

'''simple docstring'''
import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowerCamelCase      					=     2
class 					_snake_case		:






			def __init__(  self			,*,  # begin keyword-only arguments
			    _snake_case="<s>"			,_snake_case="<pad>"			,_snake_case="</s>"			,_snake_case="<unk>"			,_snake_case=None			,):
				UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  bos, unk, pad, eos
				UpperCAmelCase_     :      Dict						=  []
				UpperCAmelCase_     :      Dict						=  []
				UpperCAmelCase_     :      Dict						=  {}
				UpperCAmelCase_     :      Optional[int]						=  self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Tuple						=  self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Dict						=  self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  self.add_symbol(_snake_case     )
				if extra_special_symbols:
					for s in extra_special_symbols:
						self.add_symbol(_snake_case     )
				UpperCAmelCase_     :      Tuple						=  len(self.symbols     )






			def __eq__(  self			,_snake_case     ):
				return self.indices == other.indices






			def __getitem__(  self			,_snake_case     ):
				if idx < len(self.symbols     ):
					return self.symbols[idx]
				return self.unk_word






			def __len__(  self     ):
				return len(self.symbols     )






			def __contains__(  self			,_snake_case     ):
				return sym in self.indices






			@classmethod
			def 		UpperCamelCase__							(  cls			,_snake_case     ):
				UpperCAmelCase_     :      Dict						=  cls()
				d.add_from_file(_snake_case     )
				return d






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=1			,_snake_case=False     ):
				if word in self.indices and not overwrite:
					UpperCAmelCase_     :      Union[str, Any]						=  self.indices[word]
					UpperCAmelCase_     :      Optional[int]						=  self.count[idx] + n
					return idx
				else:
					UpperCAmelCase_     :      List[Any]						=  len(self.symbols     )
					UpperCAmelCase_     :      int						=  idx
					self.symbols.append(_snake_case     )
					self.count.append(_snake_case     )
					return idx






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return 0






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				if isinstance(_snake_case			,_snake_case     ):
					try:
						with open(_snake_case			,"r"			,encoding="utf-8"     ) as fd:
							self.add_from_file(_snake_case     )
					except FileNotFoundError as fnfe:
						raise fnfe
					except UnicodeError:
						raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(_snake_case     )     )
					return

				UpperCAmelCase_     :      Optional[int]						=  f.readlines()
				UpperCAmelCase_     :      Dict						=  self._load_meta(_snake_case     )

				for line in lines[indices_start_line:]:
					try:
						UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  line.rstrip().rsplit(" "			,1     )
						if field == "#fairseq:overwrite":
							UpperCAmelCase_     :      List[Any]						=  True
							UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  line.rsplit(" "			,1     )
						else:
							UpperCAmelCase_     :      Any						=  False
						UpperCAmelCase_     :      Optional[Any]						=  int(_snake_case     )
						UpperCAmelCase_     :      Any						=  line
						if word in self and not overwrite:
							raise RuntimeError(
							    "Duplicate word found when loading Dictionary: '{}'. "
							    "Duplicate words can overwrite earlier ones by adding the "
							    "#fairseq:overwrite flag at the end of the corresponding row "
							    "in the dictionary file. If using the Camembert model, please "
							    "download an updated copy of the model file.".format(_snake_case     )     )
						self.add_symbol(_snake_case			,n=_snake_case			,overwrite=_snake_case     )
					except ValueError:
						raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"     )




def  a__       (    _SCREAMING_SNAKE_CASE   :       List[Any]				)     ->					Any:


	"""simple docstring"""





	UpperCAmelCase_     :      Union[str, Any]						=  dict((re.sub(r"@@$"	,				""	,				_SCREAMING_SNAKE_CASE				), v) if k.endswith("@@"				) else (re.sub(r"$"	,				"</w>"	,				_SCREAMING_SNAKE_CASE				), v) for k, v in d.items()				)
	UpperCAmelCase_     :      Any						=  "<s> <pad> </s> <unk>".split()
	# restore the special tokens
	for k in keep_keys:
		del da[F'''{k}</w>''']
		UpperCAmelCase_     :      int						=  d[k]  # restore
	return da


def  a__       (    _SCREAMING_SNAKE_CASE   :       List[str]	,				_SCREAMING_SNAKE_CASE   :       Union[str, Any]				)     ->					Union[str, Any]:


	"""simple docstring"""





	if not os.path.exists(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''				)
	os.makedirs(_SCREAMING_SNAKE_CASE	,				exist_ok=_SCREAMING_SNAKE_CASE				)
	print(F'''Writing results to {pytorch_dump_folder_path}'''				)

