




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

a							      =     '''
    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 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   Any							,    _snake_case				:   List[str]=8				)       ->   Any:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   Union[str, Any]=5_12							,    _snake_case				:   Union[str, Any]=5_12				)       ->   List[str]:






      '''simple docstring'''


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







class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDConditionModel       ,				_UpperCAmelCase			:			DDPMScheduler       ,				_UpperCAmelCase			:			VQModel       ,				):
       super().__init__()

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

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			Dict						):
       # get the original timestep using init_timestep
       _A     =  min(int(num_inference_steps * strength						)       ,				_UpperCAmelCase						)

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

       return timesteps, num_inference_steps - t_start

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Optional[int]=None						):
       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						)}'''						)

       _A     =  image.to(device=_UpperCAmelCase       ,				dtype=_UpperCAmelCase						)

       _A     =  batch_size * num_images_per_prompt

       if image.shape[1] == 4:
             _A     =  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						):
                   _A     =  [
                       self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(_UpperCAmelCase						)
                   ]
                   _A     =  torch.cat(_UpperCAmelCase       ,				dim=0						)
             else:
                   _A     =  self.movq.encode(_UpperCAmelCase						).latent_dist.sample(_UpperCAmelCase						)

             _A     =  self.movq.config.scaling_factor * init_latents

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

       _A     =  init_latents.shape
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=_UpperCAmelCase       ,				dtype=_UpperCAmelCase						)

       # get latents
       _A     =  self.scheduler.add_noise(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  init_latents

       return latents

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Tuple=0						):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('Please install accelerate via `pip install accelerate`'						)

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

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

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			List[str]=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.'						)

       _A     =  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)

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

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

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def      lowerCAmelCase_				(					self			:			Tuple						):
       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			:			Any       ,				_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       ,				):
       _A     =  self._execution_device

       _A     =  guidance_scale > 1.0

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

       if do_classifier_free_guidance:
             _A     =  image_embeds.repeat_interleave(_UpperCAmelCase       ,				dim=0						)
             _A     =  negative_image_embeds.repeat_interleave(_UpperCAmelCase       ,				dim=0						)

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

       if not isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						):
             _A     =  [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'''						)

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

       _A     =  self.movq.encode(_UpperCAmelCase						)['latents']
       _A     =  latents.repeat_interleave(_UpperCAmelCase       ,				dim=0						)
       self.scheduler.set_timesteps(_UpperCAmelCase       ,				device=_UpperCAmelCase						)
       _A  ,       _A     =  self.get_timesteps(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
       _A     =  timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       _A  ,       _A     =  downscale_height_and_width(_UpperCAmelCase       ,				_UpperCAmelCase       ,				self.movq_scale_factor						)
       _A     =  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
             _A     =  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

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

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

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

       # post-processing
       _A     =  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"]:
             _A     =  image * 0.5 + 0.5
             _A     =  image.clamp(0       ,				1						)
             _A     =  image.cpu().permute(0       ,				2       ,				3       ,				1						).float().numpy()

       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)







"""simple docstring"""

import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


a							      =     logging.get_logger(__name__)

a							      =     {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Dict=None       ,				**_UpperCAmelCase			:			Optional[Any]						):
       logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'						)
       _A     =  model
       _A     =  kwargs.get('model_save_dir'       ,				_UpperCAmelCase						)
       _A     =  kwargs.get('latest_model_name'       ,				_UpperCAmelCase						)

 def __call__(					self			:			Dict       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  {k: np.array(_UpperCAmelCase						) for k, v in kwargs.items()}
       return self.model.run(_UpperCAmelCase       ,				_UpperCAmelCase						)

 @staticmethod
 def      lowerCAmelCase_				(					_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			List[Any]=None						):
       if provider is None:
             logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
             _A     =  'CPUExecutionProvider'

       return ort.InferenceSession(_UpperCAmelCase       ,				providers=[provider]       ,				sess_options=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

       _A     =  self.model_save_dir.joinpath(self.latest_model_name						)
       _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
       try:
             shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
       except shutil.SameFileError:
             pass

       # copy external weights (for models >2GB)
       _A     =  self.model_save_dir.joinpath(_UpperCAmelCase						)
       if src_path.exists():
             _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
             try:
                   shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
             except shutil.SameFileError:
                   pass

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, os.PathLike]       ,				**_UpperCAmelCase			:			List[str]       ,				):
       if os.path.isfile(_UpperCAmelCase						):
             logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''						)
             return

       os.makedirs(_UpperCAmelCase       ,				exist_ok=_UpperCAmelCase						)

       # saving model weights/files
       self._save_pretrained(_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @classmethod
 def      lowerCAmelCase_				(					cls			:			Tuple       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[Union[bool, str, None]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, None]] = None       ,				_UpperCAmelCase			:			bool = False       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional["ort.SessionOptions"] = None       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
       # load model from local directory
       if os.path.isdir(_UpperCAmelCase						):
             _A     =  OnnxRuntimeModel.load_model(
                 os.path.join(_UpperCAmelCase       ,				_UpperCAmelCase						)       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
             _A     =  Path(_UpperCAmelCase						)
       # load model from hub
       else:
             # download model
             _A     =  hf_hub_download(
                 repo_id=_UpperCAmelCase       ,				filename=_UpperCAmelCase       ,				use_auth_token=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				cache_dir=_UpperCAmelCase       ,				force_download=_UpperCAmelCase       ,				)
             _A     =  Path(_UpperCAmelCase						).parent
             _A     =  Path(_UpperCAmelCase						).name
             _A     =  OnnxRuntimeModel.load_model(_UpperCAmelCase       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
       return cls(model=_UpperCAmelCase       ,				**_UpperCAmelCase						)



 @classmethod
 def      lowerCAmelCase_				(					cls			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  None
       if len(str(_UpperCAmelCase						).split('@'						)						) == 2:
             _A  ,       _A     =  model_id.split('@'						)

       return cls._from_pretrained(
           model_id=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				cache_dir=_UpperCAmelCase       ,				force_download=_UpperCAmelCase       ,				use_auth_token=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

