





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 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  Tuple    ,snake_case_  :  Tuple=8						)       ->List[Any]:



    '''simple docstring'''







    __A   :						Optional[int]									=					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    __A   :						Union[str, 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					(       snake_case_  :  List[str]    ,snake_case_  :  Tuple=512    ,snake_case_  :  Dict=512						)       ->str:



    '''simple docstring'''







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






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__()

     self.register_modules(
         unet=__lowerCamelCase ,						scheduler=__lowerCamelCase ,						movq=__lowerCamelCase ,						)
     __A   :						Optional[Any]									=					2 ** (len(self.movq.config.block_out_channels  ) - 1)

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					min(int(num_inference_steps * strength  ) ,						__lowerCamelCase  )

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

     return timesteps, num_inference_steps - t_start

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None  ):



     '''simple docstring'''




     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  )}"""  )

     __A   :						Optional[Any]									=					image.to(device=__lowerCamelCase ,						dtype=__lowerCamelCase  )

     __A   :						Any									=					batch_size * num_images_per_prompt

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

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

     __A   :						int									=					torch.cat([init_latents] ,						dim=0  )

     __A   :						Optional[Any]									=					init_latents.shape
     __A   :						List[str]									=					randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=__lowerCamelCase ,						dtype=__lowerCamelCase  )

     # get latents
     __A   :						str									=					self.scheduler.add_noise(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						Any									=					init_latents

     return latents

 def  UpperCamelCase__(   self ,						__lowerCamelCase=0  ):



     '''simple docstring'''




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

     __A   :						Optional[int]									=					torch.device(F"""cuda:{gpu_id}"""  )

     __A   :						List[str]									=					[
         self.unet,
         self.movq,
     ]
     for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
             cpu_offload(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase=0  ):



     '''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.'''  )

     __A   :						Optional[Any]									=					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)

     __A   :						Dict									=					None
     for cpu_offloaded_model in [self.unet, self.movq]:
         __A   :						str									=					cpu_offload_with_hook(__lowerCamelCase ,						__lowerCamelCase ,						prev_module_hook=__lowerCamelCase  )

     # We'll offload the last model manually.
     __A   :						List[Any]									=					hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     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 ,						):



     '''simple docstring'''




     __A   :						List[str]									=					self._execution_device

     __A   :						Union[str, Any]									=					guidance_scale > 1.0

     if isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
         __A   :						List[Any]									=					torch.cat(__lowerCamelCase ,						dim=0  )
     __A   :						List[str]									=					image_embeds.shape[0]
     if isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
         __A   :						List[str]									=					torch.cat(__lowerCamelCase ,						dim=0  )

     if do_classifier_free_guidance:
         __A   :						int									=					image_embeds.repeat_interleave(__lowerCamelCase ,						dim=0  )
         __A   :						str									=					negative_image_embeds.repeat_interleave(__lowerCamelCase ,						dim=0  )

     __A   :						List[Any]									=					torch.cat([negative_image_embeds, image_embeds] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=__lowerCamelCase  )

     if not isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
         __A   :						str									=					[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"""  )

     __A   :						List[Any]									=					torch.cat([prepare_image(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ) for i in image] ,						dim=0  )
     __A   :						str									=					image.to(dtype=image_embeds.dtype ,						device=__lowerCamelCase  )

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

         __A   :						Optional[int]									=					{'''image_embeds''': image_embeds}
         __A   :						Optional[int]									=					self.unet(
             sample=__lowerCamelCase ,						timestep=__lowerCamelCase ,						encoder_hidden_states=__lowerCamelCase ,						added_cond_kwargs=__lowerCamelCase ,						return_dict=__lowerCamelCase ,						)[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         __A   :						Tuple									=					self.scheduler.step(
             __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						generator=__lowerCamelCase ,						)[0]

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

     if output_type == "pil":
         __A   :						List[Any]									=					self.numpy_to_pil(__lowerCamelCase  )

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase  )











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




