

import argparse
import json

import torch

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

def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[int]=1     )    ->		List[str]:



  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__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     str=0     )    ->		Any:
  UpperCAmelCase			:				str										=					[]
  for old_item in old_list:
    UpperCAmelCase			:				Optional[int]										=					old_item
    UpperCAmelCase			:				Any										=					new_item.replace('''block.'''							,					'''resnets.'''     )
    UpperCAmelCase			:				List[Any]										=					new_item.replace('''conv_shorcut'''							,					'''conv1'''     )
    UpperCAmelCase			:				int										=					new_item.replace('''in_shortcut'''							,					'''conv_shortcut'''     )
    UpperCAmelCase			:				List[str]										=					new_item.replace('''temb_proj'''							,					'''time_emb_proj'''     )

    UpperCAmelCase			:				str										=					shave_segments(__lowerCamelCase							,					n_shave_prefix_segments=__lowerCamelCase     )

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

  return mapping

def      a__	(						UpperCAmelCase				:     List[Any]							,					UpperCAmelCase				:     List[Any]=0							,					UpperCAmelCase				:     Any=False     )    ->		Any:
  UpperCAmelCase			:				Optional[int]										=					[]
  for old_item in old_list:
    UpperCAmelCase			:				Optional[int]										=					old_item

    # In `model.mid`, the layer is called `attn`.
    if not in_mid:
      UpperCAmelCase			:				Optional[int]										=					new_item.replace('''attn'''							,					'''attentions'''     )
    UpperCAmelCase			:				Optional[Any]										=					new_item.replace('''.k.'''							,					'''.key.'''     )
    UpperCAmelCase			:				Optional[Any]										=					new_item.replace('''.v.'''							,					'''.value.'''     )
    UpperCAmelCase			:				Dict										=					new_item.replace('''.q.'''							,					'''.query.'''     )

    UpperCAmelCase			:				Tuple										=					new_item.replace('''proj_out'''							,					'''proj_attn'''     )
    UpperCAmelCase			:				Union[str, Any]										=					new_item.replace('''norm'''							,					'''group_norm'''     )

    UpperCAmelCase			:				Optional[Any]										=					shave_segments(__lowerCamelCase							,					n_shave_prefix_segments=__lowerCamelCase     )
    mapping.append({'''old''': old_item, '''new''': new_item}     )

  return mapping

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     str							,					UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Tuple=None							,					UpperCAmelCase				:     List[Any]=None							,					UpperCAmelCase				:     Union[str, Any]=None     )    ->		str:
  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():
      UpperCAmelCase			:				Any										=					old_checkpoint[path]
      UpperCAmelCase			:				Optional[int]										=					old_tensor.shape[0] // 3

      UpperCAmelCase			:				List[str]										=					(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)

      UpperCAmelCase			:				Dict										=					old_tensor.shape[0] // config.get('''num_head_channels'''							,					1     ) // 3

      UpperCAmelCase			:				Any										=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
      UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				Any										=					old_tensor.split(channels // num_heads							,					dim=1     )

      UpperCAmelCase			:				Optional[int]										=					query.reshape(__lowerCamelCase     ).squeeze()
      UpperCAmelCase			:				Optional[int]										=					key.reshape(__lowerCamelCase     ).squeeze()
      UpperCAmelCase			:				int										=					value.reshape(__lowerCamelCase     ).squeeze()

  for path in paths:
    UpperCAmelCase			:				Any										=					path['''new''']

    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    UpperCAmelCase			:				Any										=					new_path.replace('''down.'''							,					'''down_blocks.'''     )
    UpperCAmelCase			:				int										=					new_path.replace('''up.'''							,					'''up_blocks.'''     )

    if additional_replacements is not None:
      for replacement in additional_replacements:
        UpperCAmelCase			:				Optional[int]										=					new_path.replace(replacement['''old''']							,					replacement['''new''']     )

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

def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Dict     )    ->		str:
  UpperCAmelCase			:				Any										=					{}

  UpperCAmelCase			:				str										=					checkpoint['''temb.dense.0.weight''']
  UpperCAmelCase			:				Optional[Any]										=					checkpoint['''temb.dense.0.bias''']
  UpperCAmelCase			:				Any										=					checkpoint['''temb.dense.1.weight''']
  UpperCAmelCase			:				str										=					checkpoint['''temb.dense.1.bias''']

  UpperCAmelCase			:				List[Any]										=					checkpoint['''norm_out.weight''']
  UpperCAmelCase			:				List[Any]										=					checkpoint['''norm_out.bias''']

  UpperCAmelCase			:				List[Any]										=					checkpoint['''conv_in.weight''']
  UpperCAmelCase			:				Optional[int]										=					checkpoint['''conv_in.bias''']
  UpperCAmelCase			:				Optional[int]										=					checkpoint['''conv_out.weight''']
  UpperCAmelCase			:				List[str]										=					checkpoint['''conv_out.bias''']

  UpperCAmelCase			:				List[str]										=					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''down''' in layer}     )
  UpperCAmelCase			:				int										=					{
      layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(__lowerCamelCase     )
  }