	# handle various types of models

	UpperCAmelCase_     :      str						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"checkpoint.pt"				)
	if not os.path.isfile(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''				)
	UpperCAmelCase_     :      Tuple						=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location="cpu"				)

	UpperCAmelCase_     :      Optional[Any]						=  chkpt["cfg"]["model"]

	# dicts
	UpperCAmelCase_     :      Optional[Any]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"dict.txt"				)
	if not os.path.isfile(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path to the file {dict_file} does not exist!'''				)
	UpperCAmelCase_     :      Optional[int]						=  Dictionary.load(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Tuple						=  rewrite_dict_keys(src_dict.indices				)
	UpperCAmelCase_     :      List[Any]						=  len(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Optional[int]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				VOCAB_FILES_NAMES["vocab_file"]				)
	print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''				)
	with open(_SCREAMING_SNAKE_CASE	,				"w"	,				encoding="utf-8"				) as f:
		f.write(json.dumps(_SCREAMING_SNAKE_CASE	,				ensure_ascii=_SCREAMING_SNAKE_CASE	,				indent=_SCREAMING_SNAKE_CASE				)				)

	# merges_file (bpecodes)
	UpperCAmelCase_     :      Optional[Any]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"bpecodes"				)
	if not os.path.isfile(_SCREAMING_SNAKE_CASE				):
		raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''				)

	UpperCAmelCase_     :      Optional[int]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				VOCAB_FILES_NAMES["merges_file"]				)
	shutil.copyfile(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	# model config
	UpperCAmelCase_     :      str						=  os.path.join(_SCREAMING_SNAKE_CASE	,				"config.json"				)

	UpperCAmelCase_     :      List[str]						=  {
	    "activation_dropout": args["activation_dropout"],
	    "architectures": ["BioGptForCausalLM"],
	    "attention_probs_dropout_prob": args["attention_dropout"],
	    "bos_token_id": 0,
	    "eos_token_id": 2,
	    "hidden_act": args["activation_fn"],
	    "hidden_dropout_prob": args["dropout"],
	    "hidden_size": args["decoder_embed_dim"],
	    "initializer_range": 0.02,
	    "intermediate_size": args["decoder_ffn_embed_dim"],
	    "layer_norm_eps": 1E-12,
	    "layerdrop": args["decoder_layerdrop"],
	    "max_position_embeddings": args["max_target_positions"],
	    "model_type": "biogpt",
	    "num_attention_heads": args["decoder_attention_heads"],
	    "num_hidden_layers": args["decoder_layers"],
	    "pad_token_id": 1,
	    "scale_embedding": not args["no_scale_embedding"],
	    "tie_word_embeddings": args["share_decoder_input_output_embed"],
	    "vocab_size": src_vocab_size,
	}

	# good hparam defaults to start with

	print(F'''Generating {biogpt_model_config_file}'''				)
	with open(_SCREAMING_SNAKE_CASE	,				"w"	,				encoding="utf-8"				) as f:
		f.write(json.dumps(_SCREAMING_SNAKE_CASE	,				ensure_ascii=_SCREAMING_SNAKE_CASE	,				indent=_SCREAMING_SNAKE_CASE				)				)

	# tokenizer config
	UpperCAmelCase_     :      Optional[int]						=  os.path.join(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Any						=  {
	    "bos_token": "<s>",
	    "eos_token": "</s>",
	    "model_max_length": 10_24,
	    "pad_token": "<pad>",
	    "special_tokens_map_file": None,
	    "tokenizer_class": "BioGptTokenizer",
	    "unk_token": "<unk>",
	}

	print(F'''Generating {biogpt_tokenizer_config_file}'''				)
	with open(_SCREAMING_SNAKE_CASE	,				"w"	,				encoding="utf-8"				) as f:
		f.write(json.dumps(_SCREAMING_SNAKE_CASE	,				ensure_ascii=_SCREAMING_SNAKE_CASE	,				indent=_SCREAMING_SNAKE_CASE				)				)

	# model
	UpperCAmelCase_     :      Union[str, Any]						=  chkpt["model"]

	# remove unneeded keys
	UpperCAmelCase_     :      Dict						=  [
	    "decoder.version",
	]
	for k in ignore_keys:
		model_state_dict.pop(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Tuple						=  list(model_state_dict.keys()				)
	for layer_name in layer_names:
		if layer_name.endswith("output_projection.weight"				):
			UpperCAmelCase_     :      Dict						=  model_state_dict.pop(_SCREAMING_SNAKE_CASE				)
		else:
			UpperCAmelCase_     :      str						=  model_state_dict.pop(_SCREAMING_SNAKE_CASE				)

