

       return latents







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

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

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=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."""						)

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

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

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

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







  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 	UpperCAmelCase_ (self						):
       if not hasattr(self.unet	,      """_hf_hook"""						):
            return self.device
       for module in self.unet.modules():
            if (
                hasattr(__snake_case	,      """_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(__snake_case						)
  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 1_00	,      SCREAMING_SNAKE_CASE_ = 4.0	,      SCREAMING_SNAKE_CASE_ = 0.3	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = "pil"	,      SCREAMING_SNAKE_CASE_ = True	,      ):
       UpperCamelCase__				=    self._execution_device

       UpperCamelCase__				=    guidance_scale > 1.0

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

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

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

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

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

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

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

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

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

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

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=__snake_case						)







import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase_						     =					'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase_						     =					importlib.util.spec_from_file_location(
    '''transformers''',
    os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowerCamelCase_						     =					spec.loader.load_module()

lowerCamelCase_						     =					transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCamelCase_						     =					re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


lowerCamelCase_						     =					{
    '''CLIPConfigMixin''',
    '''DecisionTransformerConfigMixin''',
    '''EncoderDecoderConfigMixin''',
    '''RagConfigMixin''',
    '''SpeechEncoderDecoderConfigMixin''',
    '''VisionEncoderDecoderConfigMixin''',
    '''VisionTextDualEncoderConfigMixin''',
}






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    []

     for config_class in list(CONFIG_MAPPING.values()						):
          UpperCamelCase__				=    False

          # source code of `config_class`
          UpperCamelCase__				=    inspect.getsource(__a						)
          UpperCamelCase__				=    _re_checkpoint.findall(__a						)

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               UpperCamelCase__		,	UpperCamelCase__				=    checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               UpperCamelCase__				=    f"https://huggingface.co/{ckpt_name}"
               if ckpt_link == ckpt_link_from_name:
                    UpperCamelCase__				=    True
                    break

          UpperCamelCase__				=    config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(__a						)

     if len(__a						) > 0:
          UpperCamelCase__				=    """\n""".join(sorted(__a						)						)
          raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"						)


if __name__ == "__main__":
    check_config_docstrings_have_checkpoints()





