





"""simple docstring"""
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCamelCase_								=		"""true"""



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Dict=82 ,   a_     :		List[str]=16							)			->      int:
      set_seed(42							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  RegressionModel()
      __SCREAMING_SNAKE_CASE   :List[str]						=  deepcopy(_UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  RegressionDataset(length=_UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :Tuple						=  DataLoader(_UpperCAmelCase ,   batch_size=_UpperCAmelCase							)
      model.to(accelerator.device							)
      __SCREAMING_SNAKE_CASE   :int						=  accelerator.prepare(_UpperCAmelCase ,   _UpperCAmelCase							)
      return model, ddp_model, dataloader



def      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		Optional[int]=False							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :Dict						=  AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''							)
      __SCREAMING_SNAKE_CASE   :int						=  load_dataset('''glue''' ,   '''mrpc''' ,   split='''validation'''							)

      def tokenize_function(a_     :		Tuple							):
            __SCREAMING_SNAKE_CASE   :int						=  tokenizer(examples['''sentence1'''] ,   examples['''sentence2'''] ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase							)
            return outputs

      with accelerator.main_process_first():
            __SCREAMING_SNAKE_CASE   :int						=  dataset.map(
                _UpperCAmelCase ,   batched=_UpperCAmelCase ,   remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,   )

      __SCREAMING_SNAKE_CASE   :Dict						=  tokenized_datasets.rename_column('''label''' ,   '''labels'''							)

      def collate_fn(a_     :		Any							):
            if use_longest:
                  return tokenizer.pad(_UpperCAmelCase ,   padding='''longest''' ,   return_tensors='''pt'''							)
            return tokenizer.pad(_UpperCAmelCase ,   padding='''max_length''' ,   max_length=1_28 ,   return_tensors='''pt'''							)

      return DataLoader(_UpperCAmelCase ,   shuffle=_UpperCAmelCase ,   collate_fn=_UpperCAmelCase ,   batch_size=16							)



def      __lowerCamelCase   (							a_     :		Tuple ,   a_     :		Any							)			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  Accelerator(dispatch_batches=_UpperCAmelCase ,   split_batches=_UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :Dict						=  get_dataloader(_UpperCAmelCase ,   not dispatch_batches							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  AutoModelForSequenceClassification.from_pretrained(
          '''hf-internal-testing/mrpc-bert-base-cased''' ,   return_dict=_UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :str						=  accelerator.prepare(_UpperCAmelCase ,   _UpperCAmelCase							)
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def      __lowerCamelCase   (							a_     :		Optional[int] ,   a_     :		Optional[Any] ,   a_     :		Dict							)			->      int:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      for batch in dataloader:
            __SCREAMING_SNAKE_CASE   :Any						=  batch.values()
            with torch.no_grad():
                  __SCREAMING_SNAKE_CASE   :Optional[int]						=  model(_UpperCAmelCase							)
                  __SCREAMING_SNAKE_CASE   :Any						=  accelerator.gather_for_metrics((logit, target)							)
                  logits_and_targets.append((logit, target)							)
      __SCREAMING_SNAKE_CASE   :Any						=  [], []
      for logit, targ in logits_and_targets:
            logits.append(_UpperCAmelCase							)
            targs.append(_UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :str						=  torch.cat(_UpperCAmelCase							), torch.cat(_UpperCAmelCase							)
      return logits, targs



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		str=82 ,   a_     :		str=False ,   a_     :		Optional[int]=False ,   a_     :		Dict=16							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  get_basic_setup(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  generate_predictions(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase							)
      assert (
          len(_UpperCAmelCase							) == num_samples
      ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCAmelCase							)}'''



def      __lowerCamelCase   (							a_     :		Optional[Any] = False ,   a_     :		int = False							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :int						=  evaluate.load('''glue''' ,   '''mrpc'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  get_mrpc_setup(_UpperCAmelCase ,   _UpperCAmelCase							)
      # First do baseline
      __SCREAMING_SNAKE_CASE   :int						=  setup["no"]
      model.to(_UpperCAmelCase							)
      model.eval()
      for batch in dataloader:
            batch.to(_UpperCAmelCase							)
            with torch.inference_mode():
                  __SCREAMING_SNAKE_CASE   :Optional[Any]						=  model(**_UpperCAmelCase							)
            __SCREAMING_SNAKE_CASE   :List[str]						=  outputs.logits.argmax(dim=-1							)
            metric.add_batch(predictions=_UpperCAmelCase ,   references=batch['''labels''']							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  metric.compute()

