





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__:  str       					=					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 							lowerCAmelCase_				(    A_  ,A_  ,A_=8):
    UpperCamelCase__:					List[str]												=      height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    UpperCamelCase__:					int												=      width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor



def 							lowerCAmelCase_				(    A_  ,A_=5_12  ,A_=5_12):
    UpperCamelCase__:					List[str]												=      pil_image.resize((w, h)  ,resample=Image.BICUBIC  ,reducing_gap=1)
    UpperCamelCase__:					Tuple												=      np.array(pil_image.convert("RGB"))
    UpperCamelCase__:					Union[str, Any]												=      arr.astype(np.floataa) / 127.5 - 1
    UpperCamelCase__:					Tuple												=      np.transpose(A_  ,[2, 0, 1])
    UpperCamelCase__:					int												=      torch.from_numpy(A_).unsqueeze(0)
    return image




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      def __init__(						self:	Any					,  __lowerCamelCase:	UNetaDConditionModel					,  __lowerCamelCase:	DDPMScheduler					,  __lowerCamelCase:	VQModel					,  ):
          '''simple docstring'''


          super().__init__()

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






      def 	UpperCAmelCase_   (						self:	str					,  __lowerCamelCase:	Any					,  __lowerCamelCase:	Optional[int]					,  __lowerCamelCase:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      min(int(num_inference_steps * strength			)					,  __lowerCamelCase			)

          UpperCamelCase__:					Dict												=      max(num_inference_steps - init_timestep					,  0			)
          UpperCamelCase__:					Optional[int]												=      self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start






      def 	UpperCAmelCase_   (						self:	List[str]					,  __lowerCamelCase:	str					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	List[Any]					,  __lowerCamelCase:	Tuple					,  __lowerCamelCase:	Union[str, Any]					,  __lowerCamelCase:	List[Any]=None			):
          '''simple docstring'''


          if not isinstance(__lowerCamelCase					,  (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(__lowerCamelCase			)}"			)

          UpperCamelCase__:					List[Any]												=      image.to(device=__lowerCamelCase					,  dtype=__lowerCamelCase			)

          UpperCamelCase__:					List[Any]												=      batch_size * num_images_per_prompt

          if image.shape[1] == 4:
              UpperCamelCase__:					Optional[int]												=      image

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

              elif isinstance(__lowerCamelCase					,  __lowerCamelCase			):
                  UpperCamelCase__:					Optional[Any]												=      [
                      self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(__lowerCamelCase			)
                  ]
                  UpperCamelCase__:					List[str]												=      torch.cat(__lowerCamelCase					,  dim=0			)
              else:
                  UpperCamelCase__:					str												=      self.movq.encode(__lowerCamelCase			).latent_dist.sample(__lowerCamelCase			)

              UpperCamelCase__:					Dict												=      self.movq.config.scaling_factor * init_latents

          UpperCamelCase__:					Union[str, Any]												=      torch.cat([init_latents]					,  dim=0			)

          UpperCamelCase__:					List[Any]												=      init_latents.shape
          UpperCamelCase__:					List[str]												=      randn_tensor(__lowerCamelCase					,  generator=__lowerCamelCase					,  device=__lowerCamelCase					,  dtype=__lowerCamelCase			)

          # get latents
          UpperCamelCase__:					Optional[int]												=      self.scheduler.add_noise(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)

          UpperCamelCase__:					Optional[Any]												=      init_latents

          return latents






      def 	UpperCAmelCase_   (						self:	List[str]					,  __lowerCamelCase:	Dict=0			):
          '''simple docstring'''


          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError("Please install accelerate via `pip install accelerate`"			)

          UpperCamelCase__:					str												=      torch.device(F"cuda:{gpu_id}"			)

          UpperCamelCase__:					List[str]												=      [
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                  cpu_offload(__lowerCamelCase					,  __lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Optional[int]					,  __lowerCamelCase:	Optional[int]=0			):
          '''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."			)

          UpperCamelCase__:					Tuple												=      torch.device(F"cuda:{gpu_id}"			)

