

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,
)


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

lowerCAmelCase						:   int										=    '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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





def 			_lowercase    (      __UpperCamelCase     :		Tuple   ,       __UpperCamelCase     :		List[str]   ,       __UpperCamelCase     :		Optional[Any]=8 ):
      snake_case__					=   height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      snake_case__					=   width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor





def 			_lowercase    (      __UpperCamelCase     :		Optional[int]   ,       __UpperCamelCase     :		Optional[int]=512   ,       __UpperCamelCase     :		str=512 ):
      snake_case__					=   pil_image.resize((w, h)   ,       resample=Image.BICUBIC   ,       reducing_gap=1 )
      snake_case__					=   np.array(pil_image.convert("""RGB""" ) )
      snake_case__					=   arr.astype(np.floataa ) / 1_2_7.5 - 1
      snake_case__					=   np.transpose(lowercase_   ,       [2, 0, 1] )
      snake_case__					=   torch.from_numpy(lowercase_ ).unsqueeze(0 )
      return image
class     SCREAMING_SNAKE_CASE__     (						a_		):





       '''simple docstring'''




       def __init__(						self       :						Tuple    ,		lowerCAmelCase__       :						UNetaDConditionModel    ,		lowerCAmelCase__       :						DDPMScheduler    ,		lowerCAmelCase__       :						VQModel    ,		)			->    str:
             super().__init__()

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

       def UpperCAmelCase_						(						self       :						List[str]    ,		lowerCAmelCase__       :						Optional[int]    ,		lowerCAmelCase__       :						Any    ,		lowerCAmelCase__       :						Union[str, Any]							)			->    int:
             # get the original timestep using init_timestep
             snake_case__					=   min(int(num_inference_steps * strength							)    ,		lowerCAmelCase__							)

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

             return timesteps, num_inference_steps - t_start

       def UpperCAmelCase_						(						self       :						Dict    ,		lowerCAmelCase__       :						Union[str, Any]    ,		lowerCAmelCase__       :						Any    ,		lowerCAmelCase__       :						Union[str, Any]    ,		lowerCAmelCase__       :						Union[str, Any]    ,		lowerCAmelCase__       :						Dict    ,		lowerCAmelCase__       :						Optional[Any]    ,		lowerCAmelCase__       :						str=None							)			->    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__							)}'''							)

             snake_case__					=   image.to(device=lowerCAmelCase__    ,		dtype=lowerCAmelCase__							)

             snake_case__					=   batch_size * num_images_per_prompt

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

                   snake_case__					=   self.movq.config.scaling_factor * init_latents

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

             snake_case__					=   init_latents.shape
             snake_case__					=   randn_tensor(lowerCAmelCase__    ,		generator=lowerCAmelCase__    ,		device=lowerCAmelCase__    ,		dtype=lowerCAmelCase__							)

             # get latents
             snake_case__					=   self.scheduler.add_noise(lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__							)

             snake_case__					=   init_latents

             return latents

       def UpperCAmelCase_						(						self       :						Any    ,		lowerCAmelCase__       :						int=0							)			->    Any:
             if is_accelerate_available():
                   from accelerate import cpu_offload
             else:
                   raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

             snake_case__					=   torch.device(f'''cuda:{gpu_id}'''							)

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

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						Any=0							)			->    Union[str, 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."""							)

             snake_case__					=   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)

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

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

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def UpperCAmelCase_						(						self       :						List[str]							)			->    Any:
             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       :						Optional[Any]    ,		lowerCAmelCase__       :						Union[torch.FloatTensor, List[torch.FloatTensor]]    ,		lowerCAmelCase__       :						Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]    ,		lowerCAmelCase__       :						Union[torch.FloatTensor, List[torch.FloatTensor]]    ,		lowerCAmelCase__       :						torch.FloatTensor    ,		lowerCAmelCase__       :						int = 512    ,		lowerCAmelCase__       :						int = 512    ,		lowerCAmelCase__       :						int = 100    ,		lowerCAmelCase__       :						float = 4.0    ,		lowerCAmelCase__       :						float = 0.3    ,		lowerCAmelCase__       :						int = 1    ,		lowerCAmelCase__       :						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,		lowerCAmelCase__       :						Optional[str] = "pil"    ,		lowerCAmelCase__       :						bool = True    ,		)			->    Optional[int]:
             snake_case__					=   self._execution_device

