
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 					_a      (		lowerCamelCase,			lowerCamelCase=1							):
 if n_shave_prefix_segments >= 0:
  return ".".join(path.split("""."""							)[n_shave_prefix_segments:]							)
 else:
  return ".".join(path.split("""."""							)[:n_shave_prefix_segments]							)





def 					_a      (		lowerCamelCase,			lowerCamelCase=0							):
 lowerCamelCase	:			List[Any]           =   []
 for old_item in old_list:
  lowerCamelCase	:			Any           =   old_item
  lowerCamelCase	:			Dict           =   new_item.replace("""block.""",			"""resnets."""							)
  lowerCamelCase	:			Tuple           =   new_item.replace("""conv_shorcut""",			"""conv1"""							)
  lowerCamelCase	:			Optional[int]           =   new_item.replace("""in_shortcut""",			"""conv_shortcut"""							)
  lowerCamelCase	:			Any           =   new_item.replace("""temb_proj""",			"""time_emb_proj"""							)

  lowerCamelCase	:			Tuple           =   shave_segments(lowerCamelCase,			n_shave_prefix_segments=lowerCamelCase							)

  mapping.append({"""old""": old_item, """new""": new_item}							)

 return mapping





def 					_a      (		lowerCamelCase,			lowerCamelCase=0,			lowerCamelCase=False							):
 lowerCamelCase	:			Any           =   []
 for old_item in old_list:
  lowerCamelCase	:			List[Any]           =   old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   lowerCamelCase	:			str           =   new_item.replace("""attn""",			"""attentions"""							)
  lowerCamelCase	:			str           =   new_item.replace(""".k.""",			""".key."""							)
  lowerCamelCase	:			Tuple           =   new_item.replace(""".v.""",			""".value."""							)
  lowerCamelCase	:			Any           =   new_item.replace(""".q.""",			""".query."""							)

  lowerCamelCase	:			List[Any]           =   new_item.replace("""proj_out""",			"""proj_attn"""							)
  lowerCamelCase	:			List[str]           =   new_item.replace("""norm""",			"""group_norm"""							)

  lowerCamelCase	:			Dict           =   shave_segments(lowerCamelCase,			n_shave_prefix_segments=lowerCamelCase							)
  mapping.append({"""old""": old_item, """new""": new_item}							)

 return mapping





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=None							):
 assert isinstance(lowerCamelCase,			lowerCamelCase							), "Paths should be a list of dicts containing 'old' and 'new' keys."

 if attention_paths_to_split is not None:
  if config is None:
   raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'."""							)

  for path, path_map in attention_paths_to_split.items():
   lowerCamelCase	:			List[Any]           =   old_checkpoint[path]
   lowerCamelCase	:			Optional[Any]           =   old_tensor.shape[0] // 3

   lowerCamelCase	:			Any           =   (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

   lowerCamelCase	:			Optional[int]           =   old_tensor.shape[0] // config.get("""num_head_channels""",			1							) // 3

   lowerCamelCase	:			Optional[int]           =   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
   lowerCamelCase	:			Tuple           =   old_tensor.split(channels // num_heads,			dim=1							)

   lowerCamelCase	:			List[str]           =   query.reshape(lowerCamelCase							).squeeze()
   lowerCamelCase	:			str           =   key.reshape(lowerCamelCase							).squeeze()
   lowerCamelCase	:			Tuple           =   value.reshape(lowerCamelCase							).squeeze()

 for path in paths:
  lowerCamelCase	:			str           =   path["new"]

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  lowerCamelCase	:			str           =   new_path.replace("""down.""",			"""down_blocks."""							)
  lowerCamelCase	:			Tuple           =   new_path.replace("""up.""",			"""up_blocks."""							)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    lowerCamelCase	:			Union[str, Any]           =   new_path.replace(replacement["""old"""],			replacement["""new"""]							)

  if "attentions" in new_path:
   lowerCamelCase	:			List[Any]           =   old_checkpoint[path["old"]].squeeze()
  else:
   lowerCamelCase	:			Union[str, Any]           =   old_checkpoint[path["old"]]





