





'''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,
)


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

A__       :     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      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase=8						):





      """simple docstring"""


      _lowerCAmelCase							  =							height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _lowerCAmelCase							  =							width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase=5_12							,			lowerCAmelCase=5_12						):





      """simple docstring"""


      _lowerCAmelCase							  =							pil_image.resize((w, h)							,			resample=Image.BICUBIC							,			reducing_gap=1						)
      _lowerCAmelCase							  =							np.array(pil_image.convert("""RGB"""						)						)
      _lowerCAmelCase							  =							arr.astype(np.floataa						) / 127.5 - 1
      _lowerCAmelCase							  =							np.transpose(lowerCAmelCase							,			[2, 0, 1]						)
      _lowerCAmelCase							  =							torch.from_numpy(lowerCAmelCase						).unsqueeze(0						)
      return image





class 				UpperCAmelCase  (    snake_case_     ):




      def __init__(    self    :				Any    ,    __snake_case    :				UNetaDConditionModel    ,    __snake_case    :				DDPMScheduler    ,    __snake_case    :				VQModel    ,    )       ->   Union[str, Any]:
            super().__init__()

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




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

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

            return timesteps, num_inference_steps - t_start




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				Optional[int]    ,    __snake_case    :				int    ,    __snake_case    :				List[str]    ,    __snake_case    :				List[str]    ,    __snake_case    :				Optional[int]    ,    __snake_case    :				int    ,    __snake_case    :				int=None						)       ->   List[Any]:
            if not isinstance(__snake_case    ,    (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(__snake_case						)}"						)

            _lowerCAmelCase							  =							image.to(device=__snake_case    ,    dtype=__snake_case						)

            _lowerCAmelCase							  =							batch_size * num_images_per_prompt

            if image.shape[1] == 4:
                  _lowerCAmelCase							  =							image

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

                  elif isinstance(__snake_case    ,    __snake_case						):
                        _lowerCAmelCase							  =							[
                            self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(__snake_case						)
                        ]
                        _lowerCAmelCase							  =							torch.cat(__snake_case    ,    dim=0						)
                  else:
                        _lowerCAmelCase							  =							self.movq.encode(__snake_case						).latent_dist.sample(__snake_case						)

                  _lowerCAmelCase							  =							self.movq.config.scaling_factor * init_latents

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

            _lowerCAmelCase							  =							init_latents.shape
            _lowerCAmelCase							  =							randn_tensor(__snake_case    ,    generator=__snake_case    ,    device=__snake_case    ,    dtype=__snake_case						)

            # get latents
            _lowerCAmelCase							  =							self.scheduler.add_noise(__snake_case    ,    __snake_case    ,    __snake_case						)

            _lowerCAmelCase							  =							init_latents

            return latents




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				str=0						)       ->   Dict:
            if is_accelerate_available():
                  from accelerate import cpu_offload
            else:
                  raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

            _lowerCAmelCase							  =							torch.device(f"cuda:{gpu_id}"						)

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




      def        lowercase__					(    self    :				Union[str, Any]    ,    __snake_case    :				Optional[int]=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."""						)

            _lowerCAmelCase							  =							torch.device(f"cuda:{gpu_id}"						)

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

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

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




      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def        lowercase__					(    self    :				Optional[Any]						)       ->   str:
            if not hasattr(self.unet    ,    """_hf_hook"""						):
                  return self.device
            for module in self.unet.modules():
                  if (
                      hasattr(__snake_case    ,    """_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(__snake_case						)
      def __call__(    self    :				Optional[Any]    ,    __snake_case    :				Union[torch.FloatTensor, List[torch.FloatTensor]]    ,    __snake_case    :				Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]    ,    __snake_case    :				Union[torch.FloatTensor, List[torch.FloatTensor]]    ,    __snake_case    :				torch.FloatTensor    ,    __snake_case    :				int = 5_12    ,    __snake_case    :				int = 5_12    ,    __snake_case    :				int = 1_00    ,    __snake_case    :				float = 4.0    ,    __snake_case    :				float = 0.3    ,    __snake_case    :				int = 1    ,    __snake_case    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,    __snake_case    :				Optional[str] = "pil"    ,    __snake_case    :				bool = True    ,    )       ->   List[str]:
            _lowerCAmelCase							  =							self._execution_device

            _lowerCAmelCase							  =							guidance_scale > 1.0

            if isinstance(__snake_case    ,    __snake_case						):
                  _lowerCAmelCase							  =							torch.cat(__snake_case    ,    dim=0						)
            if isinstance(__snake_case    ,    __snake_case						):
                  _lowerCAmelCase							  =							torch.cat(__snake_case    ,    dim=0						)
            if isinstance(__snake_case    ,    __snake_case						):
                  _lowerCAmelCase							  =							torch.cat(__snake_case    ,    dim=0						)

            _lowerCAmelCase							  =							image_embeds.shape[0]

            if do_classifier_free_guidance:
                  _lowerCAmelCase							  =							image_embeds.repeat_interleave(__snake_case    ,    dim=0						)
                  _lowerCAmelCase							  =							negative_image_embeds.repeat_interleave(__snake_case    ,    dim=0						)
                  _lowerCAmelCase							  =							hint.repeat_interleave(__snake_case    ,    dim=0						)

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

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

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

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

                  _lowerCAmelCase							  =							{"""image_embeds""": image_embeds, """hint""": hint}
                  _lowerCAmelCase							  =							self.unet(
                      sample=__snake_case    ,    timestep=__snake_case    ,    encoder_hidden_states=__snake_case    ,    added_cond_kwargs=__snake_case    ,    return_dict=__snake_case    ,    )[0]

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

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

                  _lowerCAmelCase							  =							self.scheduler.step(
                      __snake_case    ,    __snake_case    ,    __snake_case    ,    generator=__snake_case    ,    )[0]

            # post-processing
            _lowerCAmelCase							  =							self.movq.decode(__snake_case    ,    force_not_quantize=__snake_case						)["""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"]:
                  _lowerCAmelCase							  =							image * 0.5 + 0.5
                  _lowerCAmelCase							  =							image.clamp(0    ,    1						)
                  _lowerCAmelCase							  =							image.cpu().permute(0    ,    2    ,    3    ,    1						).float().numpy()

            if output_type == "pil":
                  _lowerCAmelCase							  =							self.numpy_to_pil(__snake_case						)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__snake_case						)













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__       :     Tuple          ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Tuple          =['''MPNetTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[Any]          =[
          '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MPNetForMaskedLM''',
          '''MPNetForMultipleChoice''',
          '''MPNetForQuestionAnswering''',
          '''MPNetForSequenceClassification''',
          '''MPNetForTokenClassification''',
          '''MPNetLayer''',
          '''MPNetModel''',
          '''MPNetPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     str          =[
          '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFMPNetEmbeddings''',
          '''TFMPNetForMaskedLM''',
          '''TFMPNetForMultipleChoice''',
          '''TFMPNetForQuestionAnswering''',
          '''TFMPNetForSequenceClassification''',
          '''TFMPNetForTokenClassification''',
          '''TFMPNetMainLayer''',
          '''TFMPNetModel''',
          '''TFMPNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

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






