


'''simple docstring'''







import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def _UpperCamelCase	(					__A				,      __A   )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__  =    UNetaDModel(**__A   )
      UpperCamelCase__  =    checkpoint["all_modules.0.W"].data
      UpperCamelCase__  =    checkpoint["all_modules.0.W"].data
      UpperCamelCase__  =    checkpoint["all_modules.1.weight"].data
      UpperCamelCase__  =    checkpoint["all_modules.1.bias"].data

      UpperCamelCase__  =    checkpoint["all_modules.2.weight"].data
      UpperCamelCase__  =    checkpoint["all_modules.2.bias"].data

      UpperCamelCase__  =    checkpoint["all_modules.3.weight"].data
      UpperCamelCase__  =    checkpoint["all_modules.3.bias"].data

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

      UpperCamelCase__  =    4

      def set_attention_weights(__A				,      __A				,      __A   ):
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

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

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

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

      def set_resnet_weights(__A				,      __A				,      __A   ):
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

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

            UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
            UpperCamelCase__  =    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:
                  UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
                  UpperCamelCase__  =    old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

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

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

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

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

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

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

      return new_model_architecture.state_dict()


if __name__ == "__main__":
  a__  : Tuple				=	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.',
  )

  a__  : str				=	parser.parse_args()

  a__  : List[str]				=	torch.load(args.checkpoint_path, map_location='cpu')

  with open(args.config_file) as f:
    a__  : int				=	json.loads(f.read())

  a__  : int				=	convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

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

  a__  : Dict				=	UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    a__  : List[str]				=	ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

    a__  : List[Any]				=	ScoreSdeVePipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__  : Any				=	{
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Tuple				=	['CodeGenTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Dict				=	[
      'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'CodeGenForCausalLM',
      'CodeGenModel',
      'CodeGenPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  from .tokenization_codegen import CodeGenTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_codegen_fast import CodeGenTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_codegen import (
        CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
        CodeGenForCausalLM,
        CodeGenModel,
        CodeGenPreTrainedModel,
    )

else:
  import sys

  a__  : Optional[int]				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