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Any           =   {}

 lowerCamelCase	:			List[str]           =   checkpoint["temb.dense.0.weight"]
 lowerCamelCase	:			List[Any]           =   checkpoint["temb.dense.0.bias"]
 lowerCamelCase	:			Any           =   checkpoint["temb.dense.1.weight"]
 lowerCamelCase	:			Optional[Any]           =   checkpoint["temb.dense.1.bias"]

 lowerCamelCase	:			Optional[Any]           =   checkpoint["norm_out.weight"]
 lowerCamelCase	:			List[str]           =   checkpoint["norm_out.bias"]

 lowerCamelCase	:			int           =   checkpoint["conv_in.weight"]
 lowerCamelCase	:			int           =   checkpoint["conv_in.bias"]
 lowerCamelCase	:			List[Any]           =   checkpoint["conv_out.weight"]
 lowerCamelCase	:			Dict           =   checkpoint["conv_out.bias"]

 lowerCamelCase	:			Dict           =   len({""".""".join(layer.split("""."""							)[:2]							) for layer in checkpoint if """down""" in layer}							)
 lowerCamelCase	:			Tuple           =   {
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)
 }

 lowerCamelCase	:			str           =   len({""".""".join(layer.split("""."""							)[:2]							) for layer in checkpoint if """up""" in layer}							)
 lowerCamelCase	:			Optional[int]           =   {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)}

 for i in range(lowerCamelCase							):
  lowerCamelCase	:			List[str]           =   (i - 1) // (config["layers_per_block"] + 1)

  if any("""downsample""" in layer for layer in down_blocks[i]							):
   lowerCamelCase	:			Any           =   checkpoint[
       F'''down.{i}.downsample.op.weight'''
   ]
   lowerCamelCase	:			List[Any]           =   checkpoint[F'''down.{i}.downsample.op.bias''']
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
  #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

  if any("""block""" in layer for layer in down_blocks[i]							):
   lowerCamelCase	:			Optional[Any]           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			2							).split("""."""							)[:2]							) for layer in down_blocks[i] if """block""" in layer}							)
   lowerCamelCase	:			str           =   {
       layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
       for layer_id in range(lowerCamelCase							)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""]							):
     lowerCamelCase	:			str           =   renew_resnet_paths(blocks[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

  if any("""attn""" in layer for layer in down_blocks[i]							):
   lowerCamelCase	:			List[Any]           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			2							).split("""."""							)[:2]							) for layer in down_blocks[i] if """attn""" in layer}							)
   lowerCamelCase	:			Optional[Any]           =   {
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(lowerCamelCase							)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""]							):
     lowerCamelCase	:			List[Any]           =   renew_attention_paths(attns[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			config=lowerCamelCase							)

 lowerCamelCase	:			List[str]           =   [key for key in checkpoint if "mid.block_1" in key]
 lowerCamelCase	:			List[Any]           =   [key for key in checkpoint if "mid.block_2" in key]
 lowerCamelCase	:			Any           =   [key for key in checkpoint if "mid.attn_1" in key]

 # Mid new 2
 lowerCamelCase	:			Optional[int]           =   renew_resnet_paths(lowerCamelCase							)
 assign_to_checkpoint(
     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}],			)

 lowerCamelCase	:			Dict           =   renew_resnet_paths(lowerCamelCase							)
 assign_to_checkpoint(
     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}],			)

 lowerCamelCase	:			int           =   renew_attention_paths(lowerCamelCase,			in_mid=lowerCamelCase							)
 assign_to_checkpoint(
     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}],			)

 for i in range(lowerCamelCase							):
  lowerCamelCase	:			Any           =   num_up_blocks - 1 - i

  if any("""upsample""" in layer for layer in up_blocks[i]							):
   lowerCamelCase	:			str           =   checkpoint[
       F'''up.{i}.upsample.conv.weight'''
   ]
   lowerCamelCase	:			Union[str, Any]           =   checkpoint[F'''up.{i}.upsample.conv.bias''']

  if any("""block""" in layer for layer in up_blocks[i]							):
   lowerCamelCase	:			List[str]           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			2							).split("""."""							)[:2]							) for layer in up_blocks[i] if """block""" in layer}							)
   lowerCamelCase	:			List[str]           =   {
       layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""] + 1							):
     lowerCamelCase	:			List[Any]           =   {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
     lowerCamelCase	:			List[Any]           =   renew_resnet_paths(blocks[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[replace_indices]							)

  if any("""attn""" in layer for layer in up_blocks[i]							):
   lowerCamelCase	:			Optional[int]           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			2							).split("""."""							)[:2]							) for layer in up_blocks[i] if """attn""" in layer}							)
   lowerCamelCase	:			List[str]           =   {
       layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""] + 1							):
     lowerCamelCase	:			Optional[int]           =   {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
     lowerCamelCase	:			Dict           =   renew_attention_paths(attns[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[replace_indices]							)

 lowerCamelCase	:			Tuple           =   {k.replace("""mid_new_2""",			"""mid_block"""							): v for k, v in new_checkpoint.items()}
 return new_checkpoint





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Optional[int]           =   {}

 lowerCamelCase	:			Any           =   checkpoint["encoder.norm_out.weight"]
 lowerCamelCase	:			Tuple           =   checkpoint["encoder.norm_out.bias"]

 lowerCamelCase	:			Optional[int]           =   checkpoint["encoder.conv_in.weight"]
 lowerCamelCase	:			List[Any]           =   checkpoint["encoder.conv_in.bias"]
 lowerCamelCase	:			List[str]           =   checkpoint["encoder.conv_out.weight"]
 lowerCamelCase	:			List[Any]           =   checkpoint["encoder.conv_out.bias"]

 lowerCamelCase	:			Dict           =   checkpoint["decoder.norm_out.weight"]
 lowerCamelCase	:			List[str]           =   checkpoint["decoder.norm_out.bias"]

 lowerCamelCase	:			Dict           =   checkpoint["decoder.conv_in.weight"]
 lowerCamelCase	:			List[Any]           =   checkpoint["decoder.conv_in.bias"]
 lowerCamelCase	:			List[Any]           =   checkpoint["decoder.conv_out.weight"]
 lowerCamelCase	:			List[str]           =   checkpoint["decoder.conv_out.bias"]

 lowerCamelCase	:			List[str]           =   len({""".""".join(layer.split("""."""							)[:3]							) for layer in checkpoint if """down""" in layer}							)
 lowerCamelCase	:			Any           =   {
     layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)
 }

 lowerCamelCase	:			List[str]           =   len({""".""".join(layer.split("""."""							)[:3]							) for layer in checkpoint if """up""" in layer}							)
 lowerCamelCase	:			Union[str, Any]           =   {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)}

 for i in range(lowerCamelCase							):
  lowerCamelCase	:			Any           =   (i - 1) // (config["layers_per_block"] + 1)

  if any("""downsample""" in layer for layer in down_blocks[i]							):
   lowerCamelCase	:			Optional[Any]           =   checkpoint[
       F'''encoder.down.{i}.downsample.conv.weight'''
   ]
   lowerCamelCase	:			Any           =   checkpoint[
       F'''encoder.down.{i}.downsample.conv.bias'''
   ]

  if any("""block""" in layer for layer in down_blocks[i]							):
   lowerCamelCase	:			Union[str, Any]           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			3							).split("""."""							)[:3]							) for layer in down_blocks[i] if """block""" in layer}							)
   lowerCamelCase	:			Optional[Any]           =   {
       layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
       for layer_id in range(lowerCamelCase							)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""]							):
     lowerCamelCase	:			Dict           =   renew_resnet_paths(blocks[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

  if any("""attn""" in layer for layer in down_blocks[i]							):
   lowerCamelCase	:			Dict           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			3							).split("""."""							)[:3]							) for layer in down_blocks[i] if """attn""" in layer}							)
   lowerCamelCase	:			List[str]           =   {
       layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
       for layer_id in range(lowerCamelCase							)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""]							):
     lowerCamelCase	:			List[str]           =   renew_attention_paths(attns[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			config=lowerCamelCase							)

 lowerCamelCase	:			Any           =   [key for key in checkpoint if "mid.block_1" in key]
 lowerCamelCase	:			List[Any]           =   [key for key in checkpoint if "mid.block_2" in key]
 lowerCamelCase	:			Any           =   [key for key in checkpoint if "mid.attn_1" in key]