             snake_case__					=   guidance_scale > 1.0

             if isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   snake_case__					=   torch.cat(lowerCAmelCase__    ,		dim=0							)
             if isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   snake_case__					=   torch.cat(lowerCAmelCase__    ,		dim=0							)
             if isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   snake_case__					=   torch.cat(lowerCAmelCase__    ,		dim=0							)

             snake_case__					=   image_embeds.shape[0]

             if do_classifier_free_guidance:
                   snake_case__					=   image_embeds.repeat_interleave(lowerCAmelCase__    ,		dim=0							)
                   snake_case__					=   negative_image_embeds.repeat_interleave(lowerCAmelCase__    ,		dim=0							)
                   snake_case__					=   hint.repeat_interleave(lowerCAmelCase__    ,		dim=0							)

             snake_case__					=   torch.cat([negative_image_embeds, image_embeds]    ,		dim=0							).to(dtype=self.unet.dtype    ,		device=lowerCAmelCase__							)
             snake_case__					=   torch.cat([hint, hint]    ,		dim=0							).to(dtype=self.unet.dtype    ,		device=lowerCAmelCase__							)

             if not isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   snake_case__					=   [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'''							)

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

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

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

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

                   # compute the previous noisy sample x_t -> x_t-1

                   snake_case__					=   self.scheduler.step(
                       lowerCAmelCase__    ,		lowerCAmelCase__    ,		lowerCAmelCase__    ,		generator=lowerCAmelCase__    ,		)[0]

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

             if output_type == "pil":
                   snake_case__					=   self.numpy_to_pil(lowerCAmelCase__							)

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=lowerCAmelCase__							)









class     SCREAMING_SNAKE_CASE__     :





       '''simple docstring'''




       def __init__(						self       :						Tuple							)			->    None:
             # numbermap keys are the number and its values are lists of indexes sorted
             # in ascending order
             snake_case__					=   {}
             # indexmap keys are an index and it's values are the number at that index
             snake_case__					=   {}

       def UpperCAmelCase_						(						self       :						Optional[int]    ,		lowerCAmelCase__       :						list | str | range    ,		lowerCAmelCase__       :						int							)			->    list[int]:
             if isinstance(lowerCAmelCase__    ,		(range, str)							):
                   snake_case__					=   list(lowerCAmelCase__							)
             elif not isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   raise TypeError(
                       """binary_search_delete() only accepts either a list, range or str"""							)

             snake_case__					=   0
             snake_case__					=   len(lowerCAmelCase__							) - 1

             while low <= high:
                   snake_case__					=   (low + high) // 2
                   if array[mid] == item:
                         array.pop(lowerCAmelCase__							)
                         return array
                   elif array[mid] < item:
                         snake_case__					=   mid + 1
                   else:
                         snake_case__					=   mid - 1
             raise ValueError(
                 """Either the item is not in the array or the array was unsorted"""							)

       def UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						list | str | range    ,		lowerCAmelCase__       :						int							)			->    list[int]:
             if isinstance(lowerCAmelCase__    ,		(range, str)							):
                   snake_case__					=   list(lowerCAmelCase__							)
             elif not isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							):
                   raise TypeError(
                       """binary_search_insert() only accepts either a list, range or str"""							)

             snake_case__					=   0
             snake_case__					=   len(lowerCAmelCase__							) - 1

             while low <= high:
                   snake_case__					=   (low + high) // 2
                   if array[mid] == index:
                         # If the item already exists in the array,
                         # insert it after the existing item
                         array.insert(mid + 1    ,		lowerCAmelCase__							)
                         return array
                   elif array[mid] < index:
                         snake_case__					=   mid + 1
                   else:
                         snake_case__					=   mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
             array.insert(lowerCAmelCase__    ,		lowerCAmelCase__							)
             return array

       def UpperCAmelCase_						(						self       :						str    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						int							)			->    None:
             # Remove previous index
             if index in self.indexmap:
                   snake_case__					=   self.indexmap[index]
                   if len(self.numbermap[n]							) == 1:
                         del self.numbermap[n]
                   else:
                         snake_case__					=   self.binary_search_delete(self.numbermap[n]    ,		lowerCAmelCase__							)

        # Set new index
             snake_case__					=   number

             # Number not seen before or empty so insert number value
             if number not in self.numbermap:
                   snake_case__					=   [index]

             # Here we need to perform a binary search insertion in order to insert
             # The item in the correct place
             else:
                   snake_case__					=   self.binary_search_insert(
                       self.numbermap[number]    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Any    ,		lowerCAmelCase__       :						int							)			->    int:
             return self.numbermap.get(lowerCAmelCase__    ,		[-1]							)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()






