





'''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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]		)		->  int:
   snake_case 						=   SwinConfig(
       embed_dim=1_92      ,    depths=(2, 2, 18, 2)      ,    num_heads=(6, 12, 24, 48)      ,    window_size=12      ,    out_features=["""stage2""", """stage3""", """stage4"""]      ,    )

   snake_case 						=   DetaConfig(
       backbone_config=__lowerCAmelCase      ,    num_queries=9_00      ,    encoder_ffn_dim=20_48      ,    decoder_ffn_dim=20_48      ,    num_feature_levels=5      ,    assign_first_stage=__lowerCAmelCase      ,    with_box_refine=__lowerCAmelCase      ,    two_stage=__lowerCAmelCase      ,    )

   # set labels
   snake_case 						=   """huggingface/label-files"""
   if "o365" in model_name:
      snake_case 						=   3_66
      snake_case 						=   """object365-id2label.json"""
   else:
      snake_case 						=   91
      snake_case 						=   """coco-detection-id2label.json"""

   snake_case 						=   num_labels
   snake_case 						=   json.load(open(cached_download(hf_hub_url(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)		)      ,    """r"""		)		)
   snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
   snake_case 						=   idalabel
   snake_case 						=   {v: k for k, v in idalabel.items()}

   return config





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int		)		->  Optional[Any]:
   snake_case 						=   []

   # stem
   # fmt: off
   rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")		)
   rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")		)
   rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")		)
   rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")		)
   # stages
   for i in range(len(config.backbone_config.depths		)		):
      for j in range(config.backbone_config.depths[i]		):
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')		)

      if i < 3:
         rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')		)
         rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')		)

   rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")		)
   rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")		)
   rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")		)
   rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")		)
   rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")		)
   rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")		)

   # 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			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							Any		)		->  List[str]:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Optional[int]		)		->  Dict:
   snake_case 						=   [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
   for i in range(len(backbone_config.depths		)		):
      snake_case 						=   num_features[i]
      for j in range(backbone_config.depths[i]		):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         snake_case 						=   state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
         snake_case 						=   state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''		)
         # next, add query, keys and values (in that order) to the state dict
         snake_case 						=   in_proj_weight[:dim, :]
         snake_case 						=   in_proj_bias[: dim]
         snake_case 						=   in_proj_weight[
             dim : dim * 2, :
         ]
         snake_case 						=   in_proj_bias[
             dim : dim * 2
         ]
         snake_case 						=   in_proj_weight[
             -dim :, :
         ]
         snake_case 						=   in_proj_bias[-dim :]
         # fmt: on





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Tuple		)		->  Dict:
   # transformer decoder self-attention layers
   snake_case 						=   config.d_model
   for i in range(config.decoder_layers		):
      # read in weights + bias of input projection layer of self-attention
      snake_case 						=   state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
      snake_case 						=   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
      snake_case 						=   in_proj_weight[:hidden_size, :]
      snake_case 						=   in_proj_bias[:hidden_size]
      snake_case 						=   in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      snake_case 						=   in_proj_bias[hidden_size : hidden_size * 2]
      snake_case 						=   in_proj_weight[-hidden_size:, :]
      snake_case 						=   in_proj_bias[-hidden_size:]





def 	__lowerCamelCase			(      )		->  Union[str, Any]:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)

   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							int		)		->  Dict:
   snake_case 						=   get_deta_config(__lowerCAmelCase		)

   # load original state dict
   if model_name == "deta-swin-large":
      snake_case 						=   hf_hub_download(repo_id="""nielsr/deta-checkpoints"""      ,    filename="""adet_swin_ft.pth"""		)
   elif model_name == "deta-swin-large-o365":
      snake_case 						=   hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""      ,    filename="""deta_swin_pt_o365.pth"""		)
   else:
      raise ValueError(F'''Model name {model_name} not supported'''		)

   snake_case 						=   torch.load(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]

   # original state dict
   for name, param in state_dict.items():
      print(__lowerCAmelCase      ,    param.shape		)

   # rename keys
   snake_case 						=   create_rename_keys(__lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_swin_q_k_v(__lowerCAmelCase      ,    config.backbone_config		)
   read_in_decoder_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase		)

   # fix some prefixes
   for key in state_dict.copy().keys():
      if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
         snake_case 						=   state_dict.pop(__lowerCAmelCase		)
         snake_case 						=   val
      if "input_proj" in key:
         snake_case 						=   state_dict.pop(__lowerCAmelCase		)
         snake_case 						=   val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         snake_case 						=   state_dict.pop(__lowerCAmelCase		)
         snake_case 						=   val