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

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

          # We'll offload the last model manually.
          UpperCamelCase__:					str												=      hook






      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          if not hasattr(self.unet					,  "_hf_hook"			):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(__lowerCamelCase					,  "_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(__lowerCamelCase			)
      def __call__(						self:	Tuple					,  __lowerCamelCase:	Union[torch.FloatTensor, List[torch.FloatTensor]]					,  __lowerCamelCase:	Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					,  __lowerCamelCase:	Union[torch.FloatTensor, List[torch.FloatTensor]]					,  __lowerCamelCase:	torch.FloatTensor					,  __lowerCamelCase:	int = 512					,  __lowerCamelCase:	int = 512					,  __lowerCamelCase:	int = 100					,  __lowerCamelCase:	float = 4.0					,  __lowerCamelCase:	float = 0.3					,  __lowerCamelCase:	int = 1					,  __lowerCamelCase:	Optional[Union[torch.Generator, List[torch.Generator]]] = None					,  __lowerCamelCase:	Optional[str] = "pil"					,  __lowerCamelCase:	bool = True					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      self._execution_device

          UpperCamelCase__:					int												=      guidance_scale > 1.0

          if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
              UpperCamelCase__:					Optional[int]												=      torch.cat(__lowerCamelCase					,  dim=0			)
          if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
              UpperCamelCase__:					Optional[int]												=      torch.cat(__lowerCamelCase					,  dim=0			)
          if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
              UpperCamelCase__:					Tuple												=      torch.cat(__lowerCamelCase					,  dim=0			)

          UpperCamelCase__:					str												=      image_embeds.shape[0]

          if do_classifier_free_guidance:
              UpperCamelCase__:					Optional[Any]												=      image_embeds.repeat_interleave(__lowerCamelCase					,  dim=0			)
              UpperCamelCase__:					Optional[Any]												=      negative_image_embeds.repeat_interleave(__lowerCamelCase					,  dim=0			)
              UpperCamelCase__:					Optional[Any]												=      hint.repeat_interleave(__lowerCamelCase					,  dim=0			)

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

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

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

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

              UpperCamelCase__:					int												=      {"image_embeds": image_embeds, "hint": hint}
              UpperCamelCase__:					str												=      self.unet(
                  sample=__lowerCamelCase					,  timestep=__lowerCamelCase					,  encoder_hidden_states=__lowerCamelCase					,  added_cond_kwargs=__lowerCamelCase					,  return_dict=__lowerCamelCase					,  )[0]

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

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

              UpperCamelCase__:					Optional[int]												=      self.scheduler.step(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=__lowerCamelCase					,  )[0]

          # post-processing
          UpperCamelCase__:					Tuple												=      self.movq.decode(__lowerCamelCase					,  force_not_quantize=__lowerCamelCase			)["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"]:
              UpperCamelCase__:					str												=      image * 0.5 + 0.5
              UpperCamelCase__:					Dict												=      image.clamp(0					,  1			)
              UpperCamelCase__:					Optional[int]												=      image.cpu().permute(0					,  2					,  3					,  1			).float().numpy()

          if output_type == "pil":
              UpperCamelCase__:					Optional[int]												=      self.numpy_to_pil(__lowerCamelCase			)

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=__lowerCamelCase			)








import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 							lowerCAmelCase_				(    ):
    UpperCamelCase__:					Optional[Any]												=      set()
    # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
    for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
        UpperCamelCase__:					Tuple												=      getattr(transformers.models.auto  ,A_  ,A_)
        if module is None:
            continue
        # all mappings in a single auto modeling file
        UpperCamelCase__:					Union[str, Any]												=      [
            x
            for x in dir(A_)
            if x.endswith("_MAPPING_NAMES")
            and (x.startswith("MODEL_") or x.startswith("TF_MODEL_") or x.startswith("FLAX_MODEL_"))
        ]
        for name in mapping_names:
            UpperCamelCase__:					Union[str, Any]												=      getattr(A_  ,A_)
            if mapping is not None:
                for v in mapping.values():
                    if isinstance(A_  ,(list, tuple)):
                        model_names.update(A_)
                    elif isinstance(A_  ,A_):
                        model_names.add(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    ):
    # All model names defined in auto mappings
    UpperCamelCase__:					Any												=      set(get_all_model_names())

