




'''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,
)


lowercase			:      List[Any]			=					logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase			:      List[Any]			=					"""
    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 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=8							):
     snake_case :			str					   = height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     snake_case :			List[str]					   = width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase=512    ,      __lowercase=512							):
     snake_case :			Dict					   = pil_image.resize((w, h)    ,      resample=Image.BICUBIC    ,      reducing_gap=1							)
     snake_case :			Union[str, Any]					   = np.array(pil_image.convert("""RGB"""							)							)
     snake_case :			Tuple					   = arr.astype(np.floataa							) / 127.5 - 1
     snake_case :			Any					   = np.transpose(__lowercase    ,      [2, 0, 1]							)
     snake_case :			Any					   = torch.from_numpy(__lowercase							).unsqueeze(0							)
     return image







class 				_a   (a__  ):





     '''simple docstring'''



     def __init__(							self       ,__a       ,__a       ,__a       ,)			->							Optional[Any]:
          super().__init__()

          self.register_modules(
              unet=__a       ,scheduler=__a       ,movq=__a       ,)
          snake_case :			Any					   = 2 ** (len(self.movq.config.block_out_channels       ) - 1)







     def  snake_case_			(							self       ,__a       ,__a       ,__a       )			->							List[Any]:
          # get the original timestep using init_timestep
          snake_case :			Any					   = min(int(num_inference_steps * strength       )       ,__a       )

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

          return timesteps, num_inference_steps - t_start







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a=None       )			->							Any:
          if not isinstance(__a       ,(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(__a       )}'''       )

          snake_case :			Tuple					   = image.to(device=__a       ,dtype=__a       )

          snake_case :			Optional[int]					   = batch_size * num_images_per_prompt

          if image.shape[1] == 4:
               snake_case :			Any					   = image

          else:
               if isinstance(__a       ,__a       ) and len(__a       ) != batch_size:
                    raise ValueError(
                        F'''You have passed a list of generators of length {len(__a       )}, but requested an effective batch'''
                        F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''       )

               elif isinstance(__a       ,__a       ):
                    snake_case :			str					   = [
                        self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(__a       )
                    ]
                    snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )
               else:
                    snake_case :			int					   = self.movq.encode(__a       ).latent_dist.sample(__a       )

               snake_case :			Tuple					   = self.movq.config.scaling_factor * init_latents

          snake_case :			Dict					   = torch.cat([init_latents]       ,dim=0       )

          snake_case :			List[Any]					   = init_latents.shape
          snake_case :			Tuple					   = randn_tensor(__a       ,generator=__a       ,device=__a       ,dtype=__a       )

          # get latents
          snake_case :			Any					   = self.scheduler.add_noise(__a       ,__a       ,__a       )

          snake_case :			Dict					   = init_latents

          return latents







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

          snake_case :			str					   = torch.device(F'''cuda:{gpu_id}'''       )

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







     def  snake_case_			(							self       ,__a=0       )			->							int:
          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 :			Dict					   = torch.device(F'''cuda:{gpu_id}'''       )

          if self.device.type != "cpu":
               self.to("""cpu"""       ,silence_dtype_warnings=__a       )
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          snake_case :			List[Any]					   = None
          for cpu_offloaded_model in [self.unet, self.movq]:
               snake_case   ,		snake_case :			List[Any]					   = cpu_offload_with_hook(__a       ,__a       ,prev_module_hook=__a       )

          # We'll offload the last model manually.
          snake_case :			int					   = hook







     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def  snake_case_			(							self       )			->							Union[str, Any]:
          if not hasattr(self.unet       ,"""_hf_hook"""       ):
               return self.device
          for module in self.unet.modules():
               if (
                   hasattr(__a       ,"""_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(__a       )
     def __call__(							self       ,__a       ,__a       ,__a       ,__a       ,__a = 512       ,__a = 512       ,__a = 100       ,__a = 4.0       ,__a = 0.3       ,__a = 1       ,__a = None       ,__a = "pil"       ,__a = True       ,)			->							List[str]:
          snake_case :			Tuple					   = self._execution_device

