
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
	__a			:  Optional[int]		    =  SwinConfig.from_pretrained(
	    '''microsoft/swin-tiny-patch4-window7-224'''  ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']  )
	__a			:  Tuple		    =  MaskFormerConfig(backbone_config=UpperCAmelCase__  )

	__a			:  List[str]		    =  '''huggingface/label-files'''
	if "ade20k-full" in model_name:
		# this should be ok
		__a			:  List[Any]		    =  8_4_7
		__a			:  Tuple		    =  '''maskformer-ade20k-full-id2label.json'''
	elif "ade" in model_name:
		# this should be ok
		__a			:  Any		    =  1_5_0
		__a			:  Union[str, Any]		    =  '''ade20k-id2label.json'''
	elif "coco-stuff" in model_name:
		# this should be ok
		__a			:  Dict		    =  1_7_1
		__a			:  List[str]		    =  '''maskformer-coco-stuff-id2label.json'''
	elif "coco" in model_name:
		# TODO
		__a			:  Optional[Any]		    =  1_3_3
		__a			:  Union[str, Any]		    =  '''coco-panoptic-id2label.json'''
	elif "cityscapes" in model_name:
		# this should be ok
		__a			:  Optional[Any]		    =  1_9
		__a			:  List[Any]		    =  '''cityscapes-id2label.json'''
	elif "vistas" in model_name:
		# this should be ok
		__a			:  str		    =  6_5
		__a			:  Any		    =  '''mapillary-vistas-id2label.json'''

	__a			:  Optional[int]		    =  json.load(open(hf_hub_download(UpperCAmelCase__  ,       UpperCAmelCase__  ,       repo_type='''dataset'''  )  ,       '''r'''  )  )
	__a			:  Union[str, Any]		    =  {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}

	return config




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
	__a			:  List[Any]		    =  []
	# stem
	# fmt: off
	rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')  )
	rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')  )
	rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')  )
	rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")  )
			rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")  )

		if i < 3:
			rename_keys.append((f"backbone.layers.{i}.downsample.reduction.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")  )
			rename_keys.append((f"backbone.layers.{i}.downsample.norm.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")  )
			rename_keys.append((f"backbone.layers.{i}.downsample.norm.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")  )
		rename_keys.append((f"backbone.norm{i}.weight", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")  )
		rename_keys.append((f"backbone.norm{i}.bias", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")  )

	# FPN
	rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')  )
	rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')  )
	rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')  )
	for source_index, target_index in zip(range(3  ,       0  ,       -1  )  ,       range(0  ,       3  )  ):
		rename_keys.append((f"sem_seg_head.adapter_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")  )
		rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")  )
		rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")  )
		rename_keys.append((f"sem_seg_head.layer_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")  )
		rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")  )
		rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")  )
	rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')  )
	rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')  )

	# Transformer decoder
	for idx in range(config.decoder_config.decoder_layers  ):
		# self-attention out projection
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")  )
		# cross-attention out projection
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")  )
		# MLP 1
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", f"model.transformer_module.decoder.layers.{idx}.fc1.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", f"model.transformer_module.decoder.layers.{idx}.fc1.bias")  )
		# MLP 2
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", f"model.transformer_module.decoder.layers.{idx}.fc2.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", f"model.transformer_module.decoder.layers.{idx}.fc2.bias")  )
		# layernorm 1 (self-attention layernorm)
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")  )
		# layernorm 2 (cross-attention layernorm)
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")  )
		# layernorm 3 (final layernorm)
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")  )

	rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')  )
	rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')  )

	# heads on top
	rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')  )

	rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')  )
	rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')  )

	rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')  )
	rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')  )

	for i in range(3  ):
		rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.weight", f"mask_embedder.{i}.0.weight")  )
		rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.bias", f"mask_embedder.{i}.0.bias")  )
	# fmt: on

	return rename_keys




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[str]  ):
	__a			:  Optional[Any]		    =  dct.pop(UpperCAmelCase__  )
	__a			:  Tuple		    =  val




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    List[Any]  ):
	__a			:  Optional[int]		    =  [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
	for i in range(len(backbone_config.depths  )  ):
		__a			:  Tuple		    =  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)
			__a			:  Dict		    =  state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"  )
			__a			:  List[Any]		    =  state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"  )
			# next, add query, keys and values (in that order) to the state dict
			__a			:  Any		    =  in_proj_weight[:dim, :]
			__a			:  str		    =  in_proj_bias[: dim]
			__a			:  List[str]		    =  in_proj_weight[
			    dim : dim * 2, :
			]
			__a			:  List[str]		    =  in_proj_bias[
			    dim : dim * 2
			]
			__a			:  Tuple		    =  in_proj_weight[
			    -dim :, :
			]
			__a			:  int		    =  in_proj_bias[-dim :]
			# fmt: on




