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

__SCREAMING_SNAKE_CASE								="\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     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]=8   ):
     lowercase_			:       int									=			height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     lowercase_			:       Any									=			width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Dict=5_12  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=5_12   ):
     lowercase_			:       Optional[Any]									=			pil_image.resize((w, h)  ,    resample=Image.BICUBIC  ,    reducing_gap=1   )
     lowercase_			:       int									=			np.array(pil_image.convert('RGB'   )   )
     lowercase_			:       Dict									=			arr.astype(np.floataa   ) / 127.5 - 1
     lowercase_			:       str									=			np.transpose(__SCREAMING_SNAKE_CASE  ,    [2, 0, 1]   )
     lowercase_			:       List[Any]									=			torch.from_numpy(__SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
     return image







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,)			->   str:


        '''simple docstring'''


        super().__init__()

        self.register_modules(
            unet=__UpperCamelCase  ,scheduler=__UpperCamelCase  ,movq=__UpperCamelCase  ,)
        lowercase_			:       List[Any]									=			2 ** (len(self.movq.config.block_out_channels    ) - 1)
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        lowercase_			:       List[str]									=			min(int(num_inference_steps * strength    )  ,__UpperCamelCase    )

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

        return timesteps, num_inference_steps - t_start
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase=None    )			->   Optional[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    )}'''    )

        lowercase_			:       Optional[int]									=			image.to(device=__UpperCamelCase  ,dtype=__UpperCamelCase    )

        lowercase_			:       Dict									=			batch_size * num_images_per_prompt

        if image.shape[1] == 4:
             lowercase_			:       Optional[Any]									=			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    ):
                  lowercase_			:       Dict									=			[
                      self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(__UpperCamelCase    )
                  ]
                  lowercase_			:       Union[str, Any]									=			torch.cat(__UpperCamelCase  ,dim=0    )
             else:
                  lowercase_			:       Optional[Any]									=			self.movq.encode(__UpperCamelCase    ).latent_dist.sample(__UpperCamelCase    )

             lowercase_			:       List[str]									=			self.movq.config.scaling_factor * init_latents

        lowercase_			:       Any									=			torch.cat([init_latents]  ,dim=0    )

        lowercase_			:       List[Any]									=			init_latents.shape
        lowercase_			:       Any									=			randn_tensor(__UpperCamelCase  ,generator=__UpperCamelCase  ,device=__UpperCamelCase  ,dtype=__UpperCamelCase    )

        # get latents
        lowercase_			:       Tuple									=			self.scheduler.add_noise(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       int									=			init_latents

        return latents
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=0    )			->   List[str]:


        '''simple docstring'''


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

        lowercase_			:       Optional[Any]									=			torch.device(f'''cuda:{gpu_id}'''    )

        lowercase_			:       Union[str, Any]									=			[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase=0    )			->   int:


        '''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.'    )

        lowercase_			:       List[str]									=			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)

        lowercase_			:       int									=			None
        for cpu_offloaded_model in [self.unet, self.movq]:
             lowercase_			:       Tuple									=			cpu_offload_with_hook(__UpperCamelCase  ,__UpperCamelCase  ,prev_module_hook=__UpperCamelCase    )

        # We'll offload the last model manually.
        lowercase_			:       Tuple									=			hook
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''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  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = 512  ,__UpperCamelCase = 512  ,__UpperCamelCase = 100  ,__UpperCamelCase = 4.0  ,__UpperCamelCase = 0.3  ,__UpperCamelCase = 1  ,__UpperCamelCase = None  ,__UpperCamelCase = "pil"  ,__UpperCamelCase = True  ,)			->   Any:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			self._execution_device

        lowercase_			:       int									=			guidance_scale > 1.0

        if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Union[str, Any]									=			torch.cat(__UpperCamelCase  ,dim=0    )
        lowercase_			:       Union[str, Any]									=			image_embeds.shape[0]
        if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Tuple									=			torch.cat(__UpperCamelCase  ,dim=0    )

        if do_classifier_free_guidance:
             lowercase_			:       Tuple									=			image_embeds.repeat_interleave(__UpperCamelCase  ,dim=0    )
             lowercase_			:       List[str]									=			negative_image_embeds.repeat_interleave(__UpperCamelCase  ,dim=0    )

        lowercase_			:       int									=			torch.cat([negative_image_embeds, image_embeds]  ,dim=0    ).to(dtype=self.unet.dtype  ,device=__UpperCamelCase    )

        if not isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Union[str, Any]									=			[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'''    )

        lowercase_			:       Optional[int]									=			torch.cat([prepare_image(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    ) for i in image]  ,dim=0    )
        lowercase_			:       List[str]									=			image.to(dtype=image_embeds.dtype  ,device=__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			self.movq.encode(__UpperCamelCase    )['latents']
        lowercase_			:       List[Any]									=			latents.repeat_interleave(__UpperCamelCase  ,dim=0    )
        self.scheduler.set_timesteps(__UpperCamelCase  ,device=__UpperCamelCase    )
        lowercase_			:       Any									=			self.get_timesteps(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       int									=			timesteps[:1].repeat(batch_size * num_images_per_prompt    )
        lowercase_			:       Any									=			downscale_height_and_width(__UpperCamelCase  ,__UpperCamelCase  ,self.movq_scale_factor    )
        lowercase_			:       str									=			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
             lowercase_			:       Any									=			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

