"""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,
)


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

lowercase__					       =	'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=8   )	->int:
  a__:     List[Any]										=	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




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			)	->			Any:





        '''simple docstring'''





        super().__init__()

        self.register_modules(
            unet=lowercase    ,			scheduler=lowercase    ,			movq=lowercase    ,			)
        a__:     List[str]										=	2 ** (len(self.movq.config.block_out_channels) - 1)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        if latents is None:
          a__:     Optional[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)
        else:
          if latents.shape != shape:
            raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}')
          a__:     Any										=	latents.to(lowercase)

        a__:     List[str]										=	latents * scheduler.init_noise_sigma
        return latents



      def   lowerCamelCase_   (						self    ,			lowercase=0)	->			Union[str, Any]:





        '''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__:     Any										=	[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
          if cpu_offloaded_model is not None:
            cpu_offload(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase=0)	->			List[Any]:





        '''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=lowercase)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        a__:     str										=	None
        for cpu_offloaded_model in [self.unet, self.movq]:
          a__							,    a__:     int										=	cpu_offload_with_hook(lowercase    ,			lowercase    ,			prev_module_hook=lowercase)

        # 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   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        if not hasattr(self.unet    ,			'_hf_hook'):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(lowercase    ,			'_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(lowercase)
      def __call__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase = 5_12    ,			lowercase = 5_12    ,			lowercase = 1_00    ,			lowercase = 4.0    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	self._execution_device

        a__:     List[Any]										=	guidance_scale > 1.0

        if isinstance(lowercase    ,			lowercase):
          a__:     List[str]										=	torch.cat(lowercase    ,			dim=0)
        if isinstance(lowercase    ,			lowercase):
          a__:     Any										=	torch.cat(lowercase    ,			dim=0)
        if isinstance(lowercase    ,			lowercase):
          a__:     Any										=	torch.cat(lowercase    ,			dim=0)

        a__:     List[str]										=	image_embeds.shape[0] * num_images_per_prompt

        if do_classifier_free_guidance:
          a__:     Union[str, Any]										=	image_embeds.repeat_interleave(lowercase    ,			dim=0)
          a__:     List[str]										=	negative_image_embeds.repeat_interleave(lowercase    ,			dim=0)
          a__:     List[Any]										=	hint.repeat_interleave(lowercase    ,			dim=0)

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

        a__:     Any										=	self.movq.config.latent_channels

        a__							,    a__:     str										=	downscale_height_and_width(lowercase    ,			lowercase    ,			self.movq_scale_factor)

        # create initial latent
        a__:     Union[str, Any]										=	self.prepare_latents(
            (batch_size, num_channels_latents, height, width)    ,			image_embeds.dtype    ,			lowercase    ,			lowercase    ,			lowercase    ,			self.scheduler    ,			)

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

          a__:     Union[str, Any]										=	{'image_embeds': image_embeds, 'hint': hint}
          a__:     Optional[int]										=	self.unet(
              sample=lowercase    ,			timestep=lowercase    ,			encoder_hidden_states=lowercase    ,			added_cond_kwargs=lowercase    ,			return_dict=lowercase    ,			)[0]

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

          # compute the previous noisy sample x_t -> x_t-1
          a__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			generator=lowercase    ,			)[0]
        # post-processing
        a__:     Union[str, Any]										=	self.movq.decode(lowercase    ,			force_not_quantize=lowercase)['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__:     List[Any]										=	image * 0.5 + 0.5
          a__:     List[Any]										=	image.clamp(0    ,			1)
          a__:     Optional[Any]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).float().numpy()

        if output_type == "pil":
          a__:     str										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=lowercase)



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowercase__					       =	{'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
 from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
 import sys

 lowercase__					       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