    with open("tests/utils/tiny_model_summary.json") as fp:
        UpperCamelCase__:					Union[str, Any]												=      json.load(A_)
    UpperCamelCase__:					Union[str, Any]												=      set()
    for model_base_name in tiny_model_info:
        tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"])

    # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
    UpperCamelCase__:					Any												=      model_names.difference(A_)
    for model_name in copy.copy(A_):
        if not model_name.startswith("TF") and F"TF{model_name}" in not_on_hub:
            tiny_models_names.remove(A_)
        elif model_name.startswith("TF") and model_name[2:] in not_on_hub:
            tiny_models_names.remove(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      COMPOSITE_MODELS.values()

    # All tiny model base names on Hub
    UpperCamelCase__:					Any												=      get_all_model_names()
    UpperCamelCase__:					Tuple												=      hf_api.list_models(
        filter=ModelFilter(
            author="hf-internal-testing"  ,))
    UpperCamelCase__:					Optional[Any]												=      set()
    for x in models:
        UpperCamelCase__:					List[str]												=      x.modelId
        UpperCamelCase__							,				UpperCamelCase__:					Any												=      model.split("/")
        if not model.startswith("tiny-random-"):
            continue
        UpperCamelCase__:					Tuple												=      model.replace("tiny-random-"  ,"")
        if not model[0].isupper():
            continue
        if model not in model_names and model not in special_models:
            continue
        _models.add(A_)

    UpperCamelCase__:					List[Any]												=      sorted(_models)
    # All tiny model names on Hub
    UpperCamelCase__:					List[str]												=      {}
    for model in models:
        UpperCamelCase__:					str												=      F"hf-internal-testing/tiny-random-{model}"
        UpperCamelCase__:					Union[str, Any]												=      model.split("-")[0]
        try:
            UpperCamelCase__:					Any												=      hf_api.repo_info(A_)
            UpperCamelCase__:					int												=      {
                "tokenizer_classes": set(),
                "processor_classes": set(),
                "model_classes": set(),
                "sha": repo_info.sha,
            }
        except Exception:
            continue
        try:
            time.sleep(1)
            UpperCamelCase__:					Any												=      AutoTokenizer.from_pretrained(A_)
            content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      AutoTokenizer.from_pretrained(A_  ,use_fast=A_)
            content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      AutoImageProcessor.from_pretrained(A_)
            content["processor_classes"].add(img_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					Union[str, Any]												=      AutoFeatureExtractor.from_pretrained(A_)
            if not isinstance(A_  ,A_):
                content["processor_classes"].add(feat_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      getattr(A_  ,A_)
            UpperCamelCase__:					Union[str, Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      getattr(A_  ,F"TF{model}")
            UpperCamelCase__:					Optional[Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass

        UpperCamelCase__:					Optional[int]												=      sorted(content["tokenizer_classes"])
        UpperCamelCase__:					List[str]												=      sorted(content["processor_classes"])
        UpperCamelCase__:					Optional[int]												=      sorted(content["model_classes"])

        UpperCamelCase__:					int												=      content
        with open(os.path.join(A_  ,"hub_tiny_model_summary.json")  ,"w") as fp:
            json.dump(A_  ,A_  ,ensure_ascii=A_  ,indent=4)


if __name__ == "__main__":
   A__:  Optional[Any]       					=					argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A__:  Dict       					=					parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   A__:  List[Any]       					=					'''tiny_models'''
   A__:  Dict       					=					True
   A__:  Dict       					=					None
   A__:  Optional[Any]       					=					get_tiny_model_names_from_repo()
   A__:  int       					=					True
   A__:  int       					=					True
   A__:  int       					=					'''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )

