






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

_A      :	Union[str, 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   __magic_name__     (				__snake_case    :	List[str]					,			__snake_case    :	List[str]					,			__snake_case    :	Tuple=8  )			->    int:
       lowercase   :    Any			=							height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       lowercase   :    Any			=							width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor


def   __magic_name__     (				__snake_case    :	Optional[Any]					,			__snake_case    :	Any=512					,			__snake_case    :	Dict=512  )			->    Tuple:
       lowercase   :    Optional[int]			=							pil_image.resize((w, h)					,			resample=Image.BICUBIC					,			reducing_gap=1  )
       lowercase   :    Optional[int]			=							np.array(pil_image.convert("RGB"  )  )
       lowercase   :    List[str]			=							arr.astype(np.floataa  ) / 127.5 - 1
       lowercase   :    List[str]			=							np.transpose(__snake_case					,			[2, 0, 1]  )
       lowercase   :    str			=							torch.from_numpy(__snake_case  ).unsqueeze(0  )
       return image

class 		a__						(				a_						):
   def __init__(      self     ,   _a     ,   _a     ,   _a     ,   ):
          super().__init__()

          self.register_modules(
              unet=_a     ,   scheduler=_a     ,   movq=_a     ,   )
          lowercase   :    List[str]			=							2 ** (len(self.movq.config.block_out_channels    ) - 1)
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a    ):
          # get the original timestep using init_timestep
          lowercase   :    Tuple			=							min(int(num_inference_steps * strength    )     ,   _a    )

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

          return timesteps, num_inference_steps - t_start
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a     ,   _a     ,   _a     ,   _a=None    ):
          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    )}"""    )

          lowercase   :    Union[str, Any]			=							image.to(device=_a     ,   dtype=_a    )

          lowercase   :    Tuple			=							batch_size * num_images_per_prompt

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

                 lowercase   :    str			=							self.movq.config.scaling_factor * init_latents

          lowercase   :    List[str]			=							torch.cat([init_latents]     ,   dim=0    )

          lowercase   :    Union[str, Any]			=							init_latents.shape
          lowercase   :    int			=							randn_tensor(_a     ,   generator=_a     ,   device=_a     ,   dtype=_a    )

          # get latents
          lowercase   :    Any			=							self.scheduler.add_noise(_a     ,   _a     ,   _a    )

          lowercase   :    Optional[int]			=							init_latents

          return latents
   def       __magic_name__      (      self     ,   _a=0    ):
          if is_accelerate_available():
                 from accelerate import cpu_offload
          else:
                 raise ImportError("Please install accelerate via `pip install accelerate`"    )

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

          lowercase   :    Dict			=							[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                        cpu_offload(_a     ,   _a    )
   def       __magic_name__      (      self     ,   _a=0    ):
          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."    )

          lowercase   :    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)

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

          # We'll offload the last model manually.
          lowercase   :    str			=							hook
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def       __magic_name__      (      self    ):
          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     ,   ):
          lowercase   :    Dict			=							self._execution_device

          lowercase   :    Tuple			=							guidance_scale > 1.0

          if isinstance(_a     ,   _a    ):
                 lowercase   :    Optional[Any]			=							torch.cat(_a     ,   dim=0    )
          if isinstance(_a     ,   _a    ):
                 lowercase   :    Union[str, Any]			=							torch.cat(_a     ,   dim=0    )
          if isinstance(_a     ,   _a    ):
                 lowercase   :    Union[str, Any]			=							torch.cat(_a     ,   dim=0    )

          lowercase   :    Dict			=							image_embeds.shape[0]

          if do_classifier_free_guidance:
                 lowercase   :    Optional[int]			=							image_embeds.repeat_interleave(_a     ,   dim=0    )
                 lowercase   :    List[Any]			=							negative_image_embeds.repeat_interleave(_a     ,   dim=0    )
                 lowercase   :    Optional[int]			=							hint.repeat_interleave(_a     ,   dim=0    )

          lowercase   :    Any			=							torch.cat([negative_image_embeds, image_embeds]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=_a    )
          lowercase   :    Any			=							torch.cat([hint, hint]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=_a    )

          if not isinstance(_a     ,   _a    ):
                 lowercase   :    str			=							[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"""    )

          lowercase   :    Tuple			=							torch.cat([prepare_image(_a     ,   _a     ,   _a    ) for i in image]     ,   dim=0    )
          lowercase   :    Union[str, Any]			=							image.to(dtype=image_embeds.dtype     ,   device=_a    )

          lowercase   :    Dict			=							self.movq.encode(_a    )["latents"]
          lowercase   :    Optional[Any]			=							latents.repeat_interleave(_a     ,   dim=0    )
          self.scheduler.set_timesteps(_a     ,   device=_a    )
          lowercase   :    str			=							self.get_timesteps(_a     ,   _a     ,   _a    )
          lowercase   :    List[Any]			=							timesteps[:1].repeat(batch_size * num_images_per_prompt    )
          lowercase   :    int			=							downscale_height_and_width(_a     ,   _a     ,   self.movq_scale_factor    )
          lowercase   :    Optional[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
                 lowercase   :    Union[str, Any]			=							torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

                 lowercase   :    Any			=							{"image_embeds": image_embeds, "hint": hint}
                 lowercase   :    str			=							self.unet(
                     sample=_a     ,   timestep=_a     ,   encoder_hidden_states=_a     ,   added_cond_kwargs=_a     ,   return_dict=_a     ,   )[0]

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

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

                 lowercase   :    int			=							self.scheduler.step(
                     _a     ,   _a     ,   _a     ,   generator=_a     ,   )[0]

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

          if output_type == "pil":
                 lowercase   :    Tuple			=							self.numpy_to_pil(_a    )

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=_a    )




"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 		a__						(				unittest.TestCase						):
   @property
   def       __magic_name__      (      self    ):
          torch.manual_seed(0    )
          lowercase   :    int			=							UNetaDModel(
              block_out_channels=(32, 64)     ,   layers_per_block=2     ,   sample_size=32     ,   in_channels=3     ,   out_channels=3     ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,   )
          return model
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							"cpu"
          lowercase   :    Dict			=							self.dummy_uncond_unet
          lowercase   :    List[str]			=							DDPMScheduler()

          lowercase   :    Tuple			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Optional[int]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    Optional[int]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"     ,   return_dict=_a    )[0]

          lowercase   :    List[str]			=							image[0, -3:, -3:, -1]
          lowercase   :    Any			=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    List[str]			=							np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04]    )

          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2

   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							self.dummy_uncond_unet
          lowercase   :    Dict			=							DDPMScheduler(prediction_type="sample"    )

          lowercase   :    List[Any]			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Any			=							torch.manual_seed(0    )
          lowercase   :    int			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.manual_seed(0    )
          lowercase   :    Optional[Any]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    )[0]

          lowercase   :    Any			=							image[0, -3:, -3:, -1]
          lowercase   :    Dict			=							image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Tuple			=							1E-2 if torch_device != "mps" else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance


@slow
@require_torch_gpu
class 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							"google/ddpm-cifar10-32"

          lowercase   :    Dict			=							UNetaDModel.from_pretrained(_a    )
          lowercase   :    str			=							DDPMScheduler.from_pretrained(_a    )

          lowercase   :    int			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Dict			=							torch.manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   output_type="numpy"    ).images

          lowercase   :    int			=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Optional[Any]			=							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]    )
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


