

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase	=			logging.get_logger(__name__)
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					Tuple   				=   DetaConfig(
     num_queries=900							,      encoder_ffn_dim=2048							,      decoder_ffn_dim=2048							,      num_feature_levels=5							,      assign_first_stage=__UpperCamelCase							,      with_box_refine=__UpperCamelCase							,      two_stage=__UpperCamelCase							,      )

 # set labels
 UpperCAmelCase__   :					str   				=   91
 UpperCAmelCase__   :					List[str]   				=   """huggingface/label-files"""
 UpperCAmelCase__   :					Any   				=   """coco-detection-id2label.json"""
 UpperCAmelCase__   :					Any   				=   json.load(open(cached_download(hf_hub_url(__UpperCamelCase							,      __UpperCamelCase							,      repo_type="""dataset""" ) )							,      """r""" ) )
 UpperCAmelCase__   :					List[str]   				=   {int(__UpperCamelCase ): v for k, v in idalabel.items()}
 UpperCAmelCase__   :					int   				=   idalabel
 UpperCAmelCase__   :					Optional[int]   				=   {v: k for k, v in idalabel.items()}

 return config
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					str   				=   []

 # stem
 # fmt: off
 rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""") )
 rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""") )
 rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""") )
 rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""") )
 rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""") )
 # stages
 for stage_idx in range(len(config.backbone_config.depths ) ):
  for layer_idx in range(config.backbone_config.depths[stage_idx] ):
   # shortcut
   if layer_idx == 0:
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
        ) )
   # 3 convs
   for i in range(3 ):
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
        ) )
    rename_keys.append(
        (
            F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
            F"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
        ) )
    # transformer encoder
 for i in range(config.encoder_layers ):
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias") )
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias") )
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias") )
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias") )
  rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias") )
  rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias") )
  rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias") )
  rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight") )
  rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias") )

 # transformer decoder
 for i in range(config.decoder_layers ):
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias") )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight") )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias") )

 # fmt: on

 return rename_keys
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Dict   				=   dct.pop(__UpperCamelCase )
 UpperCAmelCase__   :					Any   				=   val
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   config.d_model
 for i in range(config.decoder_layers ):
  # read in weights + bias of input projection layer of self-attention
  UpperCAmelCase__   :					Union[str, Any]   				=   state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
  UpperCAmelCase__   :					Tuple   				=   state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias" )
  # next, add query, keys and values (in that order) to the state dict
  UpperCAmelCase__   :					str   				=   in_proj_weight[:hidden_size, :]
  UpperCAmelCase__   :					Tuple   				=   in_proj_bias[:hidden_size]
  UpperCAmelCase__   :					Tuple   				=   in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  UpperCAmelCase__   :					List[str]   				=   in_proj_bias[hidden_size : hidden_size * 2]
  UpperCAmelCase__   :					str   				=   in_proj_weight[-hidden_size:, :]
  UpperCAmelCase__   :					Union[str, Any]   				=   in_proj_bias[-hidden_size:]
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					List[Any]   				=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase__   :					Optional[Any]   				=   Image.open(requests.get(__UpperCamelCase							,      stream=__UpperCamelCase ).raw )

 return im
@torch.no_grad()
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Tuple   				=   get_deta_config()

 # load original state dict
 if model_name == "deta-resnet-50":
  UpperCAmelCase__   :					str   				=   """adet_checkpoint0011.pth"""
 elif model_name == "deta-resnet-50-24-epochs":
  UpperCAmelCase__   :					List[Any]   				=   """adet_2x_checkpoint0023.pth"""
 else:
  raise ValueError(F"Model name {model_name} not supported" )
 UpperCAmelCase__   :					int   				=   hf_hub_download(repo_id="""nielsr/deta-checkpoints"""							,      filename=__UpperCamelCase )
 UpperCAmelCase__   :					List[Any]   				=   torch.load(__UpperCamelCase							,      map_location="""cpu""" )["""model"""]

 # rename keys
 UpperCAmelCase__   :					Any   				=   create_rename_keys(__UpperCamelCase )
 for src, dest in rename_keys:
  rename_key(__UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase )
 read_in_decoder_q_k_v(__UpperCamelCase							,      __UpperCamelCase )

 # fix some prefixes
 for key in state_dict.copy().keys():
  if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
   UpperCAmelCase__   :					Optional[int]   				=   state_dict.pop(__UpperCamelCase )
   UpperCAmelCase__   :					Optional[Any]   				=   val
  if "input_proj" in key:
   UpperCAmelCase__   :					int   				=   state_dict.pop(__UpperCamelCase )
   UpperCAmelCase__   :					int   				=   val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   UpperCAmelCase__   :					str   				=   state_dict.pop(__UpperCamelCase )
   UpperCAmelCase__   :					Optional[int]   				=   val

    # finally, create HuggingFace model and load state dict
 UpperCAmelCase__   :					str   				=   DetaForObjectDetection(__UpperCamelCase )
 model.load_state_dict(__UpperCamelCase )
 model.eval()