             lowercase_			:       Tuple									=			{'image_embeds': image_embeds}
             lowercase_			:       Dict									=			self.unet(
                 sample=__UpperCamelCase  ,timestep=__UpperCamelCase  ,encoder_hidden_states=__UpperCamelCase  ,added_cond_kwargs=__UpperCamelCase  ,return_dict=__UpperCamelCase  ,)[0]

             if do_classifier_free_guidance:
                  lowercase_			:       List[Any]									=			noise_pred.split(latents.shape[1]  ,dim=1    )
                  lowercase_			:       int									=			noise_pred.chunk(2    )
                  lowercase_			:       Tuple									=			variance_pred.chunk(2    )
                  lowercase_			:       Union[str, Any]									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  lowercase_			:       Union[str, Any]									=			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"]
             ):
                  lowercase_			:       Any									=			noise_pred.split(latents.shape[1]  ,dim=1    )

             # compute the previous noisy sample x_t -> x_t-1
             lowercase_			:       Union[str, Any]									=			self.scheduler.step(
                 __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,generator=__UpperCamelCase  ,)[0]

        # post-processing
        lowercase_			:       Any									=			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"]:
             lowercase_			:       Union[str, Any]									=			image * 0.5 + 0.5
             lowercase_			:       int									=			image.clamp(0  ,1    )
             lowercase_			:       Dict									=			image.cpu().permute(0  ,2  ,3  ,1    ).float().numpy()

        if output_type == "pil":
             lowercase_			:       List[Any]									=			self.numpy_to_pil(__UpperCamelCase    )

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=__UpperCamelCase    )





"""simple docstring"""







import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__SCREAMING_SNAKE_CASE								={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

__SCREAMING_SNAKE_CASE								=logging.WARNING



def     lowercase__( ):
     lowercase_			:       List[Any]									=			os.getenv('DATASETS_VERBOSITY'  ,    __SCREAMING_SNAKE_CASE   )
     if env_level_str:
          if env_level_str in log_levels:
               return log_levels[env_level_str]
          else:
               logging.getLogger().warning(
                   F'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
                   F'''has to be one of: { ", ".join(log_levels.keys()   ) }'''   )
     return _default_log_level



def     lowercase__( ):
     return __name__.split('.'   )[0]



def     lowercase__( ):
     return logging.getLogger(_get_library_name()   )



def     lowercase__( ):
     # Apply our default configuration to the library root logger.
     lowercase_			:       Optional[int]									=			_get_library_root_logger()
     library_root_logger.setLevel(_get_default_logging_level()   )



def     lowercase__( ):
     lowercase_			:       int									=			_get_library_root_logger()
     library_root_logger.setLevel(logging.NOTSET   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[str] = None   ):
     if name is None:
          lowercase_			:       Union[str, Any]									=			_get_library_name()
     return logging.getLogger(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return _get_library_root_logger().getEffectiveLevel()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     _get_library_root_logger().setLevel(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     lowercase_			:       Tuple									=			False



def     lowercase__( ):
     lowercase_			:       Any									=			True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()







class  UpperCamelCase	:
   def __init__(    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   int:  # pylint: disable=unused-argument


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			args[0] if args else None
   def __iter__(    self    )			->   Union[str, Any]:


        '''simple docstring'''


        return iter(self._iterator    )
   def __getattr__(    self  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        def empty_fn(*__UpperCamelCase  ,**__UpperCamelCase    ):  # pylint: disable=unused-argument
             return

        return empty_fn
   def __enter__(    self    )			->   Optional[int]:


        '''simple docstring'''


        return self







   def __exit__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        return






__SCREAMING_SNAKE_CASE								=True







class  UpperCamelCase	:
   def __call__(    self  ,*__UpperCamelCase  ,__UpperCamelCase=False  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        if _tqdm_active and not disable:
             return tqdm_lib.tqdm(*__UpperCamelCase  ,**__UpperCamelCase    )
        else:
             return EmptyTqdm(*__UpperCamelCase  ,**__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Tuple									=			None
        if _tqdm_active:
             return tqdm_lib.tqdm.set_lock(*__UpperCamelCase  ,**__UpperCamelCase    )







   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        if _tqdm_active:
             return tqdm_lib.tqdm.get_lock()






__SCREAMING_SNAKE_CASE								=_tqdm_cls()



def     lowercase__( ):
     global _tqdm_active
     return bool(_tqdm_active   )



def     lowercase__( ):
     global _tqdm_active
     lowercase_			:       Union[str, Any]									=			True







def     lowercase__( ):
     global _tqdm_active
     lowercase_			:       Optional[int]									=			False

