





'''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,
)


_SCREAMING_SNAKE_CASE     :   List[str]	 =							logging.get_logger(__name__)  # pylint: disable=invalid-name

_SCREAMING_SNAKE_CASE     :   Optional[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(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]=8    ):



    '''simple docstring'''





    _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


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					List[Any]=512		,       SCREAMING_SNAKE_CASE_  :					List[str]=512    ):



    '''simple docstring'''





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





class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     )    ->							Optional[Any]:
     super().__init__()

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




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

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

     return timesteps, num_inference_steps - t_start




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None		)    ->							List[Any]:
     if not isinstance(_lowerCAmelCase			,     (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(_lowerCAmelCase		)}'''		)

     _lowerCAmelCase				=      image.to(device=_lowerCAmelCase			,     dtype=_lowerCAmelCase		)

     _lowerCAmelCase				=      batch_size * num_images_per_prompt

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

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

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

         _lowerCAmelCase				=      self.movq.config.scaling_factor * init_latents

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

     _lowerCAmelCase				=      init_latents.shape
     _lowerCAmelCase				=      randn_tensor(_lowerCAmelCase			,     generator=_lowerCAmelCase			,     device=_lowerCAmelCase			,     dtype=_lowerCAmelCase		)

     # get latents
     _lowerCAmelCase				=      self.scheduler.add_noise(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      init_latents

     return latents




 def 							_snake_case   (	self			,     _lowerCAmelCase=0		)    ->							List[str]:
     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 							_snake_case   (	self			,     _lowerCAmelCase=0		)    ->							Optional[Any]:
     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			,			_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 							_snake_case   (	self		)    ->							Tuple:
     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			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = 512			,     _lowerCAmelCase = 512			,     _lowerCAmelCase = 100			,     _lowerCAmelCase = 4.0			,     _lowerCAmelCase = 0.3			,     _lowerCAmelCase = 1			,     _lowerCAmelCase = None			,     _lowerCAmelCase = "pil"			,     _lowerCAmelCase = True			,     )    ->							Tuple:
     _lowerCAmelCase				=      self._execution_device

     _lowerCAmelCase				=      guidance_scale > 1.0

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

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

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

     _lowerCAmelCase				=      self.movq.encode(_lowerCAmelCase		)["latents"]
     _lowerCAmelCase				=      latents.repeat_interleave(_lowerCAmelCase			,     dim=0		)
     self.scheduler.set_timesteps(_lowerCAmelCase			,     device=_lowerCAmelCase		)
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_timesteps(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      timesteps[:1].repeat(batch_size * num_images_per_prompt		)
     _lowerCAmelCase			,			_lowerCAmelCase				=      downscale_height_and_width(_lowerCAmelCase			,     _lowerCAmelCase			,     self.movq_scale_factor		)
     _lowerCAmelCase				=      self.prepare_latents(
         _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     image_embeds.dtype			,     _lowerCAmelCase			,     _lowerCAmelCase		)
     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			,			_lowerCAmelCase				=      noise_pred.split(latents.shape[1]			,     dim=1		)
             _lowerCAmelCase			,			_lowerCAmelCase				=      noise_pred.chunk(2		)
             _lowerCAmelCase			,			_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			,			_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		)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