          snake_case :			str					   = guidance_scale > 1.0

          if isinstance(__a       ,__a       ):
               snake_case :			List[str]					   = torch.cat(__a       ,dim=0       )
          if isinstance(__a       ,__a       ):
               snake_case :			str					   = torch.cat(__a       ,dim=0       )
          if isinstance(__a       ,__a       ):
               snake_case :			int					   = torch.cat(__a       ,dim=0       )

          snake_case :			List[Any]					   = image_embeds.shape[0]

          if do_classifier_free_guidance:
               snake_case :			Dict					   = image_embeds.repeat_interleave(__a       ,dim=0       )
               snake_case :			Optional[Any]					   = negative_image_embeds.repeat_interleave(__a       ,dim=0       )
               snake_case :			Dict					   = hint.repeat_interleave(__a       ,dim=0       )

          snake_case :			str					   = torch.cat([negative_image_embeds, image_embeds]       ,dim=0       ).to(dtype=self.unet.dtype       ,device=__a       )
          snake_case :			List[Any]					   = torch.cat([hint, hint]       ,dim=0       ).to(dtype=self.unet.dtype       ,device=__a       )

          if not isinstance(__a       ,__a       ):
               snake_case :			Dict					   = [image]
          if not all(isinstance(__a       ,(PIL.Image.Image, torch.Tensor)       ) for i in image       ):
               raise ValueError(
                   F'''Input is in incorrect format: {[type(__a       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''       )

          snake_case :			Dict					   = torch.cat([prepare_image(__a       ,__a       ,__a       ) for i in image]       ,dim=0       )
          snake_case :			int					   = image.to(dtype=image_embeds.dtype       ,device=__a       )

          snake_case :			Optional[int]					   = self.movq.encode(__a       )["""latents"""]
          snake_case :			Optional[Any]					   = latents.repeat_interleave(__a       ,dim=0       )
          self.scheduler.set_timesteps(__a       ,device=__a       )
          snake_case   ,		snake_case :			Dict					   = self.get_timesteps(__a       ,__a       ,__a       )
          snake_case :			Tuple					   = timesteps[:1].repeat(batch_size * num_images_per_prompt       )
          snake_case   ,		snake_case :			List[Any]					   = downscale_height_and_width(__a       ,__a       ,self.movq_scale_factor       )
          snake_case :			int					   = self.prepare_latents(
              __a       ,__a       ,__a       ,__a       ,image_embeds.dtype       ,__a       ,__a       )
          for i, t in enumerate(self.progress_bar(__a       )       ):
               # expand the latents if we are doing classifier free guidance
               snake_case :			Optional[Any]					   = torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

               snake_case :			Optional[Any]					   = {"""image_embeds""": image_embeds, """hint""": hint}
               snake_case :			Union[str, Any]					   = self.unet(
                   sample=__a       ,timestep=__a       ,encoder_hidden_states=__a       ,added_cond_kwargs=__a       ,return_dict=__a       ,)[0]

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

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

               snake_case :			Optional[Any]					   = self.scheduler.step(
                   __a       ,__a       ,__a       ,generator=__a       ,)[0]

          # post-processing
          snake_case :			List[Any]					   = self.movq.decode(__a       ,force_not_quantize=__a       )["""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 :			Union[str, Any]					   = image * 0.5 + 0.5
               snake_case :			Tuple					   = image.clamp(0       ,1       )
               snake_case :			Optional[Any]					   = image.cpu().permute(0       ,2       ,3       ,1       ).float().numpy()

          if output_type == "pil":
               snake_case :			Any					   = self.numpy_to_pil(__a       )

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=__a       )









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase			:      Any			=					{"""configuration_visual_bert""": ["""VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VisualBertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      Optional[int]			=					[
          """VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VisualBertForMultipleChoice""",
          """VisualBertForPreTraining""",
          """VisualBertForQuestionAnswering""",
          """VisualBertForRegionToPhraseAlignment""",
          """VisualBertForVisualReasoning""",
          """VisualBertLayer""",
          """VisualBertModel""",
          """VisualBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

      lowercase			:      List[Any]			=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