	UpperCAmelCase_     :      Tuple						=  BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
	UpperCAmelCase_     :      Any						=  BioGptForCausalLM(_SCREAMING_SNAKE_CASE				)

	# check that it loads ok
	model_new.load_state_dict(_SCREAMING_SNAKE_CASE				)

	# save
	UpperCAmelCase_     :      int						=  os.path.join(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)
	print(F'''Generating {pytorch_weights_dump_path}'''				)
	torch.save(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE				)

	print("Conversion is done!"				)


if __name__ == "__main__":
					_lowerCamelCase      					=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--biogpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
					        """ bpecodes, etc."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_lowerCamelCase      					=     parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""






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

import regex as re

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


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

_lowercase							=				logging.get_logger(__name__)


_lowercase							=				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowercase							=				{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

_lowercase							=				{'''facebook/blenderbot-3B''': 128}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowerCAmelCase__			(		)						->Any:
			__lowercase       =       (
			    list(range(ord("!"      )			,   ord("~"      ) + 1      )      ) + list(range(ord("¡"      )			,   ord("¬"      ) + 1      )      ) + list(range(ord("®"      )			,   ord("ÿ"      ) + 1      )      )
			)
			__lowercase       =       bs[:]
			__lowercase       =       0
			for b in range(2**8      ):
						if b not in bs:
									bs.append(__magic_name__      )
									cs.append(2**8 + n      )
									n += 1
			__lowercase       =       [chr(__magic_name__      ) for n in cs]
			return dict(zip(__magic_name__			,   __magic_name__      )      )



def      lowerCAmelCase__			(		__magic_name__      )						->Tuple:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[str]          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			Tuple          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			int          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase					:			Dict          =   ["""input_ids""", """attention_mask"""]
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="replace"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							int:




										'''simple docstring'''
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else bos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else eos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else sep_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else cls_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else unk_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else pad_token

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

										super().__init__(
										    errors=_lowerCamelCase  ,    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    add_prefix_space=_lowerCamelCase  ,    **_lowerCamelCase  ,    )

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

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										__lowercase       =       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"       )
							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       get_pairs(_lowerCamelCase       )

										if not pairs:
													return token

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

																if word[i] == first and i < len(_lowerCamelCase       ) - 1 and word[i + 1] == second:
																			new_word.append(first + second       )
																			i += 2
																else:
																			new_word.append(word[i]       )
																			i += 1
													__lowercase       =       tuple(_lowerCamelCase       )
													__lowercase       =       new_word
													if len(_lowerCamelCase       ) == 1:
																break
													else:
																__lowercase       =       get_pairs(_lowerCamelCase       )
										__lowercase       =       " ".join(_lowerCamelCase       )
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       []
										for token in re.findall(self.pat  ,    _lowerCamelCase       ):
													__lowercase       =       "".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(_lowerCamelCase       ).split(" "       )       )
										return bpe_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       "".join(_lowerCamelCase       )
										__lowercase       =       bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"  ,    errors=self.errors       )
										return text
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

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

										__lowercase       =       0
										with open(_lowerCamelCase  ,    "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 _lowerCamelCase       : 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!"       )
																			__lowercase       =       token_index
																writer.write(" ".join(_lowerCamelCase       ) + "\n"       )
																index += 1

										return vocab_file, merge_file
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowerCamelCase  ,    token_ids_a=_lowerCamelCase  ,    already_has_special_tokens=_lowerCamelCase       )

										if token_ids_a is None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=False  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       kwargs.pop("add_prefix_space"  ,    self.add_prefix_space       )
										if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase       ) > 0 and not text[0].isspace()):
													__lowercase       =       " " + text
										return (text, kwargs)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple:




										'''simple docstring'''
										return token_ids_a + [self.eos_token_id]





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       []
										for is_user, text in conversation.iter_texts():
													if is_user:
																# We need to space prefix as it's being done within blenderbot
																inputs.append(" " + text       )
													else:
																# Generated responses should contain them already.
																inputs.append(_lowerCamelCase       )

										__lowercase       =       "  ".join(_lowerCamelCase       )
										__lowercase       =       self.encode(_lowerCamelCase       )
										if len(_lowerCamelCase       ) > self.model_max_length:
													__lowercase       =       input_ids[-self.model_max_length :]
													logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''       )
										return input_ids





