




import argparse
import json

import torch

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

def 			lowerCAmelCase_			(  lowerCamelCase:       List[str]     ,				lowerCamelCase:       int=1						)					->			Union[str, Any]:


      '''simple docstring'''




      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('.'						)[n_shave_prefix_segments:]						)
      else:
            return ".".join(path.split('.'						)[:n_shave_prefix_segments]						)

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       List[str]=0						)					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				[]
      for old_item in old_list:
            lowerCamelCase__        =				old_item
            lowerCamelCase__        =				new_item.replace('block.'     ,				'resnets.'						)
            lowerCamelCase__        =				new_item.replace('conv_shorcut'     ,				'conv1'						)
            lowerCamelCase__        =				new_item.replace('in_shortcut'     ,				'conv_shortcut'						)
            lowerCamelCase__        =				new_item.replace('temb_proj'     ,				'time_emb_proj'						)

            lowerCamelCase__        =				shave_segments(_UpperCAmelCase     ,				n_shave_prefix_segments=_UpperCAmelCase						)

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

      return mapping

def 			lowerCAmelCase_			(  lowerCamelCase:       List[str]     ,				lowerCamelCase:       List[str]=0     ,				lowerCamelCase:       List[str]=False						)					->			Dict:


      '''simple docstring'''




      lowerCamelCase__        =				[]
      for old_item in old_list:
            lowerCamelCase__        =				old_item

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

            lowerCamelCase__        =				new_item.replace('proj_out'     ,				'proj_attn'						)
            lowerCamelCase__        =				new_item.replace('norm'     ,				'group_norm'						)

            lowerCamelCase__        =				shave_segments(_UpperCAmelCase     ,				n_shave_prefix_segments=_UpperCAmelCase						)
            mapping.append({'old': old_item, 'new': new_item}						)

      return mapping

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       str     ,				lowerCamelCase:       Dict     ,				lowerCamelCase:       Tuple=None     ,				lowerCamelCase:       Any=None     ,				lowerCamelCase:       Union[str, Any]=None						)					->			Any:


      '''simple docstring'''




      assert isinstance(_UpperCAmelCase     ,				_UpperCAmelCase						), "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__        =				old_checkpoint[path]
                  lowerCamelCase__        =				old_tensor.shape[0] // 3

                  lowerCamelCase__        =				(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

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

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

                  lowerCamelCase__        =				query.reshape(_UpperCAmelCase						).squeeze()
                  lowerCamelCase__        =				key.reshape(_UpperCAmelCase						).squeeze()
                  lowerCamelCase__        =				value.reshape(_UpperCAmelCase						).squeeze()

      for path in paths:
            lowerCamelCase__        =				path['new']

            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            lowerCamelCase__        =				new_path.replace('down.'     ,				'down_blocks.'						)
            lowerCamelCase__        =				new_path.replace('up.'     ,				'up_blocks.'						)

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

            if "attentions" in new_path:
                  lowerCamelCase__        =				old_checkpoint[path['old']].squeeze()
            else:
                  lowerCamelCase__        =				old_checkpoint[path['old']]

def 			lowerCAmelCase_			(  lowerCamelCase:       int     ,				lowerCamelCase:       Tuple						)					->			Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__        =				{}

      lowerCamelCase__        =				checkpoint['temb.dense.0.weight']
      lowerCamelCase__        =				checkpoint['temb.dense.0.bias']
      lowerCamelCase__        =				checkpoint['temb.dense.1.weight']
      lowerCamelCase__        =				checkpoint['temb.dense.1.bias']

      lowerCamelCase__        =				checkpoint['norm_out.weight']
      lowerCamelCase__        =				checkpoint['norm_out.bias']

      lowerCamelCase__        =				checkpoint['conv_in.weight']
      lowerCamelCase__        =				checkpoint['conv_in.bias']
      lowerCamelCase__        =				checkpoint['conv_out.weight']
      lowerCamelCase__        =				checkpoint['conv_out.bias']

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'down' in layer}						)
      lowerCamelCase__        =				{
          layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase						)
      }