  UpperCAmelCase			:				str										=					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''up''' in layer}     )
  UpperCAmelCase			:				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     ):
    UpperCAmelCase			:				List[str]										=					(i - 1) // (config['''layers_per_block'''] + 1)

    if any('''downsample''' in layer for layer in down_blocks[i]     ):
      UpperCAmelCase			:				int										=					checkpoint[
          f'''down.{i}.downsample.op.weight'''
      ]
      UpperCAmelCase			:				Optional[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]     ):
      UpperCAmelCase			:				List[str]										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					2     ).split('''.'''     )[:2]     ) for layer in down_blocks[i] if '''block''' in layer}     )
      UpperCAmelCase			:				List[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''']     ):
          UpperCAmelCase			:				Optional[int]										=					renew_resnet_paths(blocks[j]     )
          assign_to_checkpoint(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase     )

    if any('''attn''' in layer for layer in down_blocks[i]     ):
      UpperCAmelCase			:				Tuple										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					2     ).split('''.'''     )[:2]     ) for layer in down_blocks[i] if '''attn''' in layer}     )
      UpperCAmelCase			:				int										=					{
          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''']     ):
          UpperCAmelCase			:				Tuple										=					renew_attention_paths(attns[j]     )
          assign_to_checkpoint(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					config=__lowerCamelCase     )

  UpperCAmelCase			:				Optional[Any]										=					[key for key in checkpoint if '''mid.block_1''' in key]
  UpperCAmelCase			:				Any										=					[key for key in checkpoint if '''mid.block_2''' in key]
  UpperCAmelCase			:				List[Any]										=					[key for key in checkpoint if '''mid.attn_1''' in key]

  # Mid new 2
  UpperCAmelCase			:				List[str]										=					renew_resnet_paths(__lowerCamelCase     )
  assign_to_checkpoint(
      __lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]							,					)

  UpperCAmelCase			:				Optional[int]										=					renew_resnet_paths(__lowerCamelCase     )
  assign_to_checkpoint(
      __lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]							,					)

  UpperCAmelCase			:				str										=					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     ):
    UpperCAmelCase			:				str										=					num_up_blocks - 1 - i

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

    if any('''block''' in layer for layer in up_blocks[i]     ):
      UpperCAmelCase			:				Tuple										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					2     ).split('''.'''     )[:2]     ) for layer in up_blocks[i] if '''block''' in layer}     )
      UpperCAmelCase			:				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     ):
          UpperCAmelCase			:				List[str]										=					{'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
          UpperCAmelCase			:				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]     ):
      UpperCAmelCase			:				int										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					2     ).split('''.'''     )[:2]     ) for layer in up_blocks[i] if '''attn''' in layer}     )
      UpperCAmelCase			:				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     ):
          UpperCAmelCase			:				Optional[Any]										=					{'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
          UpperCAmelCase			:				Optional[Any]										=					renew_attention_paths(attns[j]     )
          assign_to_checkpoint(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					additional_replacements=[replace_indices]     )

  UpperCAmelCase			:				List[Any]										=					{k.replace('''mid_new_2'''							,					'''mid_block'''     ): v for k, v in new_checkpoint.items()}
  return new_checkpoint

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Any     )    ->		Optional[Any]:
  UpperCAmelCase			:				Dict										=					{}

  UpperCAmelCase			:				List[str]										=					checkpoint['''encoder.norm_out.weight''']
  UpperCAmelCase			:				List[Any]										=					checkpoint['''encoder.norm_out.bias''']

  UpperCAmelCase			:				int										=					checkpoint['''encoder.conv_in.weight''']
  UpperCAmelCase			:				List[str]										=					checkpoint['''encoder.conv_in.bias''']
  UpperCAmelCase			:				List[str]										=					checkpoint['''encoder.conv_out.weight''']
  UpperCAmelCase			:				str										=					checkpoint['''encoder.conv_out.bias''']

  UpperCAmelCase			:				Any										=					checkpoint['''decoder.norm_out.weight''']
  UpperCAmelCase			:				str										=					checkpoint['''decoder.norm_out.bias''']

  UpperCAmelCase			:				Optional[int]										=					checkpoint['''decoder.conv_in.weight''']
  UpperCAmelCase			:				List[str]										=					checkpoint['''decoder.conv_in.bias''']
  UpperCAmelCase			:				Tuple										=					checkpoint['''decoder.conv_out.weight''']
  UpperCAmelCase			:				List[Any]										=					checkpoint['''decoder.conv_out.bias''']

  UpperCAmelCase			:				Optional[int]										=					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''down''' in layer}     )
  UpperCAmelCase			:				List[Any]										=					{
      layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(__lowerCamelCase     )
  }

  UpperCAmelCase			:				List[str]										=					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''up''' in layer}     )
  UpperCAmelCase			:				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     ):
    UpperCAmelCase			:				int										=					(i - 1) // (config['''layers_per_block'''] + 1)

    if any('''downsample''' in layer for layer in down_blocks[i]     ):
      UpperCAmelCase			:				int										=					checkpoint[
          f'''encoder.down.{i}.downsample.conv.weight'''
      ]
      UpperCAmelCase			:				List[Any]										=					checkpoint[
          f'''encoder.down.{i}.downsample.conv.bias'''
      ]

    if any('''block''' in layer for layer in down_blocks[i]     ):
      UpperCAmelCase			:				List[Any]										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					3     ).split('''.'''     )[:3]     ) for layer in down_blocks[i] if '''block''' in layer}     )
      UpperCAmelCase			:				List[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''']     ):
          UpperCAmelCase			:				int										=					renew_resnet_paths(blocks[j]     )
          assign_to_checkpoint(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase     )

    if any('''attn''' in layer for layer in down_blocks[i]     ):
      UpperCAmelCase			:				Optional[int]										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					3     ).split('''.'''     )[:3]     ) for layer in down_blocks[i] if '''attn''' in layer}     )
      UpperCAmelCase			:				List[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''']     ):
          UpperCAmelCase			:				Optional[int]										=					renew_attention_paths(attns[j]     )
          assign_to_checkpoint(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					config=__lowerCamelCase     )

  UpperCAmelCase			:				Any										=					[key for key in checkpoint if '''mid.block_1''' in key]
  UpperCAmelCase			:				Tuple										=					[key for key in checkpoint if '''mid.block_2''' in key]
  UpperCAmelCase			:				Dict										=					[key for key in checkpoint if '''mid.attn_1''' in key]

  # Mid new 2
  UpperCAmelCase			:				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'''}]							,					)

  UpperCAmelCase			:				Tuple										=					renew_resnet_paths(__lowerCamelCase     )
  assign_to_checkpoint(
      __lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]							,					)

  UpperCAmelCase			:				Union[str, 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     ):
    UpperCAmelCase			:				Optional[Any]										=					num_up_blocks - 1 - i

    if any('''upsample''' in layer for layer in up_blocks[i]     ):
      UpperCAmelCase			:				Optional[Any]										=					checkpoint[
          f'''decoder.up.{i}.upsample.conv.weight'''
      ]
      UpperCAmelCase			:				Dict										=					checkpoint[
          f'''decoder.up.{i}.upsample.conv.bias'''
      ]

    if any('''block''' in layer for layer in up_blocks[i]     ):
      UpperCAmelCase			:				Union[str, Any]										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					3     ).split('''.'''     )[:3]     ) for layer in up_blocks[i] if '''block''' in layer}     )
      UpperCAmelCase			:				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     ):
          UpperCAmelCase			:				Optional[Any]										=					{'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
          UpperCAmelCase			:				Union[str, 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]     ):
      UpperCAmelCase			:				List[Any]										=					len(
          {'''.'''.join(shave_segments(__lowerCamelCase							,					3     ).split('''.'''     )[:3]     ) for layer in up_blocks[i] if '''attn''' in layer}     )
      UpperCAmelCase			:				List[Any]										=					{
          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     ):
          UpperCAmelCase			:				List[str]										=					{'''old''': f'''up_blocks.{i}''', '''new''': f'''up_blocks.{block_id}'''}
          UpperCAmelCase			:				Optional[int]										=					renew_attention_paths(attns[j]     )
          assign_to_checkpoint(__lowerCamelCase							,					__lowerCamelCase							,					__lowerCamelCase							,					additional_replacements=[replace_indices]     )

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

  return new_checkpoint


if __name__ == "__main__":
     _lowerCamelCase							:  Dict     =				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							:  int     =				parser.parse_args()
     _lowerCamelCase							:  Optional[Any]     =				torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          _lowerCamelCase							:  List[Any]     =				json.loads(f.read())

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

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

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

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

          _lowerCamelCase							:  List[Any]     =				DDPMPipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							SCREAMING_SNAKE_CASE__       			=				None


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__       			=				{
    """facebook/xglm-564M""": 2_0_4_8,
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__          =				["input_ids", "attention_mask"]
							lowerCAmelCase__          =				XGLMTokenizer




							def __init__(  self						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="<unk>"						,       UpperCAmelCase="<pad>"						,       **UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 7
								lowercase_			 = [F'<madeupword{i}>' for i in range(self.num_madeup_words  )]

								lowercase_			 = kwargs.get("additional_special_tokens"						,       []  )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    UpperCAmelCase						,       tokenizer_file=UpperCAmelCase						,       bos_token=UpperCAmelCase						,       eos_token=UpperCAmelCase						,       sep_token=UpperCAmelCase						,       cls_token=UpperCAmelCase						,       unk_token=UpperCAmelCase						,       pad_token=UpperCAmelCase						,       **UpperCAmelCase						,       )

								lowercase_			 = vocab_file
								lowercase_			 = False if not self.vocab_file else True




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								lowercase_			 = [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								lowercase_			 = [self.sep_token_id]

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




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

								if not os.path.isdir(UpperCAmelCase  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'  )
									return
								lowercase_			 = os.path.join(
								    UpperCAmelCase						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
									copyfile(self.vocab_file						,       UpperCAmelCase  )

								return (out_vocab_file,)

