

'''simple docstring'''






import argparse
import json
from pathlib import Path

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

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = DPTConfig(embedding_type='''hybrid'''      )

 if "large" in checkpoint_url:
  __lowercase							  = 1024
  __lowercase							  = 4096
  __lowercase							  = 24
  __lowercase							  = 16
  __lowercase							  = [5, 11, 17, 23]
  __lowercase							  = [256, 512, 1024, 1024]
  __lowercase							  = (1, 384, 384)

 if "nyu" or "midas" in checkpoint_url:
  __lowercase							  = 768
  __lowercase							  = [1, 1, 1, 0.5]
  __lowercase							  = [256, 512, 768, 768]
  __lowercase							  = 150
  __lowercase							  = 16
  __lowercase							  = (1, 384, 384)
  __lowercase							  = False
  __lowercase							  = "project"

 if "ade" in checkpoint_url:
  __lowercase							  = True
  __lowercase							  = 768
  __lowercase							  = [1, 1, 1, 0.5]
  __lowercase							  = 150
  __lowercase							  = 16
  __lowercase							  = "huggingface/label-files"
  __lowercase							  = "ade20k-id2label.json"
  __lowercase							  = json.load(open(cached_download(hf_hub_url(A__     ,				A__     ,				repo_type='''dataset'''      )      )     ,				'''r'''      )      )
  __lowercase							  = {int(A__      ): v for k, v in idalabel.items()}
  __lowercase							  = idalabel
  __lowercase							  = {v: k for k, v in idalabel.items()}
  __lowercase							  = [1, 150, 480, 480]

 return config, expected_shape


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
 for k in ignore_keys:
  state_dict.pop(A__     ,				A__      )


def 		_A     (    A__      ):






 """simple docstring"""



 if (
     "pretrained.model" in name
     and "cls_token" not in name
     and "pos_embed" not in name
     and "patch_embed" not in name
 ):
  __lowercase							  = name.replace('''pretrained.model'''     ,				'''dpt.encoder'''      )
 if "pretrained.model" in name:
  __lowercase							  = name.replace('''pretrained.model'''     ,				'''dpt.embeddings'''      )
 if "patch_embed" in name:
  __lowercase							  = name.replace('''patch_embed'''     ,				''''''      )
 if "pos_embed" in name:
  __lowercase							  = name.replace('''pos_embed'''     ,				'''position_embeddings'''      )
 if "attn.proj" in name:
  __lowercase							  = name.replace('''attn.proj'''     ,				'''attention.output.dense'''      )
 if "proj" in name and "project" not in name:
  __lowercase							  = name.replace('''proj'''     ,				'''projection'''      )
 if "blocks" in name:
  __lowercase							  = name.replace('''blocks'''     ,				'''layer'''      )
 if "mlp.fc1" in name:
  __lowercase							  = name.replace('''mlp.fc1'''     ,				'''intermediate.dense'''      )
 if "mlp.fc2" in name:
  __lowercase							  = name.replace('''mlp.fc2'''     ,				'''output.dense'''      )
 if "norm1" in name and "backbone" not in name:
  __lowercase							  = name.replace('''norm1'''     ,				'''layernorm_before'''      )
 if "norm2" in name and "backbone" not in name:
  __lowercase							  = name.replace('''norm2'''     ,				'''layernorm_after'''      )
 if "scratch.output_conv" in name:
  __lowercase							  = name.replace('''scratch.output_conv'''     ,				'''head'''      )
 if "scratch" in name:
  __lowercase							  = name.replace('''scratch'''     ,				'''neck'''      )
 if "layer1_rn" in name:
  __lowercase							  = name.replace('''layer1_rn'''     ,				'''convs.0'''      )
 if "layer2_rn" in name:
  __lowercase							  = name.replace('''layer2_rn'''     ,				'''convs.1'''      )
 if "layer3_rn" in name:
  __lowercase							  = name.replace('''layer3_rn'''     ,				'''convs.2'''      )
 if "layer4_rn" in name:
  __lowercase							  = name.replace('''layer4_rn'''     ,				'''convs.3'''      )
 if "refinenet" in name:
  __lowercase							  = int(name[len('''neck.refinenet'''      ) : len('''neck.refinenet'''      ) + 1]      )
  # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
  __lowercase							  = name.replace(F"refinenet{layer_idx}"     ,				F"fusion_stage.layers.{abs(layer_idx-4      )}"      )
 if "out_conv" in name:
  __lowercase							  = name.replace('''out_conv'''     ,				'''projection'''      )
 if "resConfUnit1" in name:
  __lowercase							  = name.replace('''resConfUnit1'''     ,				'''residual_layer1'''      )
 if "resConfUnit2" in name:
  __lowercase							  = name.replace('''resConfUnit2'''     ,				'''residual_layer2'''      )
 if "conv1" in name:
  __lowercase							  = name.replace('''conv1'''     ,				'''convolution1'''      )
 if "conv2" in name:
  __lowercase							  = name.replace('''conv2'''     ,				'''convolution2'''      )
 # readout blocks
 if "pretrained.act_postprocess1.0.project.0" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess1.0.project.0'''     ,				'''neck.reassemble_stage.readout_projects.0.0'''      )
 if "pretrained.act_postprocess2.0.project.0" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess2.0.project.0'''     ,				'''neck.reassemble_stage.readout_projects.1.0'''      )
 if "pretrained.act_postprocess3.0.project.0" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess3.0.project.0'''     ,				'''neck.reassemble_stage.readout_projects.2.0'''      )
 if "pretrained.act_postprocess4.0.project.0" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess4.0.project.0'''     ,				'''neck.reassemble_stage.readout_projects.3.0'''      )

