'''simple docstring'''





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def SCREAMING_SNAKE_CASE__  (				snake_case       :				str				,	snake_case       :				Optional[Any]  )   ->				str:







     """simple docstring"""
     a					:							int							= UNetaDModel(**snake_case  )
     a					:							Dict							= checkpoint['all_modules.0.W'].data
     a					:							Optional[int]							= checkpoint['all_modules.0.W'].data
     a					:							List[str]							= checkpoint['all_modules.1.weight'].data
     a					:							List[Any]							= checkpoint['all_modules.1.bias'].data

     a					:							Tuple							= checkpoint['all_modules.2.weight'].data
     a					:							Tuple							= checkpoint['all_modules.2.bias'].data

     a					:							Dict							= checkpoint['all_modules.3.weight'].data
     a					:							Tuple							= checkpoint['all_modules.3.bias'].data

     a					:							Union[str, Any]							= checkpoint[list(checkpoint.keys()  )[-4]].data
     a					:							Optional[Any]							= checkpoint[list(checkpoint.keys()  )[-3]].data
     a					:							Optional[int]							= checkpoint[list(checkpoint.keys()  )[-2]].data
     a					:							List[Any]							= checkpoint[list(checkpoint.keys()  )[-1]].data

     a					:							str							= 4

     def set_attention_weights(snake_case       :				int				,	snake_case       :				List[Any]				,	snake_case       :				str  ):
          a					:							List[Any]							= old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
          a					:							Optional[Any]							= old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
          a					:							Any							= old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

          a					:							List[str]							= old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
          a					:							int							= old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
          a					:							Tuple							= old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

          a					:							List[Any]							= old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
          a					:							int							= old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

          a					:							Any							= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
          a					:							Any							= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

     def set_resnet_weights(snake_case       :				str				,	snake_case       :				Tuple				,	snake_case       :				List[Any]  ):
          a					:							Dict							= old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
          a					:							str							= old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
          a					:							List[Any]							= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
          a					:							Optional[int]							= old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

          a					:							List[Any]							= old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
          a					:							List[str]							= old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
          a					:							Dict							= old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
          a					:							Union[str, Any]							= old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

          a					:							Union[str, Any]							= old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
          a					:							Tuple							= 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:
               a					:							Any							= old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
               a					:							List[str]							= old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

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

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

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

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

          if hasattr(snake_case				,	'resnet_up'  ) and block.resnet_up is not None:
               a					:							Union[str, Any]							= checkpoint[F"""all_modules.{module_index}.weight"""].data
               a					:							Optional[int]							= checkpoint[F"""all_modules.{module_index}.bias"""].data
               module_index += 1
               a					:							Optional[int]							= checkpoint[F"""all_modules.{module_index}.weight"""].data
               a					:							List[Any]							= checkpoint[F"""all_modules.{module_index}.bias"""].data
               module_index += 1
               set_resnet_weights(block.resnet_up				,	snake_case				,	snake_case  )
               module_index += 1

     a					:							List[Any]							= checkpoint[F"""all_modules.{module_index}.weight"""].data
     a					:							Dict							= checkpoint[F"""all_modules.{module_index}.bias"""].data
     module_index += 1
     a					:							Dict							= checkpoint[F"""all_modules.{module_index}.weight"""].data
     a					:							Any							= checkpoint[F"""all_modules.{module_index}.bias"""].data

     return new_model_architecture.state_dict()


if __name__ == "__main__":
      UpperCamelCase       :     Optional[int]			=    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       :     Dict			=    torch.load(args.checkpoint_path, map_location="""cpu""")

      with open(args.config_file) as f:
            UpperCamelCase       :     Optional[int]			=    json.loads(f.read())

      UpperCamelCase       :     str			=    convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

      UpperCamelCase       :     Optional[Any]			=    UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

            UpperCamelCase       :     Optional[int]			=    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


UpperCamelCase       :     Any			=    logging.get_logger(__name__)

UpperCamelCase       :     Optional[int]			=    {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    List[str]				=			"cpmant"





    def __init__(					self     :						Tuple							,							UpperCAmelCase_     :						int = 3_0_7_2_0							,							UpperCAmelCase_     :						int = 4_0_9_6							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 1_2_8							,							UpperCAmelCase_     :						int = 1_0_2_4_0							,							UpperCAmelCase_     :						int = 4_8							,							UpperCAmelCase_     :						int = 0.0							,							UpperCAmelCase_     :						int = 5_1_2							,							UpperCAmelCase_     :						int = 2_0_4_8							,							UpperCAmelCase_     :						int = 1e-6							,							UpperCAmelCase_     :						float = 1.0							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						int = 3_2							,							UpperCAmelCase_     :						bool = True							,							**UpperCAmelCase_     :						Tuple							,							):


         """simple docstring"""







         super().__init__(**UpperCAmelCase_)
         a					:							Union[str, Any]							= prompt_types
         a					:							List[str]							= prompt_length
         a					:							List[Any]							= segment_types
         a					:							Any							= hidden_size
         a					:							Optional[int]							= num_attention_heads
         a					:							Union[str, Any]							= dim_head
         a					:							int							= dim_ff
         a					:							Union[str, Any]							= num_hidden_layers
         a					:							List[Any]							= position_bias_num_buckets
         a					:							Optional[int]							= position_bias_max_distance
         a					:							str							= dropout_p
         a					:							List[str]							= eps
         a					:							Dict							= use_cache
         a					:							Tuple							= vocab_size
         a					:							Optional[Any]							= init_std

