






"""simple docstring"""





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		List[Any]			)				->    Optional[Any]:
  lowerCamelCase_   					=						UNetaDModel(**_lowerCamelCase			)
  lowerCamelCase_   					=						checkpoint['all_modules.0.W'].data
  lowerCamelCase_   					=						checkpoint['all_modules.0.W'].data
  lowerCamelCase_   					=						checkpoint['all_modules.1.weight'].data
  lowerCamelCase_   					=						checkpoint['all_modules.1.bias'].data

  lowerCamelCase_   					=						checkpoint['all_modules.2.weight'].data
  lowerCamelCase_   					=						checkpoint['all_modules.2.bias'].data

  lowerCamelCase_   					=						checkpoint['all_modules.3.weight'].data
  lowerCamelCase_   					=						checkpoint['all_modules.3.bias'].data

  lowerCamelCase_   					=						checkpoint[list(checkpoint.keys()			)[-4]].data
  lowerCamelCase_   					=						checkpoint[list(checkpoint.keys()			)[-3]].data
  lowerCamelCase_   					=						checkpoint[list(checkpoint.keys()			)[-2]].data
  lowerCamelCase_   					=						checkpoint[list(checkpoint.keys()			)[-1]].data

  lowerCamelCase_   					=						4

  def set_attention_weights(_lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		str			):
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

  def set_resnet_weights(_lowerCamelCase	:		str		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		Union[str, Any]			):
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
    lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

    if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
      lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
      lowerCamelCase_   					=						old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

  for i, block in enumerate(new_model_architecture.downsample_blocks			):
    lowerCamelCase_   					=						hasattr(_lowerCamelCase		,    'attentions'			)
    for j in range(len(block.resnets			)			):
      set_resnet_weights(block.resnets[j]		,    _lowerCamelCase		,    _lowerCamelCase			)
      module_index += 1
      if has_attentions:
        set_attention_weights(block.attentions[j]		,    _lowerCamelCase		,    _lowerCamelCase			)
        module_index += 1

    if hasattr(_lowerCamelCase		,    'downsamplers'			) and block.downsamplers is not None:
      set_resnet_weights(block.resnet_down		,    _lowerCamelCase		,    _lowerCamelCase			)
      module_index += 1
      lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
      lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
      module_index += 1

  set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,    _lowerCamelCase		,    _lowerCamelCase			)
  module_index += 1
  set_attention_weights(new_model_architecture.mid_block.attentions[0]		,    _lowerCamelCase		,    _lowerCamelCase			)
  module_index += 1
  set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,    _lowerCamelCase		,    _lowerCamelCase			)
  module_index += 1

  for i, block in enumerate(new_model_architecture.up_blocks			):
    lowerCamelCase_   					=						hasattr(_lowerCamelCase		,    'attentions'			)
    for j in range(len(block.resnets			)			):
      set_resnet_weights(block.resnets[j]		,    _lowerCamelCase		,    _lowerCamelCase			)
      module_index += 1
    if has_attentions:
      set_attention_weights(
          block.attentions[0]		,    _lowerCamelCase		,    _lowerCamelCase			)  # why can there only be a single attention layer for up?
      module_index += 1

    if hasattr(_lowerCamelCase		,    'resnet_up'			) and block.resnet_up is not None:
      lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.weight'''].data
      lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.bias'''].data
      module_index += 1
      lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.weight'''].data
      lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.bias'''].data
      module_index += 1
      set_resnet_weights(block.resnet_up		,    _lowerCamelCase		,    _lowerCamelCase			)
      module_index += 1

  lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.weight'''].data
  lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.bias'''].data
  module_index += 1
  lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.weight'''].data
  lowerCamelCase_   					=						checkpoint[F'''all_modules.{module_index}.bias'''].data

  return new_model_architecture.state_dict()


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    str    =						argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path',
      default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
      type=str,
      required=False,
      help='Path to the checkpoint to convert.',
  )

  parser.add_argument(
      '--config_file',
      default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
      type=str,
      required=False,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument(
      '--dump_path',
      default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
      type=str,
      required=False,
      help='Path to the output model.',
  )

  _SCREAMING_SNAKE_CASE :    List[str]    =						parser.parse_args()

  _SCREAMING_SNAKE_CASE :    str    =						torch.load(args.checkpoint_path, map_location='cpu')

  with open(args.config_file) as f:
    _SCREAMING_SNAKE_CASE :    Union[str, Any]    =						json.loads(f.read())

  _SCREAMING_SNAKE_CASE :    List[str]    =						convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

  if "sde" in config:
    del config["sde"]

  _SCREAMING_SNAKE_CASE :    Optional[int]    =						UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    _SCREAMING_SNAKE_CASE :    Dict    =						ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

    _SCREAMING_SNAKE_CASE :    str    =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)












"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE :    List[Any]    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :    str    =						{
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					str						=			"""regnet"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			["""x""", """y"""]






    def __init__(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							Any=3	,						__SCREAMING_SNAKE_CASE					:							Any=32	,						__SCREAMING_SNAKE_CASE					:							List[str]=[128, 192, 512, 1088]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=[2, 6, 12, 2]	,						__SCREAMING_SNAKE_CASE					:							str=64	,						__SCREAMING_SNAKE_CASE					:							List[Any]="y"	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="relu"	,						**__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						)					->		Union[str, Any]:
      super().__init__(**__SCREAMING_SNAKE_CASE )
      if layer_type not in self.layer_types:
        raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
      lowerCamelCase_   					=						num_channels
      lowerCamelCase_   					=						embedding_size
      lowerCamelCase_   					=						hidden_sizes
      lowerCamelCase_   					=						depths
      lowerCamelCase_   					=						groups_width
      lowerCamelCase_   					=						layer_type
      lowerCamelCase_   					=						hidden_act
      # always downsample in the first stage
      lowerCamelCase_   					=						True




