"""simple docstring"""







import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE									=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE									=						'    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'



class  _lowerCamelCase			(unittest.TestCase     ):







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							tempfile.mkdtemp()
					os.makedirs(os.path.join(self.transformer_dir			,					'models/bert/'						)						)
					_lowercase :    Optional[Any]							=							self.transformer_dir
					shutil.copy(
					    os.path.join(lowerCamelCase_			,					'src/transformers/models/bert/modeling_bert.py'						)			,					os.path.join(self.transformer_dir			,					'models/bert/modeling_bert.py'						)			,					)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    str							=							'src/transformers'
					shutil.rmtree(self.transformer_dir						)







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       str=None						):

					"""simple docstring"""






					_lowercase :    Any							=							comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
					if overwrite_result is not None:
						_lowercase :    Dict							=							comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
					_lowercase :    Optional[Any]							=							black.Mode(target_versions={black.TargetVersion.PYaa}			,					line_length=1_1_9						)
					_lowercase :    List[Any]							=							black.format_str(lowerCamelCase_			,					mode=lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							os.path.join(self.transformer_dir			,					'new_code.py'						)
					with open(lowerCamelCase_			,					'w'			,					newline='\n'						) as f:
						f.write(lowerCamelCase_						)
					if overwrite_result is None:
						self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase_						)						) == 0						)
					else:
						check_copies.is_copy_consistent(f.name			,					overwrite=lowerCamelCase_						)
						with open(lowerCamelCase_			,					'r'						) as f:
							self.assertTrue(f.read()			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					_lowercase :    str							=							check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'						)
					self.assertEqual(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					self.check_copy_consistency(
					    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'			,					'BertLMPredictionHead'			,					REFERENCE_CODE + '\n'			,					)

					# With no empty line at the end
					self.check_copy_consistency(
					    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'			,					'BertLMPredictionHead'			,					lowerCamelCase_			,					)

					# Copy consistency with rename
					self.check_copy_consistency(
					    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'			,					'TestModelLMPredictionHead'			,					re.sub('Bert'			,					'TestModel'			,					lowerCamelCase_						)			,					)

					# Copy consistency with a really long name
					_lowercase :    str							=							'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
					self.check_copy_consistency(
					    F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''			,					F'''{long_class_name}LMPredictionHead'''			,					re.sub('Bert'			,					lowerCamelCase_			,					lowerCamelCase_						)			,					)

					# Copy consistency with overwrite
					self.check_copy_consistency(
					    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'			,					'TestModelLMPredictionHead'			,					lowerCamelCase_			,					overwrite_result=re.sub('Bert'			,					'TestModel'			,					lowerCamelCase_						)			,					)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase :    int							=							check_copies.LOCALIZED_READMES['README_zh-hans.md']

					_lowercase :    Optional[int]							=							(
					    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
					    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
					    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
					    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
					    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
					    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
					    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
					    ' method has been applied to compress GPT2 into'
					    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
					    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
					    ' Multilingual BERT into'
					    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
					    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
					    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
					    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
					    ' Luong, Quoc V. Le, Christopher D. Manning.'
					)
					_lowercase :    int							=							(
					    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
					    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
					    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
					    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
					)
					_lowercase :    Tuple							=							(
					    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
					    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
					    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
					    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
					    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
					    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
					    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
					    ' method has been applied to compress GPT2 into'
					    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
					    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
					    ' Multilingual BERT into'
					    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
					    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
					    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
					    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
					    ' Christopher D. Manning 发布。\n'
					)

					_lowercase :    Union[str, Any]							=							check_copies.convert_to_localized_md(
					    lowerCamelCase_			,					lowerCamelCase_			,					localized_readme['format_model_list']						)

					self.assertFalse(lowerCamelCase_						)
					self.assertEqual(lowerCamelCase_			,					lowerCamelCase_						)

					_lowercase :    List[Any]							=							check_copies.convert_to_localized_md(
					    lowerCamelCase_			,					lowerCamelCase_			,					localized_readme['format_model_list']						)

					# Check whether the number of models is equal to README.md after conversion.
					self.assertTrue(lowerCamelCase_						)

					_lowercase :    Tuple							=							(
					    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
					    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
					    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
					    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
					)
					_lowercase :    Optional[Any]							=							(
					    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
					    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
					    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
					    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
					)
					_lowercase :    Any							=							(
					    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
					    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
					    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
					    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
					)

					_lowercase :    Union[str, Any]							=							check_copies.convert_to_localized_md(
					    lowerCamelCase_			,					lowerCamelCase_			,					localized_readme['format_model_list']						)

					# Check if the model link is synchronized.
					self.assertEqual(lowerCamelCase_			,					lowerCamelCase_						)




"""simple docstring"""







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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE									=						{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

SCREAMING_SNAKE_CASE									=						{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}