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'up' in layer}						)
      lowerCamelCase__        =				{layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase						)}

      for i in range(_UpperCAmelCase						):
            lowerCamelCase__        =				(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				checkpoint[
                      f"""down.{i}.downsample.op.weight"""
                  ]
                  lowerCamelCase__        =				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__        =				len(
                      {'.'.join(shave_segments(_UpperCAmelCase     ,				2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'block' in layer}						)
                  lowerCamelCase__        =				{
                      layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
                      for layer_id in range(_UpperCAmelCase						)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']						):
                              lowerCamelCase__        =				renew_resnet_paths(blocks[j]						)
                              assign_to_checkpoint(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase						)

            if any('attn' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(_UpperCAmelCase     ,				2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
                      for layer_id in range(_UpperCAmelCase						)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']						):
                              lowerCamelCase__        =				renew_attention_paths(attns[j]						)
                              assign_to_checkpoint(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				config=_UpperCAmelCase						)

      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_1' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_2' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.attn_1' in key]

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

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

      lowerCamelCase__        =				renew_attention_paths(_UpperCAmelCase     ,				in_mid=_UpperCAmelCase						)
      assign_to_checkpoint(
          _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,				)

      for i in range(_UpperCAmelCase						):
            lowerCamelCase__        =				num_up_blocks - 1 - i

            if any('upsample' in layer for layer in up_blocks[i]						):
                  lowerCamelCase__        =				checkpoint[
                      f"""up.{i}.upsample.conv.weight"""
                  ]
                  lowerCamelCase__        =				checkpoint[f"""up.{i}.upsample.conv.bias"""]

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

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

            if any('attn' in layer for layer in up_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(_UpperCAmelCase     ,				2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase						)
                  }

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

      lowerCamelCase__        =				{k.replace('mid_new_2'     ,				'mid_block'						): v for k, v in new_checkpoint.items()}
      return new_checkpoint

def 			lowerCAmelCase_			(  lowerCamelCase:       Any     ,				lowerCamelCase:       Optional[int]						)					->			str:


      '''simple docstring'''




      lowerCamelCase__        =				{}

      lowerCamelCase__        =				checkpoint['encoder.norm_out.weight']
      lowerCamelCase__        =				checkpoint['encoder.norm_out.bias']

      lowerCamelCase__        =				checkpoint['encoder.conv_in.weight']
      lowerCamelCase__        =				checkpoint['encoder.conv_in.bias']
      lowerCamelCase__        =				checkpoint['encoder.conv_out.weight']
      lowerCamelCase__        =				checkpoint['encoder.conv_out.bias']

      lowerCamelCase__        =				checkpoint['decoder.norm_out.weight']
      lowerCamelCase__        =				checkpoint['decoder.norm_out.bias']

      lowerCamelCase__        =				checkpoint['decoder.conv_in.weight']
      lowerCamelCase__        =				checkpoint['decoder.conv_in.bias']
      lowerCamelCase__        =				checkpoint['decoder.conv_out.weight']
      lowerCamelCase__        =				checkpoint['decoder.conv_out.bias']

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'down' in layer}						)
      lowerCamelCase__        =				{
          layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase						)
      }

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'up' in layer}						)
      lowerCamelCase__        =				{layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase						)}

      for i in range(_UpperCAmelCase						):
            lowerCamelCase__        =				(i - 1) // (config['layers_per_block'] + 1)

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

            if any('block' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(_UpperCAmelCase     ,				3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'block' in layer}						)
                  lowerCamelCase__        =				{
                      layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
                      for layer_id in range(_UpperCAmelCase						)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']						):
                              lowerCamelCase__        =				renew_resnet_paths(blocks[j]						)
                              assign_to_checkpoint(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase						)

            if any('attn' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(_UpperCAmelCase     ,				3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
                      for layer_id in range(_UpperCAmelCase						)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']						):
                              lowerCamelCase__        =				renew_attention_paths(attns[j]						)
                              assign_to_checkpoint(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				config=_UpperCAmelCase						)

      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_1' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_2' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.attn_1' in key]

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

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

      lowerCamelCase__        =				renew_attention_paths(_UpperCAmelCase     ,				in_mid=_UpperCAmelCase						)
      assign_to_checkpoint(
          _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]     ,				)

      for i in range(_UpperCAmelCase						):
            lowerCamelCase__        =				num_up_blocks - 1 - i

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

            if any('block' in layer for layer in up_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(_UpperCAmelCase     ,				3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'block' in layer}						)
                  lowerCamelCase__        =				{
                      layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase						)
                  }

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

            if any('attn' in layer for layer in up_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(_UpperCAmelCase     ,				3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_UpperCAmelCase						)
                  }

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

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

      return new_checkpoint


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE    :   str       =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.""")

