





"""simple docstring"""



from typing import List, Optional, Union

import torch

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
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

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

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""







def 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  Optional[Any]    ,snake_case_  :  Optional[int]=8						)       ->Union[str, Any]:



    '''simple docstring'''







    __A   :						Optional[int]									=					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    __A   :						Tuple									=					width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






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   :						List[Any]									=					2 ** (len(self.movq.config.block_out_channels  ) - 1)

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



     '''simple docstring'''




     if latents is None:
         __A   :						Tuple									=					randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=__lowerCamelCase ,						dtype=__lowerCamelCase  )
     else:
         if latents.shape != shape:
             raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
         __A   :						int									=					latents.to(__lowerCamelCase  )

     __A   :						Dict									=					latents * scheduler.init_noise_sigma
     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   :						List[str]									=					torch.device(F"""cuda:{gpu_id}"""  )

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

     # We'll offload the last model manually.
     __A   :						Optional[int]									=					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 = 1 ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						):



     '''simple docstring'''




     __A   :						str									=					self._execution_device

     __A   :						int									=					guidance_scale > 1.0

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

     __A   :						Union[str, Any]									=					image_embeds.shape[0] * num_images_per_prompt

     if do_classifier_free_guidance:
         __A   :						Optional[Any]									=					image_embeds.repeat_interleave(__lowerCamelCase ,						dim=0  )
         __A   :						Tuple									=					negative_image_embeds.repeat_interleave(__lowerCamelCase ,						dim=0  )
         __A   :						Tuple									=					hint.repeat_interleave(__lowerCamelCase ,						dim=0  )

     __A   :						Optional[int]									=					torch.cat([negative_image_embeds, image_embeds] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=__lowerCamelCase  )
     __A   :						int									=					torch.cat([hint, hint] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=__lowerCamelCase  )
     self.scheduler.set_timesteps(__lowerCamelCase ,						device=__lowerCamelCase  )
     __A   :						Any									=					self.scheduler.timesteps

     __A   :						List[str]									=					self.movq.config.latent_channels

     __A   :						Optional[int]									=					downscale_height_and_width(__lowerCamelCase ,						__lowerCamelCase ,						self.movq_scale_factor  )

     # create initial latent
     __A   :						int									=					self.prepare_latents(
         (batch_size, num_channels_latents, height, width) ,						image_embeds.dtype ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						self.scheduler ,						)

     for i, t in enumerate(self.progress_bar(__lowerCamelCase  )  ):
         # expand the latents if we are doing classifier free guidance
         __A   :						int									=					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

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

         if do_classifier_free_guidance:
             __A   :						int									=					noise_pred.split(latents.shape[1] ,						dim=1  )
             __A   :						str									=					noise_pred.chunk(2  )
             __A   :						int									=					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   :						str									=					noise_pred.split(latents.shape[1] ,						dim=1  )

         # compute the previous noisy sample x_t -> x_t-1
         __A   :						Dict									=					self.scheduler.step(
             __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						generator=__lowerCamelCase ,						)[0]
     # post-processing
     __A   :						Any									=					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   :						str									=					image.clamp(0 ,						1  )
         __A   :						Tuple									=					image.cpu().permute(0 ,						2 ,						3 ,						1  ).float().numpy()

     if output_type == "pil":
         __A   :						Optional[int]									=					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_			=      {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
       from .tokenization_tapex import TapexTokenizer


else:
       import sys

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




