

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						UNetaDModel(**__A	)
    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(__A ,       __A ,       __A	):
        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(__A ,       __A ,       __A	):
        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(__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
            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] ,       __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	):
        SCREAMING_SNAKE_CASE__					     =						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:
            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 ,       __A ,       __A	)
            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__":
  A__        =			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__        =			parser.parse_args()

  A__        =			torch.load(args.checkpoint_path, map_location="cpu")

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

  A__        =			convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

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

  A__        =			UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

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

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





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


A__        =			logging.get_logger(__name__)

A__        =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A__        =			{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

A__        =			{
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: str										=      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				: Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				: Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				: List[str]										=      ["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__				: List[str]										=      LongformerTokenizer



  def __init__(	self    :				List[Any]     ,  A_    :				Any=None     ,  A_    :				Union[str, Any]=None     ,  A_    :				Optional[int]=None     ,  A_    :				int="replace"     ,  A_    :				Any="<s>"     ,  A_    :				int="</s>"     ,  A_    :				str="</s>"     ,  A_    :				Optional[Any]="<s>"     ,  A_    :				Optional[int]="<unk>"     ,  A_    :				Optional[int]="<pad>"     ,  A_    :				List[str]="<mask>"     ,  A_    :				Optional[int]=False     ,  A_    :				List[str]=True     ,  **A_    :				List[str]     ,  )					-> Any:
      super().__init__(
          A_     ,  A_     ,  tokenizer_file=A_     ,  errors=A_     ,  bos_token=A_     ,  eos_token=A_     ,  sep_token=A_     ,  cls_token=A_     ,  unk_token=A_     ,  pad_token=A_     ,  mask_token=A_     ,  add_prefix_space=A_     ,  trim_offsets=A_     ,  **A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get("add_prefix_space"     ,  A_	) != add_prefix_space:
          SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  pre_tok_state.pop("type"	)	)
          SCREAMING_SNAKE_CASE__					     =						add_prefix_space
          SCREAMING_SNAKE_CASE__					     =						pre_tok_class(**A_	)

      SCREAMING_SNAKE_CASE__					     =						add_prefix_space

      SCREAMING_SNAKE_CASE__					     =						"post_processor"
      SCREAMING_SNAKE_CASE__					     =						getattr(self.backend_tokenizer     ,  A_     ,  A_	)
      if tokenizer_component_instance:
          SCREAMING_SNAKE_CASE__					     =						json.loads(tokenizer_component_instance.__getstate__()	)

          # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
          if "sep" in state:
              SCREAMING_SNAKE_CASE__					     =						tuple(state["sep"]	)
          if "cls" in state:
              SCREAMING_SNAKE_CASE__					     =						tuple(state["cls"]	)

          SCREAMING_SNAKE_CASE__					     =						False

          if state.get("add_prefix_space"     ,  A_	) != add_prefix_space:
              SCREAMING_SNAKE_CASE__					     =						add_prefix_space
              SCREAMING_SNAKE_CASE__					     =						True

          if state.get("trim_offsets"     ,  A_	) != trim_offsets:
              SCREAMING_SNAKE_CASE__					     =						trim_offsets
              SCREAMING_SNAKE_CASE__					     =						True

          if changes_to_apply:
              SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  state.pop("type"	)	)
              SCREAMING_SNAKE_CASE__					     =						component_class(**A_	)
              setattr(self.backend_tokenizer     ,  A_     ,  A_	)



  @property
  def      _UpperCamelCase    (	self    :				Dict	)					-> str:
      if self._mask_token is None:
          if self.verbose:
              logger.error("Using mask_token, but it is not set yet."	)
          return None
      return str(self._mask_token	)



  @mask_token.setter
  def      _UpperCamelCase    (	self    :				int     ,  A_    :				Optional[Any]	)					-> int:
      SCREAMING_SNAKE_CASE__					     =						AddedToken(A_     ,  lstrip=A_     ,  rstrip=A_	) if isinstance(A_     ,  A_	) else value
      SCREAMING_SNAKE_CASE__					     =						value



  def      _UpperCamelCase    (	self    :				int     ,  *A_    :				int     ,  **A_    :				Dict	)					-> BatchEncoding:
      SCREAMING_SNAKE_CASE__					     =						kwargs.get("is_split_into_words"     ,  A_	)
      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*A_     ,  **A_	)



  def      _UpperCamelCase    (	self    :				Dict     ,  *A_    :				int     ,  **A_    :				Union[str, Any]	)					-> BatchEncoding:
      SCREAMING_SNAKE_CASE__					     =						kwargs.get("is_split_into_words"     ,  A_	)

      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*A_     ,  **A_	)



  def      _UpperCamelCase    (	self    :				int     ,  A_    :				str     ,  A_    :				Optional[str] = None	)					-> Tuple[str]:
      SCREAMING_SNAKE_CASE__					     =						self._tokenizer.model.save(A_     ,  name=A_	)
      return tuple(A_	)



  def      _UpperCamelCase    (	self    :				Tuple     ,  A_    :				List[str]     ,  A_    :				str=None	)					-> List[str]:
      SCREAMING_SNAKE_CASE__					     =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
          return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







  def      _UpperCamelCase    (	self    :				List[str]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]

      if token_ids_a is None:
          return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]