 # Mid new 2
 lowerCamelCase	:			Dict           =   renew_resnet_paths(lowerCamelCase							)
 assign_to_checkpoint(
     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}],			)

 lowerCamelCase	:			List[str]           =   renew_resnet_paths(lowerCamelCase							)
 assign_to_checkpoint(
     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}],			)

 lowerCamelCase	:			List[Any]           =   renew_attention_paths(lowerCamelCase,			in_mid=lowerCamelCase							)
 assign_to_checkpoint(
     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}],			)

 for i in range(lowerCamelCase							):
  lowerCamelCase	:			str           =   num_up_blocks - 1 - i

  if any("""upsample""" in layer for layer in up_blocks[i]							):
   lowerCamelCase	:			Optional[Any]           =   checkpoint[
       F'''decoder.up.{i}.upsample.conv.weight'''
   ]
   lowerCamelCase	:			str           =   checkpoint[
       F'''decoder.up.{i}.upsample.conv.bias'''
   ]

  if any("""block""" in layer for layer in up_blocks[i]							):
   lowerCamelCase	:			str           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			3							).split("""."""							)[:3]							) for layer in up_blocks[i] if """block""" in layer}							)
   lowerCamelCase	:			Optional[Any]           =   {
       layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)
   }

   if num_blocks > 0:
    for j in range(config["""layers_per_block"""] + 1							):
     lowerCamelCase	:			Dict           =   {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
     lowerCamelCase	:			str           =   renew_resnet_paths(blocks[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[replace_indices]							)

  if any("""attn""" in layer for layer in up_blocks[i]							):
   lowerCamelCase	:			Dict           =   len(
       {""".""".join(shave_segments(lowerCamelCase,			3							).split("""."""							)[:3]							) for layer in up_blocks[i] if """attn""" in layer}							)
   lowerCamelCase	:			Optional[int]           =   {
       layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowerCamelCase							)
   }

   if num_attn > 0:
    for j in range(config["""layers_per_block"""] + 1							):
     lowerCamelCase	:			Any           =   {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
     lowerCamelCase	:			List[Any]           =   renew_attention_paths(attns[j]							)
     assign_to_checkpoint(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			additional_replacements=[replace_indices]							)

 lowerCamelCase	:			Optional[Any]           =   {k.replace("""mid_new_2""",			"""mid_block"""							): v for k, v in new_checkpoint.items()}
 lowerCamelCase	:			Optional[int]           =   checkpoint["quant_conv.weight"]
 lowerCamelCase	:			Optional[Any]           =   checkpoint["quant_conv.bias"]
 if "quantize.embedding.weight" in checkpoint:
  lowerCamelCase	:			List[Any]           =   checkpoint["quantize.embedding.weight"]
 lowerCamelCase	:			List[Any]           =   checkpoint["post_quant_conv.weight"]
 lowerCamelCase	:			Any           =   checkpoint["post_quant_conv.bias"]

 return new_checkpoint


if __name__ == "__main__":
    _lowerCamelCase           =argparse.ArgumentParser()

    parser.add_argument(
        """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
    )

    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the architecture.""",
    )

    parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

    _lowerCamelCase           =parser.parse_args()
    _lowerCamelCase           =torch.load(args.checkpoint_path)

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

    # unet case
    _lowerCamelCase           ={key.split(""".""")[0] for key in checkpoint.keys()}
    if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        _lowerCamelCase           =convert_vq_autoenc_checkpoint(checkpoint, config)
    else:
        _lowerCamelCase           =convert_ddpm_checkpoint(checkpoint, config)

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

    if config["_class_name"] == "VQModel":
        _lowerCamelCase           =VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    elif config["_class_name"] == "AutoencoderKL":
        _lowerCamelCase           =AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
    else:
        _lowerCamelCase           =UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)