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Any  ):
	# fmt: off
	__a			:  int		    =  config.decoder_config.hidden_size
	for idx in range(config.decoder_config.decoder_layers  ):
		# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
		__a			:  Dict		    =  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"  )
		__a			:  int		    =  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"  )
		# next, add query, keys and values (in that order) to the state dict
		__a			:  Union[str, Any]		    =  in_proj_weight[: hidden_size, :]
		__a			:  Union[str, Any]		    =  in_proj_bias[:config.hidden_size]
		__a			:  List[Any]		    =  in_proj_weight[hidden_size : hidden_size * 2, :]
		__a			:  Tuple		    =  in_proj_bias[hidden_size : hidden_size * 2]
		__a			:  Optional[int]		    =  in_proj_weight[-hidden_size :, :]
		__a			:  Union[str, Any]		    =  in_proj_bias[-hidden_size :]
		# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
		__a			:  Dict		    =  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"  )
		__a			:  Dict		    =  state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"  )
		# next, add query, keys and values (in that order) to the state dict
		__a			:  Dict		    =  in_proj_weight[: hidden_size, :]
		__a			:  List[str]		    =  in_proj_bias[:config.hidden_size]
		__a			:  Optional[int]		    =  in_proj_weight[hidden_size : hidden_size * 2, :]
		__a			:  Union[str, Any]		    =  in_proj_bias[hidden_size : hidden_size * 2]
		__a			:  Optional[int]		    =  in_proj_weight[-hidden_size :, :]
		__a			:  List[Any]		    =  in_proj_bias[-hidden_size :]
	# fmt: on




def        __UpperCamelCase					(    ):
	__a			:  int		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__a			:  Any		    =  Image.open(requests.get(UpperCAmelCase__  ,       stream=UpperCAmelCase__  ).raw  )
	return im




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    int = False  ):
	__a			:  Optional[Any]		    =  get_maskformer_config(UpperCAmelCase__  )

	# load original state_dict
	with open(UpperCAmelCase__  ,       '''rb'''  ) as f:
		__a			:  List[str]		    =  pickle.load(UpperCAmelCase__  )
	__a			:  Optional[int]		    =  data['''model''']

	# for name, param in state_dict.items():
	#     print(name, param.shape)

	# rename keys
	__a			:  List[str]		    =  create_rename_keys(UpperCAmelCase__  )
	for src, dest in rename_keys:
		rename_key(UpperCAmelCase__  ,       UpperCAmelCase__  ,       UpperCAmelCase__  )
	read_in_swin_q_k_v(UpperCAmelCase__  ,       config.backbone_config  )
	read_in_decoder_q_k_v(UpperCAmelCase__  ,       UpperCAmelCase__  )

	# update to torch tensors
	for key, value in state_dict.items():
		__a			:  str		    =  torch.from_numpy(UpperCAmelCase__  )

	# load 🤗 model
	__a			:  List[Any]		    =  MaskFormerForInstanceSegmentation(UpperCAmelCase__  )
	model.eval()

	for name, param in model.named_parameters():
		print(UpperCAmelCase__  ,       param.shape  )

	__a    ,	__a			:  int		    =  model.load_state_dict(UpperCAmelCase__  ,       strict=UpperCAmelCase__  )
	assert missing_keys == [
	    "model.pixel_level_module.encoder.model.layernorm.weight",
	    "model.pixel_level_module.encoder.model.layernorm.bias",
	]
	assert len(UpperCAmelCase__  ) == 0, f"Unexpected keys: {unexpected_keys}"

	# verify results
	__a			:  int		    =  prepare_img()
	if "vistas" in model_name:
		__a			:  str		    =  6_5
	elif "cityscapes" in model_name:
		__a			:  Optional[Any]		    =  6_5_5_3_5
	else:
		__a			:  Any		    =  2_5_5
	__a			:  Dict		    =  True if '''ade''' in model_name else False
	__a			:  Optional[int]		    =  MaskFormerImageProcessor(ignore_index=UpperCAmelCase__  ,       reduce_labels=UpperCAmelCase__  )

	__a			:  Dict		    =  image_processor(UpperCAmelCase__  ,       return_tensors='''pt'''  )

	__a			:  Optional[int]		    =  model(**UpperCAmelCase__  )

	print('''Logits:'''  ,       outputs.class_queries_logits[0, :3, :3]  )

	if model_name == "maskformer-swin-tiny-ade":
		__a			:  str		    =  torch.tensor(
		    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]  )
	assert torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,       UpperCAmelCase__  ,       atol=1e-4  )
	print('''Looks ok!'''  )

	if pytorch_dump_folder_path is not None:
		print(f"Saving model and image processor to {pytorch_dump_folder_path}"  )
		Path(UpperCAmelCase__  ).mkdir(exist_ok=UpperCAmelCase__  )
		model.save_pretrained(UpperCAmelCase__  )
		image_processor.save_pretrained(UpperCAmelCase__  )

	if push_to_hub:
		print('''Pushing model and image processor to the hub...'''  )
		model.push_to_hub(f"nielsr/{model_name}"  )
		image_processor.push_to_hub(f"nielsr/{model_name}"  )


if __name__ == "__main__":
						lowercase__          =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='maskformer-swin-tiny-ade',
						    type=str,
						    help=('Name of the MaskFormer model you\'d like to convert',),
						)
						parser.add_argument(
						    '--checkpoint_path',
						    default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
						    type=str,
						    help='Path to the original state dict (.pth file).',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						lowercase__          =parser.parse_args()
						convert_maskformer_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

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

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_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			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [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			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