      # Then do distributed
      __SCREAMING_SNAKE_CASE   :str						=  setup["ddp"]
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  model(**_UpperCAmelCase							)
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  outputs.logits.argmax(dim=-1							)
            __SCREAMING_SNAKE_CASE   :Any						=  batch["labels"]
            __SCREAMING_SNAKE_CASE   :str						=  accelerator.gather_for_metrics((preds, references)							)
            metric.add_batch(predictions=_UpperCAmelCase ,   references=_UpperCAmelCase							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key] ,   distributed[key]							), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''



def      __lowerCamelCase   (							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :Tuple						=  Accelerator(split_batches=_UpperCAmelCase ,   dispatch_batches=_UpperCAmelCase							)
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print('''**Testing gather_for_metrics**'''							)
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''							)
                        test_mrpc(_UpperCAmelCase ,   _UpperCAmelCase							)
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('''**Test torch metrics**'''							)
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  __SCREAMING_SNAKE_CASE   :int						=  Accelerator(split_batches=_UpperCAmelCase ,   dispatch_batches=_UpperCAmelCase							)
                  if accelerator.is_local_main_process:
                        print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''							)
                  test_torch_metrics(_UpperCAmelCase ,   99							)
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('''**Test last batch is not dropped when perfectly divisible**'''							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  Accelerator()
      test_torch_metrics(_UpperCAmelCase ,   5_12							)
      accelerator.state._reset_state()



def      __lowerCamelCase   (							a_     :		Tuple							)			->      List[str]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()


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							:  Any   	=						logging.get_logger(__name__)


lowerCAmelCase							:  Tuple   	=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

lowerCAmelCase							:  Optional[int]   	=						{
    """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							:  Optional[Any]   	=						{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}







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







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: str  		=       set()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)    )
												SCREAMING_SNAKE_CASE_: Tuple  		=       char
						return pairs



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							Dict										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							str										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase   :							List[str]										=     ['''input_ids''', '''attention_mask''']



			def __init__( self  :     str   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]="replace"   ,						lowerCAmelCase__  :     Optional[Any]="<s>"   ,						lowerCAmelCase__  :     int="</s>"   ,						lowerCAmelCase__  :     Optional[Any]="</s>"   ,						lowerCAmelCase__  :     int="<s>"   ,						lowerCAmelCase__  :     Optional[Any]="<unk>"   ,						lowerCAmelCase__  :     List[Any]="<pad>"   ,						lowerCAmelCase__  :     Any="<mask>"   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						**lowerCAmelCase__  :     Tuple   ,						):
									SCREAMING_SNAKE_CASE_: int  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_: str  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_: int  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else unk_token
									SCREAMING_SNAKE_CASE_: Any  		=       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
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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:
															SCREAMING_SNAKE_CASE_: Tuple  		=       json.load(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       {v: k for k, v in self.encoder.items()}
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       errors  # how to handle errors in decoding
									SCREAMING_SNAKE_CASE_: List[Any]  		=       bytes_to_unicode()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {v: k for k, v in self.byte_encoder.items()}
									with open(lowerCAmelCase__   ,						encoding="utf-8") as merges_handle:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       merges_handle.read().split("\n")[1:-1]
									SCREAMING_SNAKE_CASE_: str  		=       [tuple(merge.split()) for merge in bpe_merges]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       dict(zip(lowerCAmelCase__   ,						range(len(lowerCAmelCase__))))
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     int):
									return len(self.encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return dict(self.encoder   ,						**self.added_tokens_encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									if token in self.cache:
															return self.cache[token]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       tuple(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_pairs(lowerCAmelCase__)

									if not pairs:
															return token

									while True:
															SCREAMING_SNAKE_CASE_: int  		=       min(lowerCAmelCase__   ,						key=lambda lowerCAmelCase__: self.bpe_ranks.get(lowerCAmelCase__   ,						float("inf")))
															if bigram not in self.bpe_ranks:
																					break
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       bigram
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       []
															SCREAMING_SNAKE_CASE_: List[Any]  		=       0
															while i < len(lowerCAmelCase__):
																					try:
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       word.index(lowerCAmelCase__   ,						lowerCAmelCase__)
																					except ValueError:
																											new_word.extend(word[i:])
																											break
																					else:
																											new_word.extend(word[i:j])
																											SCREAMING_SNAKE_CASE_: Tuple  		=       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
															SCREAMING_SNAKE_CASE_: str  		=       tuple(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: int  		=       new_word
															if len(lowerCAmelCase__) == 1:
																					break
															else:
																					SCREAMING_SNAKE_CASE_: Dict  		=       get_pairs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       " ".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       word
									return word



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
									for token in re.findall(self.pat   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: str  		=       "".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  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return self.encoder.get(lowerCAmelCase__   ,						self.encoder.get(self.unk_token))



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Union[str, Any]):
									return self.decoder.get(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Any  		=       "".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"   ,						errors=self.errors)
									return text



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = None):
									if not os.path.isdir(lowerCAmelCase__):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_: Any  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_: Any  		=       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")

									SCREAMING_SNAKE_CASE_: List[Any]  		=       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!")
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       token_index
																					writer.write(" ".join(lowerCAmelCase__) + "\n")
																					index += 1

									return vocab_file, merge_file



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [self.cls_token_id]
									SCREAMING_SNAKE_CASE_: Dict  		=       [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None   ,						lowerCAmelCase__  :     bool = False):
									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  :     Optional[int]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: 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 + sep + token_ids_a + sep) * [0]




			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[str]=False   ,						**lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       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()):
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       " " + text
									return (text, kwargs)






