

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

A__										=      "\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 				__a							(       __A			,							__A			,							__A=8						):
    SCREAMING_SNAKE_CASE__         =    height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    SCREAMING_SNAKE_CASE__         =    width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor





def 				__a							(       __A			,							__A=512			,							__A=512						):
    SCREAMING_SNAKE_CASE__         =    pil_image.resize((w, h)			,							resample=Image.BICUBIC			,							reducing_gap=1						)
    SCREAMING_SNAKE_CASE__         =    np.array(pil_image.convert("RGB"						)						)
    SCREAMING_SNAKE_CASE__         =    arr.astype(np.floataa						) / 127.5 - 1
    SCREAMING_SNAKE_CASE__         =    np.transpose(__A			,							[2, 0, 1]						)
    SCREAMING_SNAKE_CASE__         =    torch.from_numpy(__A						).unsqueeze(0						)
    return image







class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""



  def __init__(						self     :					str  ,	A_     :					List[str]  ,	A_     :					Optional[Any]  ,	A_     :					int  ,	)       ->			int:
      super().__init__()

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



  def   _UpperCamelCase     (						self     :					Optional[Any]  ,	A_     :					List[Any]  ,	A_     :					str  ,	A_     :					Optional[Any]						)       ->			Tuple:
      # get the original timestep using init_timestep
      SCREAMING_SNAKE_CASE__         =    min(int(num_inference_steps * strength						)  ,	A_						)

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

      return timesteps, num_inference_steps - t_start



  def   _UpperCamelCase     (						self     :					int  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]  ,	A_     :					Optional[int]  ,	A_     :					Tuple  ,	A_     :					Union[str, Any]  ,	A_     :					str  ,	A_     :					Tuple=None						)       ->			Optional[int]:
      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_						)}"						)

      SCREAMING_SNAKE_CASE__         =    image.to(device=A_  ,	dtype=A_						)

      SCREAMING_SNAKE_CASE__         =    batch_size * num_images_per_prompt

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

          SCREAMING_SNAKE_CASE__         =    self.movq.config.scaling_factor * init_latents

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

      SCREAMING_SNAKE_CASE__         =    init_latents.shape
      SCREAMING_SNAKE_CASE__         =    randn_tensor(A_  ,	generator=A_  ,	device=A_  ,	dtype=A_						)

      # get latents
      SCREAMING_SNAKE_CASE__         =    self.scheduler.add_noise(A_  ,	A_  ,	A_						)

      SCREAMING_SNAKE_CASE__         =    init_latents

      return latents



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					str=0						)       ->			str:
      if is_accelerate_available():
          from accelerate import cpu_offload
      else:
          raise ImportError("Please install accelerate via `pip install accelerate`"						)

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

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



  def   _UpperCamelCase     (						self     :					Optional[Any]  ,	A_     :					str=0						)       ->			Dict:
      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."						)

      SCREAMING_SNAKE_CASE__         =    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)

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

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



  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def   _UpperCamelCase     (						self     :					int						)       ->			Dict:
      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     :					Any  ,	A_     :					int  ,	A_     :					Optional[Any]  ,	A_     :					int  ,	A_     :					Dict  ,	A_     :					Any = 512  ,	A_     :					Dict = 512  ,	A_     :					Dict = 100  ,	A_     :					List[Any] = 4.0  ,	A_     :					Tuple = 0.3  ,	A_     :					Dict = 1  ,	A_     :					Tuple = None  ,	A_     :					Union[str, Any] = "pil"  ,	A_     :					Union[str, Any] = True  ,	)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self._execution_device

      SCREAMING_SNAKE_CASE__         =    guidance_scale > 1.0

      if isinstance(A_  ,	A_						):
          SCREAMING_SNAKE_CASE__         =    torch.cat(A_  ,	dim=0						)
      if isinstance(A_  ,	A_						):
          SCREAMING_SNAKE_CASE__         =    torch.cat(A_  ,	dim=0						)
      if isinstance(A_  ,	A_						):
          SCREAMING_SNAKE_CASE__         =    torch.cat(A_  ,	dim=0						)

      SCREAMING_SNAKE_CASE__         =    image_embeds.shape[0]

      if do_classifier_free_guidance:
          SCREAMING_SNAKE_CASE__         =    image_embeds.repeat_interleave(A_  ,	dim=0						)
          SCREAMING_SNAKE_CASE__         =    negative_image_embeds.repeat_interleave(A_  ,	dim=0						)
          SCREAMING_SNAKE_CASE__         =    hint.repeat_interleave(A_  ,	dim=0						)

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

      if not isinstance(A_  ,	A_						):
          SCREAMING_SNAKE_CASE__         =    [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"						)

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

      SCREAMING_SNAKE_CASE__         =    self.movq.encode(A_						)['''latents''']
      SCREAMING_SNAKE_CASE__         =    latents.repeat_interleave(A_  ,	dim=0						)
      self.scheduler.set_timesteps(A_  ,	device=A_						)
      SCREAMING_SNAKE_CASE__         =    self.get_timesteps(A_  ,	A_  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    timesteps[:1].repeat(batch_size * num_images_per_prompt						)
      SCREAMING_SNAKE_CASE__         =    downscale_height_and_width(A_  ,	A_  ,	self.movq_scale_factor						)
      SCREAMING_SNAKE_CASE__         =    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
          SCREAMING_SNAKE_CASE__         =    torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

          SCREAMING_SNAKE_CASE__         =    {'''image_embeds''': image_embeds, '''hint''': hint}
          SCREAMING_SNAKE_CASE__         =    self.unet(
              sample=A_  ,	timestep=A_  ,	encoder_hidden_states=A_  ,	added_cond_kwargs=A_  ,	return_dict=A_  ,	)[0]

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

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

          SCREAMING_SNAKE_CASE__         =    self.scheduler.step(
              A_  ,	A_  ,	A_  ,	generator=A_  ,	)[0]

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

      if output_type == "pil":
          SCREAMING_SNAKE_CASE__         =    self.numpy_to_pil(A_						)

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=A_						)










import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

							# Load weights from tf checkpoint
							load_tf_weights_in_electra(
							    UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	discriminator_or_generator=UpperCAmelCase     )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'     )
							torch.save(model.state_dict()    ,	UpperCAmelCase     )


if __name__ == "__main__":
						__lowerCamelCase						= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--discriminator_or_generator',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
						        '\'generator\'.'
						    ),
						)
						__lowerCamelCase						= parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
						)