
'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			UNetaDModel(**SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.0.W"""].data
   _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.0.W"""].data
   _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.1.weight"""].data
   _SCREAMING_SNAKE_CASE       	=			checkpoint["""all_modules.1.bias"""].data

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

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

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

   _SCREAMING_SNAKE_CASE       	=			4

   def set_attention_weights(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

   def set_resnet_weights(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
      _SCREAMING_SNAKE_CASE       	=			old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

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

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

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

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

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

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

   _SCREAMING_SNAKE_CASE       	=			checkpoint[F"all_modules.{module_index}.weight"].data
   _SCREAMING_SNAKE_CASE       	=			checkpoint[F"all_modules.{module_index}.bias"].data
   module_index += 1
   _SCREAMING_SNAKE_CASE       	=			checkpoint[F"all_modules.{module_index}.weight"].data
   _SCREAMING_SNAKE_CASE       	=			checkpoint[F"all_modules.{module_index}.bias"].data

   return new_model_architecture.state_dict()


if __name__ == "__main__":
     UpperCamelCase__ :						List[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.",
     )

     UpperCamelCase__ :						Any    							=					parser.parse_args()

     UpperCamelCase__ :						Tuple    							=					torch.load(args.checkpoint_path, map_location="cpu")

     with open(args.config_file) as f:
          UpperCamelCase__ :						List[Any]    							=					json.loads(f.read())

     UpperCamelCase__ :						Union[str, Any]    							=					convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

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

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

     try:
          UpperCamelCase__ :						Any    							=					ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

          UpperCamelCase__ :						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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__ :						List[str]    							=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						Optional[Any]    							=					[
         "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTJForCausalLM",
         "GPTJForQuestionAnswering",
         "GPTJForSequenceClassification",
         "GPTJModel",
         "GPTJPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[str]    							=					[
         "TFGPTJForCausalLM",
         "TFGPTJForQuestionAnswering",
         "TFGPTJForSequenceClassification",
         "TFGPTJModel",
         "TFGPTJPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[Any]    							=					[
         "FlaxGPTJForCausalLM",
         "FlaxGPTJModel",
         "FlaxGPTJPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase__ :						Any    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






