
"""simple docstring"""




from typing import Mapping

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


_A      :	Any       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   __lowerCAmelCase						=    """mt5"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a=250_112     ,   _a=512     ,   _a=64     ,   _a=1_024     ,   _a=8     ,   _a=None     ,   _a=6     ,   _a=32     ,   _a=128     ,   _a=0.1     ,   _a=1E-6     ,   _a=1.0     ,   _a="gated-gelu"     ,   _a=True     ,   _a=True     ,   _a="T5Tokenizer"     ,   _a=False     ,   _a=0     ,   _a=1     ,   _a=0     ,   **_a     ,   ):
          super().__init__(
              is_encoder_decoder=_a     ,   tokenizer_class=_a     ,   tie_word_embeddings=_a     ,   pad_token_id=_a     ,   eos_token_id=_a     ,   decoder_start_token_id=_a     ,   **_a     ,   )
          lowercase   :    Dict			=							vocab_size
          lowercase   :    Optional[int]			=							d_model
          lowercase   :    Optional[int]			=							d_kv
          lowercase   :    Optional[Any]			=							d_ff
          lowercase   :    Dict			=							num_layers
          lowercase   :    Dict			=							(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          lowercase   :    List[str]			=							num_heads
          lowercase   :    Optional[Any]			=							relative_attention_num_buckets
          lowercase   :    Dict			=							relative_attention_max_distance
          lowercase   :    Optional[int]			=							dropout_rate
          lowercase   :    Any			=							layer_norm_epsilon
          lowercase   :    Union[str, Any]			=							initializer_factor
          lowercase   :    Optional[int]			=							feed_forward_proj
          lowercase   :    int			=							use_cache

          lowercase   :    List[Any]			=							self.feed_forward_proj.split("-"    )
          lowercase   :    Any			=							act_info[-1]
          lowercase   :    Tuple			=							act_info[0] == "gated"

          if len(_a    ) > 1 and act_info[0] != "gated" or len(_a    ) > 2:
                 raise ValueError(
                     f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                     "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                     "'gated-gelu' or 'relu'"    )

          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
                 lowercase   :    Optional[int]			=							"gelu_new"
   @property
   def       __magic_name__      (      self    ):
          return self.d_model
   @property
   def       __magic_name__      (      self    ):
          return self.num_heads

   @property
   def       __magic_name__      (      self    ):
          return self.num_layers


class 		a__						(				a_						):
   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							{
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
                 lowercase   :    Any			=							"past_encoder_sequence + sequence"
                 lowercase   :    List[str]			=							{0: "batch"}
                 lowercase   :    Union[str, Any]			=							{0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
                 lowercase   :    int			=							{0: "batch", 1: "decoder_sequence"}
                 lowercase   :    List[Any]			=							{0: "batch", 1: "decoder_sequence"}

          if self.use_past:
                 self.fill_with_past_key_values_(_a     ,   direction="inputs"    )

          return common_inputs
   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def       __magic_name__      (      self    ):
          return 13

   @property
   def       __magic_name__      (      self    ):
          return 5E-4




"""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)


