
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:UNetaDModel
	a			:KarrasVeScheduler
	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			UNetaDModel       ,							SCREAMING_SNAKE_CASE_  :			KarrasVeScheduler       )       ->     List[str]:
					super().__init__()
					self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       )




	@torch.no_grad()
	def __call__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int = 1       ,							SCREAMING_SNAKE_CASE_  :			int = 5_0       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = "pil"       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							**SCREAMING_SNAKE_CASE_  :			int       ,							)       ->     Union[Tuple, ImagePipelineOutput]:
					lowercase_       =					self.unet.config.sample_size
					lowercase_       =					(batch_size, 3, img_size, img_size)

					lowercase_       =					self.unet

					# sample x_0 ~ N(0, sigma_0^2 * I)
					lowercase_       =					randn_tensor(SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							device=self.device       ) * self.scheduler.init_noise_sigma

					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       )

					for t in self.progress_bar(self.scheduler.timesteps       ):
									# here sigma_t == t_i from the paper
									lowercase_       =					self.scheduler.schedule[t]
									lowercase_       =					self.scheduler.schedule[t - 1] if t > 0 else 0

									# 1. Select temporarily increased noise level sigma_hat
									# 2. Add new noise to move from sample_i to sample_hat
									lowercase_  ,	lowercase_       =					self.scheduler.add_noise_to_input(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       )

									# 3. Predict the noise residual given the noise magnitude `sigma_hat`
									# The model inputs and output are adjusted by following eq. (213) in [1].
									lowercase_       =					(sigma_hat / 2) * model((sample_hat + 1) / 2       ,							sigma_hat / 2       ).sample

									# 4. Evaluate dx/dt at sigma_hat
									# 5. Take Euler step from sigma to sigma_prev
									lowercase_       =					self.scheduler.step(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

									if sigma_prev != 0:
													# 6. Apply 2nd order correction
													# The model inputs and output are adjusted by following eq. (213) in [1].
													lowercase_       =					(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,							sigma_prev / 2       ).sample
													lowercase_       =					self.scheduler.step_correct(
													    SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							step_output.prev_sample       ,							step_output['''derivative''']       ,							)
									lowercase_       =					step_output.prev_sample

					lowercase_       =					(sample / 2 + 0.5).clamp(0       ,							1       )
					lowercase_       =					sample.cpu().permute(0       ,							2       ,							3       ,							1       ).numpy()
					if output_type == "pil":
									lowercase_       =					self.numpy_to_pil(SCREAMING_SNAKE_CASE_       )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_       )











"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def      __lowerCamelCase   (							a_     :		str ,   a_     :		Dict ,   a_     :		Any ,   a_     :		str							)			->      str:
      __SCREAMING_SNAKE_CASE   :int						=  s.rsplit(a_ ,   a_							)
      return new.join(a_							)



def      __lowerCamelCase   (							a_     :		List[str]							)			->      Dict:
      # encoder.embeddings are double copied in original FLAVA
      return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()							)



def      __lowerCamelCase   (							a_     :		Optional[int]							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  {}

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        __SCREAMING_SNAKE_CASE   :Optional[Any]						=  key.replace(f'''{group_key}.''' ,   f'''{group_key}.group.'''							)

            if "res_path" in key:
                  __SCREAMING_SNAKE_CASE   :str						=  key.replace('''res_path.''' ,   '''res_path.path.'''							)

            if key.endswith('''.w'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.w''' ,   '''.weight''' ,   1							)
            if key.endswith('''.b'''							):
                  __SCREAMING_SNAKE_CASE   :List[Any]						=  rreplace(a_ ,   '''.b''' ,   '''.bias''' ,   1							)

            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  value.float()

      return upgrade



@torch.no_grad()
def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Optional[int] ,   a_     :		Optional[int]=None ,   a_     :		Dict=True							)			->      Union[str, Any]:
      from dall_e import Encoder

      __SCREAMING_SNAKE_CASE   :int						=  Encoder()
      if os.path.exists(a_							):
            __SCREAMING_SNAKE_CASE   :Dict						=  torch.load(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :List[str]						=  torch.hub.load_state_dict_from_url(a_							)

      if isinstance(a_ ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[str]						=  ckpt.state_dict()
      encoder.load_state_dict(a_							)

      if config_path is not None:
            __SCREAMING_SNAKE_CASE   :Any						=  FlavaImageCodebookConfig.from_pretrained(a_							)
      else:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  FlavaImageCodebookConfig()

      __SCREAMING_SNAKE_CASE   :Tuple						=  FlavaImageCodebook(a_							).eval()
      __SCREAMING_SNAKE_CASE   :List[str]						=  encoder.state_dict()

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  upgrade_state_dict(a_							)
      hf_model.load_state_dict(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hf_model.state_dict()
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  count_parameters(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  count_parameters(a_							)

      assert torch.allclose(a_ ,   a_ ,   atol=1e-3							)

      if save_checkpoint:
            hf_model.save_pretrained(a_							)
      else:
            return hf_state_dict


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  lowerCamelCase_								=		parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)