 # resize blocks
 if "pretrained.act_postprocess1.3" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess1.3'''     ,				'''neck.reassemble_stage.layers.0.projection'''      )
 if "pretrained.act_postprocess1.4" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess1.4'''     ,				'''neck.reassemble_stage.layers.0.resize'''      )
 if "pretrained.act_postprocess2.3" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess2.3'''     ,				'''neck.reassemble_stage.layers.1.projection'''      )
 if "pretrained.act_postprocess2.4" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess2.4'''     ,				'''neck.reassemble_stage.layers.1.resize'''      )
 if "pretrained.act_postprocess3.3" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess3.3'''     ,				'''neck.reassemble_stage.layers.2.projection'''      )
 if "pretrained.act_postprocess4.3" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess4.3'''     ,				'''neck.reassemble_stage.layers.3.projection'''      )
 if "pretrained.act_postprocess4.4" in name:
  __lowercase							  = name.replace('''pretrained.act_postprocess4.4'''     ,				'''neck.reassemble_stage.layers.3.resize'''      )
 if "pretrained" in name:
  __lowercase							  = name.replace('''pretrained'''     ,				'''dpt'''      )
 if "bn" in name:
  __lowercase							  = name.replace('''bn'''     ,				'''batch_norm'''      )
 if "head" in name:
  __lowercase							  = name.replace('''head'''     ,				'''head.head'''      )
 if "encoder.norm" in name:
  __lowercase							  = name.replace('''encoder.norm'''     ,				'''layernorm'''      )
 if "auxlayer" in name:
  __lowercase							  = name.replace('''auxlayer'''     ,				'''auxiliary_head.head'''      )
 if "backbone" in name:
  __lowercase							  = name.replace('''backbone'''     ,				'''backbone.bit.encoder'''      )

 if ".." in name:
  __lowercase							  = name.replace('''..'''     ,				'''.'''      )

 if "stem.conv" in name:
  __lowercase							  = name.replace('''stem.conv'''     ,				'''bit.embedder.convolution'''      )
 if "blocks" in name:
  __lowercase							  = name.replace('''blocks'''     ,				'''layers'''      )
 if "convolution" in name and "backbone" in name:
  __lowercase							  = name.replace('''convolution'''     ,				'''conv'''      )
 if "layer" in name and "backbone" in name:
  __lowercase							  = name.replace('''layer'''     ,				'''layers'''      )
 if "backbone.bit.encoder.bit" in name:
  __lowercase							  = name.replace('''backbone.bit.encoder.bit'''     ,				'''backbone.bit'''      )
 if "embedder.conv" in name:
  __lowercase							  = name.replace('''embedder.conv'''     ,				'''embedder.convolution'''      )
 if "backbone.bit.encoder.stem.norm" in name:
  __lowercase							  = name.replace('''backbone.bit.encoder.stem.norm'''     ,				'''backbone.bit.embedder.norm'''      )
 return name