 UpperCAmelCase__   :					Union[str, Any]   				=   """cuda""" if torch.cuda.is_available() else """cpu"""
 model.to(__UpperCamelCase )

 # load image processor
 UpperCAmelCase__   :					Dict   				=   DetaImageProcessor(format="""coco_detection""" )

 # verify our conversion on image
 UpperCAmelCase__   :					int   				=   prepare_img()
 UpperCAmelCase__   :					Any   				=   processor(images=__UpperCamelCase							,      return_tensors="""pt""" )
 UpperCAmelCase__   :					List[Any]   				=   encoding["""pixel_values"""]
 UpperCAmelCase__   :					Any   				=   model(pixel_values.to(__UpperCamelCase ) )

 # verify logits
 if model_name == "deta-resnet-50":
  UpperCAmelCase__   :					Any   				=   torch.tensor(
      [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
  UpperCAmelCase__   :					Optional[Any]   				=   torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]] )
 elif model_name == "deta-resnet-50-24-epochs":
  UpperCAmelCase__   :					Optional[int]   				=   torch.tensor(
      [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
  UpperCAmelCase__   :					List[Any]   				=   torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]] )

 assert torch.allclose(outputs.logits[0, :3, :3]							,      expected_logits.to(__UpperCamelCase )							,      atol=1e-4 )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,      expected_boxes.to(__UpperCamelCase )							,      atol=1e-4 )
 print("""Everything ok!""" )

 if pytorch_dump_folder_path:
  # Save model and processor
  logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..." )
  Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
  model.save_pretrained(__UpperCamelCase )
  processor.save_pretrained(__UpperCamelCase )

 # Push to hub
 if push_to_hub:
  print("""Pushing model and processor to hub...""" )
  model.push_to_hub(F"jozhang97/{model_name}" )
  processor.push_to_hub(F"jozhang97/{model_name}" )


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

      parser.add_argument(
          '--model_name',
          type=str,
          default='deta-resnet-50',
          choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
          help='Name of the model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default=None,
          type=str,
          help='Path to the folder to output PyTorch model.',
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )
      __UpperCAmelCase	=			parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	=			logging.get_logger(__name__)

__UpperCAmelCase	=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
  snake_case_			    =    DebertaTokenizer

  def __init__(					self						:     Optional[Any]   ,A						:     Any=None   ,A						:     Any=None   ,A						:     Dict=None   ,A						:     Optional[int]="replace"   ,A						:     Optional[int]="[CLS]"   ,A						:     str="[SEP]"   ,A						:     Optional[int]="[SEP]"   ,A						:     List[Any]="[CLS]"   ,A						:     Dict="[UNK]"   ,A						:     List[str]="[PAD]"   ,A						:     Optional[Any]="[MASK]"   ,A						:     str=False   ,**A						:     Dict   ,):






   '''simple docstring'''
   super().__init__(
       A   ,A   ,tokenizer_file=A   ,errors=A   ,bos_token=A   ,eos_token=A   ,unk_token=A   ,sep_token=A   ,cls_token=A   ,pad_token=A   ,mask_token=A   ,add_prefix_space=A   ,**A   ,)
   UpperCAmelCase__   :					Tuple   				=   kwargs.pop("""add_bos_token"""   ,A      )

   UpperCAmelCase__   :					Tuple   				=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
   if pre_tok_state.get("""add_prefix_space"""   ,A      ) != add_prefix_space:
    UpperCAmelCase__   :					List[Any]   				=   getattr(A   ,pre_tok_state.pop("""type"""      )      )
    UpperCAmelCase__   :					Any   				=   add_prefix_space
    UpperCAmelCase__   :					List[Any]   				=   pre_tok_class(**A      )

   UpperCAmelCase__   :					str   				=   add_prefix_space

  @property
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   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 				__lowercase    (					self						:     Dict   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   AddedToken(A   ,lstrip=A   ,rstrip=A      ) if isinstance(A   ,A      ) else value
   UpperCAmelCase__   :					List[str]   				=   value

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__   :					Dict   				=   [self.cls_token_id]
   UpperCAmelCase__   :					str   				=   [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   [self.sep_token_id]
   UpperCAmelCase__   :					str   				=   [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      ) * [0] + len(token_ids_a + sep      ) * [1]

  def 				__lowercase    (					self						:     int   ,*A						:     Tuple   ,**A						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   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 				__lowercase    (					self						:     str   ,*A						:     List[str]   ,**A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   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 				__lowercase    (					self						:     Tuple   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   self._tokenizer.model.save(A   ,name=A      )
   return tuple(A      )




  def 				__lowercase    (					self						:     List[str]   ,A						:     "Conversation"      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   []
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(A   ,add_special_tokens=A      ) + [self.eos_token_id]      )

   if len(A      ) > self.model_max_length:
    UpperCAmelCase__   :					Optional[int]   				=   input_ids[-self.model_max_length :]
   return input_ids