@lru_cache()
def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Tuple							=							(
	    list(range(ord('!'			)     ,ord('~'			) + 1			)			) + list(range(ord('¡'			)     ,ord('¬'			) + 1			)			) + list(range(ord('®'			)     ,ord('ÿ'			) + 1			)			)
	)
	_lowercase :    List[Any]							=							bs[:]
	_lowercase :    Optional[Any]							=							0
	for b in range(2**8			):
		if b not in bs:
			bs.append(__UpperCAmelCase			)
			cs.append(2**8 + n			)
			n += 1
	_lowercase :    Dict							=							[chr(__UpperCAmelCase			) for n in cs]
	return dict(zip(__UpperCAmelCase     ,__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							set()
	_lowercase :    int							=							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)			)
		_lowercase :    Union[str, Any]							=							char
	return pairs







class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  VOCAB_FILES_NAMES
				_snake_case     =  PRETRAINED_VOCAB_FILES_MAP
				_snake_case     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case     =  ["input_ids", "attention_mask"]







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

					"""simple docstring"""






					_lowercase :    int							=							AddedToken(lowerCamelCase_			,					lstrip=lowerCamelCase_			,					rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_			,					lowerCamelCase_						) else bos_token
					_lowercase :    int							=							AddedToken(lowerCamelCase_			,					lstrip=lowerCamelCase_			,					rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_			,					lowerCamelCase_						) else eos_token
					_lowercase :    Dict							=							AddedToken(lowerCamelCase_			,					lstrip=lowerCamelCase_			,					rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_			,					lowerCamelCase_						) else sep_token
					_lowercase :    Optional[int]							=							AddedToken(lowerCamelCase_			,					lstrip=lowerCamelCase_			,					rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_			,					lowerCamelCase_						) else cls_token
					_lowercase :    Any							=							AddedToken(lowerCamelCase_			,					lstrip=lowerCamelCase_			,					rstrip=lowerCamelCase_						) if isinstance(lowerCamelCase_			,					lowerCamelCase_						) else unk_token
					_lowercase :    Union[str, 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
					_lowercase :    str							=							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 :    str							=							json.load(lowerCamelCase_						)
					_lowercase :    Tuple							=							{v: k for k, v in self.encoder.items()}
					_lowercase :    Any							=							errors  # how to handle errors in decoding
					_lowercase :    str							=							bytes_to_unicode()
					_lowercase :    Optional[int]							=							{v: k for k, v in self.byte_encoder.items()}
					with open(lowerCamelCase_			,					encoding='utf-8'						) as merges_handle:
						_lowercase :    Union[str, Any]							=							merges_handle.read().split('\n'						)[1:-1]
					_lowercase :    Dict							=							[tuple(merge.split()						) for merge in bpe_merges]
					_lowercase :    Any							=							dict(zip(lowerCamelCase_			,					range(len(lowerCamelCase_						)						)						)						)
					_lowercase :    Tuple							=							{}
					_lowercase :    Optional[Any]							=							add_prefix_space

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					_lowercase :    int							=							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    :       Dict						):

					"""simple docstring"""






					return len(self.encoder						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					if token in self.cache:
						return self.cache[token]
					_lowercase :    str							=							tuple(lowerCamelCase_						)
					_lowercase :    Dict							=							get_pairs(lowerCamelCase_						)

					if not pairs:
						return token

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







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							[]
					for token in re.findall(self.pat			,					lowerCamelCase_						):
						_lowercase :    List[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 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					return self.decoder.get(lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       Any						):

					"""simple docstring"""






					_lowercase :    Dict							=							''.join(lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							bytearray([self.byte_decoder[c] for c in text]						).decode('utf-8'			,					errors=self.errors						)
					return text







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[str] = None						):

					"""simple docstring"""






					if not os.path.isdir(lowerCamelCase_						):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
						return
					_lowercase :    Union[str, Any]							=							os.path.join(
					    lowerCamelCase_			,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
					_lowercase :    List[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'						)

					_lowercase :    Optional[int]							=							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 :    int							=							token_index
							writer.write(' '.join(lowerCamelCase_						) + '\n'						)
							index += 1

					return vocab_file, merge_file







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       List[int]			,					lowerCamelCase_    :       Optional[List[int]] = None						):

					"""simple docstring"""






					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					_lowercase :    Optional[Any]							=							[self.cls_token_id]
					_lowercase :    List[Any]							=							[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       List[int]			,					lowerCamelCase_    :       Optional[List[int]] = None			,					lowerCamelCase_    :       bool = False						):

					"""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 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[int]			,					lowerCamelCase_    :       Optional[List[int]] = None						):

					"""simple docstring"""






					_lowercase :    int							=							[self.sep_token_id]
					_lowercase :    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    :       Tuple			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       str=False			,					**lowerCamelCase_    :       int						):

					"""simple docstring"""






					_lowercase :    Dict							=							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 :    Dict							=							' ' + text
					return (text, kwargs)