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 for i in range(config.num_hidden_layers      ):
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __lowercase							  = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"      )
  __lowercase							  = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias"      )
  # next, add query, keys and values (in that order) to the state dict
  __lowercase							  = in_proj_weight[: config.hidden_size, :]
  __lowercase							  = in_proj_bias[: config.hidden_size]
  __lowercase							  = in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __lowercase							  = in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __lowercase							  = in_proj_weight[
      -config.hidden_size :, :
  ]
  __lowercase							  = in_proj_bias[-config.hidden_size :]


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = "http://images.cocodataset.org/val2017/000000039769.jpg"
 __lowercase							  = Image.open(requests.get(A__     ,				stream=A__      ).raw      )
 return im


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = get_dpt_config(A__      )
 # load original state_dict from URL
 # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
 __lowercase							  = torch.load(A__     ,				map_location='''cpu'''      )
 # remove certain keys
 remove_ignore_keys_(A__      )
 # rename keys
 for key in state_dict.copy().keys():
  __lowercase							  = state_dict.pop(A__      )
  __lowercase							  = val
 # read in qkv matrices
 read_in_q_k_v(A__     ,				A__      )

 # load HuggingFace model
 __lowercase							  = DPTForSemanticSegmentation(A__      ) if "ade" in checkpoint_url else DPTForDepthEstimation(A__      )
 model.load_state_dict(A__      )
 model.eval()

 # Check outputs on an image
 __lowercase							  = 480 if "ade" in checkpoint_url else 384
 __lowercase							  = DPTImageProcessor(size=A__      )

 __lowercase							  = prepare_img()
 __lowercase							  = image_processor(A__     ,				return_tensors='''pt'''      )

 # forward pass
 __lowercase							  = model(**A__      ).logits if "ade" in checkpoint_url else model(**A__      ).predicted_depth

 if show_prediction:
  __lowercase							  = (
      torch.nn.functional.interpolate(
          outputs.unsqueeze(1      )     ,				size=(image.size[1], image.size[0])     ,				mode='''bicubic'''     ,				align_corners=A__     ,				)
      .squeeze()
      .cpu()
      .numpy()
  )

  Image.fromarray((prediction / prediction.max()) * 255      ).show()

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

 if push_to_hub:
  model.push_to_hub('''ybelkada/dpt-hybrid-midas'''      )
  image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''      )


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
     type=str,
     help='''URL of the original DPT checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=False,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument(
     '''--push_to_hub''',
     action='''store_true''',
 )
 parser.add_argument(
     '''--model_name''',
     default='''dpt-large''',
     type=str,
     help='''Name of the model, in case you\'re pushing to the hub.''',
 )
 parser.add_argument(
     '''--show_prediction''',
     action='''store_true''',
 )

 lowerCAmelCase__											=	parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase					:	List[str]       =  logging.get_logger(__name__)


__lowerCamelCase					:	Tuple       =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__lowerCamelCase					:	List[Any]       =  {
    '''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'''
        ),
    },
}

__lowerCamelCase					:	int       =  {
    '''allenai/longformer-base-4096''': 40_96,
    '''allenai/longformer-large-4096''': 40_96,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
		    list(range(ord("!"	)   , ord("~"	) + 1	)	) + list(range(ord("¡"	)   , ord("¬"	) + 1	)	) + list(range(ord("®"	)   , ord("ÿ"	) + 1	)	)
		)
		SCREAMING_SNAKE_CASE_  :		str				      =    bs[:]
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    0
		for b in range(2**8	):
				if b not in bs:
						bs.append(lowerCAmelCase	)
						cs.append(2**8 + n	)
						n += 1
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    [chr(lowerCAmelCase	) for n in cs]
		return dict(zip(lowerCAmelCase   , lowerCAmelCase	)	)