        _lowerCamelCase           =DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

        _lowerCamelCase           =DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)






from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase           =logging.get_logger(__name__)





class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Tuple					 =    ["""input_ids""", """attention_mask"""]
      def __init__(   self					,						__magic_name__="[PAD]"					,						__magic_name__="[BOS]"					,						__magic_name__="[EOS]"					,						__magic_name__="[MASK]"					,						__magic_name__="[CLS]"					,						__magic_name__="[SEP]"					,						__magic_name__=2_0_4_8					,						**__magic_name__					,						):
       lowerCamelCase	:			List[str]           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else pad_token
       lowerCamelCase	:			Dict           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else bos_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else eos_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else mask_token
       lowerCamelCase	:			Any           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else cls_token
       lowerCamelCase	:			Optional[Any]           =   AddedToken(__magic_name__					,						lstrip=__magic_name__					,						rstrip=__magic_name__   ) if isinstance(__magic_name__					,						__magic_name__   ) else sep_token

       super().__init__(
           pad_token=__magic_name__					,						bos_token=__magic_name__					,						eos_token=__magic_name__					,						mask_token=__magic_name__					,						cls_token=__magic_name__					,						sep_token=__magic_name__					,						model_max_length=__magic_name__					,						**__magic_name__					,						)

       lowerCamelCase	:			List[Any]           =   2**8  # utf is 8 bits

       # define special tokens dict
       lowerCamelCase	:			Dict[str, int]           =   {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       lowerCamelCase	:			Optional[int]           =   len(self.special_tokens_encoder   )
       lowerCamelCase	:			Dict[int, str]           =   {v: k for k, v in self.special_tokens_encoder.items()}
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
        lowerCamelCase	:			List[Any]           =   chr(__magic_name__   )
        lowerCamelCase	:			Union[str, Any]           =   i + len(self.special_tokens_encoder   )
       return vocab
      @property
      def 		UpperCamelCase__							(   self   ):
       return self._utf_vocab_size + self._num_special_tokens
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = None					,						__magic_name__ = False   ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__magic_name__					,						token_ids_a=__magic_name__					,						already_has_special_tokens=__magic_name__   )

       # normal case: some special tokens
       if token_ids_a is None:
        return [1] + [0] * len(__magic_name__   ) + [1]
       return [1] + ([0] * len(__magic_name__   )) + [1] + ([0] * len(__magic_name__   )) + [1]
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       else:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			Optional[int]           =   [chr(__magic_name__   ) for i in text.encode("""utf-8"""   )]
       return tokens
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if token in self.special_tokens_encoder:
        lowerCamelCase	:			List[str]           =   self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
        lowerCamelCase	:			List[Any]           =   self.added_tokens_encoder[token]
       elif len(__magic_name__   ) != 1:
        lowerCamelCase	:			Union[str, Any]           =   self.unk_token_id
       else:
        lowerCamelCase	:			int           =   ord(__magic_name__   ) + self._num_special_tokens
       return token_id
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if index in self.special_tokens_decoder:
        lowerCamelCase	:			Union[str, Any]           =   self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
        lowerCamelCase	:			List[str]           =   self.added_tokens_decoder[index]
       else:
        lowerCamelCase	:			Optional[int]           =   chr(index - self._num_special_tokens   )
       return token
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			List[Any]           =   b""""""
       for token in tokens:
        if token in self.special_tokens_decoder:
         lowerCamelCase	:			List[str]           =   self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.added_tokens_decoder:
         lowerCamelCase	:			Optional[int]           =   self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.special_tokens_encoder:
         lowerCamelCase	:			Tuple           =   token.encode("""utf-8"""   )
        elif token in self.added_tokens_encoder:
         lowerCamelCase	:			Union[str, Any]           =   token.encode("""utf-8"""   )
        else:
         lowerCamelCase	:			Dict           =   bytes([ord(__magic_name__   )]   )
        bstring += tok_string
       lowerCamelCase	:			Any           =   bstring.decode("""utf-8"""					,						errors="""replace"""   )
       return string






      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__ = None   ):
       return ()




