
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A      :	Tuple       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """electra"""
   def __init__(      self     ,   _a=30_522     ,   _a=128     ,   _a=256     ,   _a=12     ,   _a=4     ,   _a=1_024     ,   _a="gelu"     ,   _a=0.1     ,   _a=0.1     ,   _a=512     ,   _a=2     ,   _a=0.0_2     ,   _a=1E-12     ,   _a="first"     ,   _a=True     ,   _a="gelu"     ,   _a=0.1     ,   _a=0     ,   _a="absolute"     ,   _a=True     ,   _a=None     ,   **_a     ,   ):
          super().__init__(pad_token_id=_a     ,   **_a    )

          lowercase   :    str			=							vocab_size
          lowercase   :    Union[str, Any]			=							embedding_size
          lowercase   :    Optional[Any]			=							hidden_size
          lowercase   :    Any			=							num_hidden_layers
          lowercase   :    Any			=							num_attention_heads
          lowercase   :    List[Any]			=							intermediate_size
          lowercase   :    Tuple			=							hidden_act
          lowercase   :    Union[str, Any]			=							hidden_dropout_prob
          lowercase   :    Optional[Any]			=							attention_probs_dropout_prob
          lowercase   :    List[Any]			=							max_position_embeddings
          lowercase   :    Tuple			=							type_vocab_size
          lowercase   :    Dict			=							initializer_range
          lowercase   :    Optional[int]			=							layer_norm_eps

          lowercase   :    Any			=							summary_type
          lowercase   :    Any			=							summary_use_proj
          lowercase   :    Tuple			=							summary_activation
          lowercase   :    List[str]			=							summary_last_dropout
          lowercase   :    Tuple			=							position_embedding_type
          lowercase   :    List[str]			=							use_cache
          lowercase   :    Union[str, Any]			=							classifier_dropout


class 		a__						(				a_						):
   @property
   def       __magic_name__      (      self    ):
          if self.task == "multiple-choice":
                 lowercase   :    Optional[int]			=							{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase   :    Dict			=							{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]    )




"""simple docstring"""




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def   __magic_name__     (				__snake_case    :	str					,			__snake_case    :	Any  )			->    Union[str, Any]:
       lowercase   :    List[str]			=							UNetaDModel(**__snake_case  )
       lowercase   :    str			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Union[str, Any]			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Optional[Any]			=							checkpoint["all_modules.1.weight"].data
       lowercase   :    List[str]			=							checkpoint["all_modules.1.bias"].data

       lowercase   :    List[Any]			=							checkpoint["all_modules.2.weight"].data
       lowercase   :    Union[str, Any]			=							checkpoint["all_modules.2.bias"].data

       lowercase   :    str			=							checkpoint["all_modules.3.weight"].data
       lowercase   :    str			=							checkpoint["all_modules.3.bias"].data

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

       lowercase   :    Tuple			=							4

       def set_attention_weights(__snake_case    :	Optional[int]					,			__snake_case    :	List[str]					,			__snake_case    :	List[str]  ):
              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
              lowercase   :    Tuple			=							old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
              lowercase   :    List[Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

              lowercase   :    List[str]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

              lowercase   :    Any			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

       def set_resnet_weights(__snake_case    :	Tuple					,			__snake_case    :	Optional[int]					,			__snake_case    :	List[str]  ):
              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
              lowercase   :    Tuple			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
              lowercase   :    List[str]			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
              lowercase   :    Any			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
              lowercase   :    Any			=							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:
                     lowercase   :    str			=							old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
                     lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

       for i, block in enumerate(new_model_architecture.downsample_blocks  ):
              lowercase   :    List[Any]			=							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
                     lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
                     lowercase   :    Dict			=							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  ):
              lowercase   :    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:
                     lowercase   :    Any			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
                     lowercase   :    Union[str, Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     lowercase   :    Any			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
                     lowercase   :    str			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up					,			__snake_case					,			__snake_case  )
                     module_index += 1

       lowercase   :    str			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
       lowercase   :    Optional[Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
       module_index += 1
       lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
       lowercase   :    List[Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
   _A      :	Any       		=	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      :	Union[str, Any]       		=	parser.parse_args()

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

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

   _A      :	Any       		=	convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

   _A      :	Any       		=	UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

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