  __SCREAMING_SNAKE_CASE    :   List[str]       =parser.parse_args()
  __SCREAMING_SNAKE_CASE    :   List[Any]       =torch.load(args.checkpoint_path)

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

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

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

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

    __SCREAMING_SNAKE_CASE    :   Union[str, Any]       =DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

    __SCREAMING_SNAKE_CASE    :   Optional[Any]       =DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.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


__SCREAMING_SNAKE_CASE    :   Dict       =logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE    :   Dict       ={
    """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"""
        ),
    },
}

__SCREAMING_SNAKE_CASE    :   int       ={
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =VOCAB_FILES_NAMES
     _snake_case:	List[str]     =PRETRAINED_VOCAB_FILES_MAP
     _snake_case:	str     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case:	str     =["input_ids", "attention_mask"]
     _snake_case:	Union[str, Any]     =LongformerTokenizer




     def __init__(    self  :   str	,  A__  :   Optional[Any]=None	,  A__  :   Dict=None	,  A__  :   Any=None	,  A__  :   Optional[Any]="replace"	,  A__  :   Tuple="<s>"	,  A__  :   Any="</s>"	,  A__  :   Dict="</s>"	,  A__  :   Any="<s>"	,  A__  :   Optional[Any]="<unk>"	,  A__  :   Tuple="<pad>"	,  A__  :   Union[str, Any]="<mask>"	,  A__  :   List[str]=False	,  A__  :   List[str]=True	,  **A__  :   Optional[int]	,  )				->       List[str]:
           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__	,  )

           lowerCamelCase__        =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
           if pre_tok_state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                 lowerCamelCase__        =				getattr(A__	,  pre_tok_state.pop('type'			)			)
                 lowerCamelCase__        =				add_prefix_space
                 lowerCamelCase__        =				pre_tok_class(**A__			)

           lowerCamelCase__        =				add_prefix_space

           lowerCamelCase__        =				'post_processor'
           lowerCamelCase__        =				getattr(self.backend_tokenizer	,  A__	,  A__			)
           if tokenizer_component_instance:
                 lowerCamelCase__        =				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:
                       lowerCamelCase__        =				tuple(state['sep']			)
                 if "cls" in state:
                       lowerCamelCase__        =				tuple(state['cls']			)

                 lowerCamelCase__        =				False

                 if state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                       lowerCamelCase__        =				add_prefix_space
                       lowerCamelCase__        =				True

                 if state.get('trim_offsets'	,  A__			) != trim_offsets:
                       lowerCamelCase__        =				trim_offsets
                       lowerCamelCase__        =				True

                 if changes_to_apply:
                       lowerCamelCase__        =				getattr(A__	,  state.pop('type'			)			)
                       lowerCamelCase__        =				component_class(**A__			)
                       setattr(self.backend_tokenizer	,  A__	,  A__			)




     @property
     def  __snake_case   (    self  :   Tuple			)				->       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  __snake_case   (    self  :   Dict	,  A__  :   Tuple			)				->       int:
           lowerCamelCase__        =				AddedToken(A__	,  lstrip=A__	,  rstrip=A__			) if isinstance(A__	,  A__			) else value
           lowerCamelCase__        =				value




     def  __snake_case   (    self  :   Any	,  *A__  :   Any	,  **A__  :   Any			)				->       BatchEncoding:
           lowerCamelCase__        =				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  __snake_case   (    self  :   Tuple	,  *A__  :   str	,  **A__  :   int			)				->       BatchEncoding:
           lowerCamelCase__        =				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  __snake_case   (    self  :   Union[str, Any]	,  A__  :   str	,  A__  :   Optional[str] = None			)				->       Tuple[str]:
           lowerCamelCase__        =				self._tokenizer.model.save(A__	,  name=A__			)
           return tuple(A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   int	,  A__  :   List[str]=None			)				->       Dict:
           lowerCamelCase__        =				[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  __snake_case   (    self  :   Union[str, Any]	,  A__  :   List[int]	,  A__  :   Optional[List[int]] = None			)				->       List[int]:
           lowerCamelCase__        =				[self.sep_token_id]
           lowerCamelCase__        =				[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]