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    set()
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    char
		return pairs







class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							['input_ids', 'attention_mask']

					def __init__(    self      :       Union[str, Any],_A      :       List[Any],_A      :       Tuple,_A      :       str="replace",_A      :       Optional[int]="<s>",_A      :       Dict="</s>",_A      :       Any="</s>",_A      :       Optional[Any]="<s>",_A      :       Union[str, Any]="<unk>",_A      :       int="<pad>",_A      :       Dict="<mask>",_A      :       int=False,**_A      :       Dict,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else bos_token
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else eos_token
							SCREAMING_SNAKE_CASE_  :		str				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else sep_token
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else cls_token
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else unk_token
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_  :		Dict				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else mask_token

							super().__init__(
							    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,)

							with open(_A,encoding="utf-8"    ) as vocab_handle:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    json.load(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {v: k for k, v in self.encoder.items()}
							SCREAMING_SNAKE_CASE_  :		Any				      =    errors  # how to handle errors in decoding
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    bytes_to_unicode()
							SCREAMING_SNAKE_CASE_  :		str				      =    {v: k for k, v in self.byte_encoder.items()}
							with open(_A,encoding="utf-8"    ) as merges_handle:
									SCREAMING_SNAKE_CASE_  :		int				      =    merges_handle.read().split("\n"    )[1:-1]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(merge.split()    ) for merge in bpe_merges]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    dict(zip(_A,range(len(_A    )    )    )    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    {}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"    )

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							return len(self.encoder    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return dict(self.encoder,**self.added_tokens_encoder    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       int    ):







							"""simple docstring"""






							if token in self.cache:
									return self.cache[token]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tuple(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    get_pairs(_A    )

							if not pairs:
									return token

							while True:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    min(_A,key=lambda _A    : self.bpe_ranks.get(_A,float("inf"    )    )    )
									if bigram not in self.bpe_ranks:
											break
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    bigram
									SCREAMING_SNAKE_CASE_  :		int				      =    []
									SCREAMING_SNAKE_CASE_  :		Dict				      =    0
									while i < len(_A    ):
											try:
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    word.index(_A,_A    )
											except ValueError:
													new_word.extend(word[i:]    )
													break
											else:
													new_word.extend(word[i:j]    )
													SCREAMING_SNAKE_CASE_  :		str				      =    j

											if word[i] == first and i < len(_A    ) - 1 and word[i + 1] == second:
													new_word.append(first + second    )
													i += 2
											else:
													new_word.append(word[i]    )
													i += 1
									SCREAMING_SNAKE_CASE_  :		Dict				      =    tuple(_A    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    new_word
									if len(_A    ) == 1:
											break
									else:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    get_pairs(_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    " ".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    word
							return word

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    []
							for token in re.findall(self.pat,_A    ):
									SCREAMING_SNAKE_CASE_  :		Any				      =    "".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A    ).split(" "    )    )
							return bpe_tokens

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str    ):







							"""simple docstring"""






							return self.encoder.get(_A,self.encoder.get(self.unk_token    )    )

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       str    ):







							"""simple docstring"""






							return self.decoder.get(_A    )

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8",errors=self.errors    )
							return text

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






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

							with open(_A,"w",encoding="utf-8"    ) as f:
									f.write(json.dumps(self.encoder,indent=2,sort_keys=_A,ensure_ascii=_A    ) + "\n"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
							with open(_A,"w",encoding="utf-8"    ) as writer:
									writer.write("#version: 0.2\n"    )
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),key=lambda _A    : kv[1]    ):
											if index != token_index:
													logger.warning(
													    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
													    " Please check that the tokenizer is not corrupted!"    )
													SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    token_index
											writer.write(" ".join(_A    ) + "\n"    )
											index += 1

							return vocab_file, merge_file

					def 				__UpperCamelCase   (    self      :       Optional[Any],_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]
							SCREAMING_SNAKE_CASE_  :		str				      =    [self.cls_token_id]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None,_A      :       bool = False    ):







							"""simple docstring"""






							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_A,token_ids_a=_A,already_has_special_tokens=_A    )

							if token_ids_a is None:
									return [1] + ([0] * len(_A    )) + [1]
							return [1] + ([0] * len(_A    )) + [1, 1] + ([0] * len(_A    )) + [1]

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [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]






					def 				__UpperCamelCase   (    self      :       Any,_A      :       Union[str, Any],_A      :       Any=False,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    kwargs.pop("add_prefix_space",self.add_prefix_space    )
							if (is_split_into_words or add_prefix_space) and (len(_A    ) > 0 and not text[0].isspace()):
									SCREAMING_SNAKE_CASE_  :		str				      =    " " + text
							return (text, kwargs)