    # finally, create HuggingFace model and load state dict
   snake_case 						=   DetaForObjectDetection(__lowerCAmelCase		)
   model.load_state_dict(__lowerCAmelCase		)
   model.eval()

   snake_case 						=   """cuda""" if torch.cuda.is_available() else """cpu"""
   model.to(__lowerCAmelCase		)

   # load image processor
   snake_case 						=   DetaImageProcessor(format="""coco_detection"""		)

   # verify our conversion on image
   snake_case 						=   prepare_img()
   snake_case 						=   processor(images=__lowerCAmelCase      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]
   snake_case 						=   model(pixel_values.to(__lowerCAmelCase		)		)

   # verify logits
   print("""Logits:"""      ,    outputs.logits[0, :3, :3]		)
   print("""Boxes:"""      ,    outputs.pred_boxes[0, :3, :3]		)
   if model_name == "deta-swin-large":
      snake_case 						=   torch.tensor(
          [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]		)
      snake_case 						=   torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]		)
   elif model_name == "deta-swin-large-o365":
      snake_case 						=   torch.tensor(
          [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]		)
      snake_case 						=   torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]		)
   assert torch.allclose(outputs.logits[0, :3, :3]      ,    expected_logits.to(__lowerCAmelCase		)      ,    atol=1e-4		)
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,    expected_boxes.to(__lowerCAmelCase		)      ,    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(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
      model.save_pretrained(__lowerCAmelCase		)
      processor.save_pretrained(__lowerCAmelCase		)

   # 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__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     type=str,
     default="deta-swin-large",
     choices=["deta-swin-large", "deta-swin-large-o365"],
     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."
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''




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

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


if is_sentencepiece_available():
 from .tokenization_rembert import RemBertTokenizer
else:
 _SCREAMING_SNAKE_CASE							     =			None

_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE							     =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE							     =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE							     =			{
    "google/rembert": 256,
}

_SCREAMING_SNAKE_CASE							     =			"▁"
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   VOCAB_FILES_NAMES
     snake_case_						      =   PRETRAINED_VOCAB_FILES_MAP
     snake_case_						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case_						      =   RemBertTokenizer



     def __init__(   self							:			List[Any]   ,       __snake_case							:			int=None   ,       __snake_case							:			Union[str, Any]=None   ,       __snake_case							:			Tuple=True   ,       __snake_case							:			Dict=True   ,       __snake_case							:			str=False   ,       __snake_case							:			Union[str, Any]="[CLS]"   ,       __snake_case							:			Optional[int]="[SEP]"   ,       __snake_case							:			str="<unk>"   ,       __snake_case							:			Dict="[SEP]"   ,       __snake_case							:			Dict="<pad>"   ,       __snake_case							:			Union[str, Any]="[CLS]"   ,       __snake_case							:			int="[MASK]"   ,       **__snake_case							:			Optional[int]   ,       )->	Tuple:
        # Mask token behave like a normal word, i.e. include the space before it
        snake_case 						=   AddedToken(__snake_case   ,       lstrip=__snake_case   ,       rstrip=__snake_case						) if isinstance(__snake_case   ,       __snake_case						) else mask_token

        super().__init__(
            __snake_case   ,       tokenizer_file=__snake_case   ,       do_lower_case=__snake_case   ,       remove_space=__snake_case   ,       keep_accents=__snake_case   ,       bos_token=__snake_case   ,       eos_token=__snake_case   ,       unk_token=__snake_case   ,       sep_token=__snake_case   ,       pad_token=__snake_case   ,       cls_token=__snake_case   ,       mask_token=__snake_case   ,       **__snake_case   ,       )

        snake_case 						=   do_lower_case
        snake_case 						=   remove_space
        snake_case 						=   keep_accents
        snake_case 						=   vocab_file
        snake_case 						=   False if not self.vocab_file else True



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]
        if token_ids_a is None:
           return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None   ,       __snake_case							:			bool = False						)->	List[int]:

        if already_has_special_tokens:
           if token_ids_a is not None:
              raise ValueError(
                  """You should not supply a second sequence if the provided sequence of """
                  """ids is already formatted with special tokens for the model."""						)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
           return [1] + ([0] * len(__snake_case						)) + [1] + ([0] * len(__snake_case						)) + [1]
        return [1] + ([0] * len(__snake_case						)) + [1]



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep						) * [0]
        return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			str   ,       __snake_case							:			Optional[str] = None						)->	Tuple[str]:
        if not os.path.isdir(__snake_case						):
           logger.error("""Vocabulary path ({}) should be a directory""".format(__snake_case						)						)
           return
        snake_case 						=   os.path.join(
            __snake_case   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

        return (out_vocab_file,)

