





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,
)


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

_UpperCamelCase       :				Any           =			"""\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case=8      )    ->		Any:


   '''simple docstring'''







   __A    =  height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   __A    =  width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor
def  __UpperCamelCase    (						snake_case							,			snake_case=5_1_2							,			snake_case=5_1_2      )    ->		Any:


   '''simple docstring'''







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







class 		_lowerCAmelCase(		_lowerCAmelCase):

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							)->   Tuple:
     super().__init__()

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

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   str:
     # get the original timestep using init_timestep
     __A    =  min(int(num_inference_steps * strength      )    ,							UpperCAmelCase      )

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

     return timesteps, num_inference_steps - t_start

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase=None      )->   Dict:
     if not isinstance(UpperCAmelCase    ,							(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(UpperCAmelCase      )}"      )

     __A    =  image.to(device=UpperCAmelCase    ,							dtype=UpperCAmelCase      )

     __A    =  batch_size * num_images_per_prompt

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

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

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

        __A    =  self.movq.config.scaling_factor * init_latents

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

     __A    =  init_latents.shape
     __A    =  randn_tensor(UpperCAmelCase    ,							generator=UpperCAmelCase    ,							device=UpperCAmelCase    ,							dtype=UpperCAmelCase      )

     # get latents
     __A    =  self.scheduler.add_noise(UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )

     __A    =  init_latents

     return latents

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase=0      )->   Union[str, Any]:
     if is_accelerate_available():
        from accelerate import cpu_offload
     else:
        raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

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

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

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase=0      )->   Optional[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.'''      )

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

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

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

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

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     if not hasattr(self.unet    ,							'''_hf_hook'''      ):
        return self.device
     for module in self.unet.modules():
        if (
            hasattr(UpperCAmelCase    ,							'''_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(UpperCAmelCase      )
  def __call__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 5_12    ,							UpperCAmelCase = 1_00    ,							UpperCAmelCase = 4.0    ,							UpperCAmelCase = 0.3    ,							UpperCAmelCase = 1    ,							UpperCAmelCase = None    ,							UpperCAmelCase = "pil"    ,							UpperCAmelCase = True    ,							)->   str:
     __A    =  self._execution_device

     __A    =  guidance_scale > 1.0

     if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        __A    =  torch.cat(UpperCAmelCase    ,							dim=0      )
     if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        __A    =  torch.cat(UpperCAmelCase    ,							dim=0      )
     if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        __A    =  torch.cat(UpperCAmelCase    ,							dim=0      )

     __A    =  image_embeds.shape[0]

     if do_classifier_free_guidance:
        __A    =  image_embeds.repeat_interleave(UpperCAmelCase    ,							dim=0      )
        __A    =  negative_image_embeds.repeat_interleave(UpperCAmelCase    ,							dim=0      )
        __A    =  hint.repeat_interleave(UpperCAmelCase    ,							dim=0      )

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

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

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

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

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

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

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

        __A    =  self.scheduler.step(
            UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							generator=UpperCAmelCase    ,							)[0]

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

     if output_type == "pil":
        __A    =  self.numpy_to_pil(UpperCAmelCase      )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=UpperCAmelCase      )






"""simple docstring"""
from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest
@custom_tokenizers
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Optional[Any]		,	snake_case:				Optional[Any]		,	snake_case:				Tuple		,	snake_case:				int		,	snake_case:				Optional[Any]		)  ->      int:
											return True






					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Optional[int]:
											snake_case_					:Dict		=     CpmTokenizer.from_pretrained("""TsinghuaAI/CPM-Generate"""		)
											snake_case_					:Any		=     """Hugging Face大法好，谁用谁知道。"""
											snake_case_					:Union[str, Any]		=     """Hugging Face大法好,谁用谁知道。<unk>"""
											snake_case_					:Dict		=     """▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。""".split()

											snake_case_					:str		=     tokenizer.tokenize(snake_case		)
											self.assertListEqual(snake_case		,	snake_case		)

											snake_case_					:List[Any]		=     tokens + [tokenizer.unk_token]

											snake_case_					:Optional[Any]		=     [13_789, 13_283, 1_421, 8, 10, 1_164, 13_608, 16_528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case		)		,	snake_case		)

											snake_case_					:int		=     tokenizer.decode(snake_case		)
											self.assertEqual(snake_case		,	snake_case		)
