



'''simple docstring'''




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

import black


UpperCamelCase__       :	Union[str, Any]    = 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.
UpperCamelCase__       :	Optional[Any]    = '''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''






class 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		tempfile.mkdtemp()
					os.makedirs(os.path.join(self.transformer_dir							,     """models/bert/"""						)						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		"""src/transformers"""
					shutil.rmtree(self.transformer_dir						)

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Optional[Any]=None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		comment + F"\nclass {class_name}(nn.Module):\n" + class_code
					if overwrite_result is not None:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
					__SCREAMING_SNAKE_CASE       :		int           =		black.Mode(target_versions={black.TargetVersion.PYaa}							,     line_length=1_1_9						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		black.format_str(lowerCAmelCase__							,     mode=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		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     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""						)
					self.assertEqual(lowerCAmelCase__							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Any						):





					"""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
					__SCREAMING_SNAKE_CASE       :		List[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"""






					__SCREAMING_SNAKE_CASE       :		str           =		check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

					__SCREAMING_SNAKE_CASE       :		Any           =		(
					    """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."""
					)
					__SCREAMING_SNAKE_CASE       :		List[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"""
					)
					__SCREAMING_SNAKE_CASE       :		List[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。\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"""
					)

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Any           =		check_copies.convert_to_localized_md(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     localized_readme["""format_model_list"""]						)

					self.assertFalse(lowerCAmelCase__						)
					self.assertEqual(lowerCAmelCase__							,     lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		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__						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		(
					    """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."""
					)
					__SCREAMING_SNAKE_CASE       :		int           =		(
					    """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"""
					)
					__SCREAMING_SNAKE_CASE       :		List[str]           =		(
					    """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"""
					)

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[str]           =		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 Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


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


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

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase__       :	Tuple    = {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__       :	List[Any]    = {
    '''allenai/led-base-16384''': 1_63_84,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Tuple           =		(
		    list(range(ord("""!"""					)       ,      ord("""~"""					) + 1					)					) + list(range(ord("""¡"""					)       ,      ord("""¬"""					) + 1					)					) + list(range(ord("""®"""					)       ,      ord("""ÿ"""					) + 1					)					)
		)
		__SCREAMING_SNAKE_CASE       :		Any           =		bs[:]
		__SCREAMING_SNAKE_CASE       :		Tuple           =		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       :		Union[str, Any]           =		[chr(_lowerCamelCase					) for n in cs]
		return dict(zip(_lowerCamelCase       ,      _lowerCamelCase					)					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]					):
		__SCREAMING_SNAKE_CASE       :		Dict           =		set()
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)					)
				__SCREAMING_SNAKE_CASE       :		str           =		char
		return pairs






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Union[str, Any]			    =		VOCAB_FILES_NAMES
			_A      :		Any			    =		PRETRAINED_VOCAB_FILES_MAP
			_A      :		Any			    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		Optional[Any]			    =		['''input_ids''', '''attention_mask''']

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





					"""simple docstring"""






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

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					__SCREAMING_SNAKE_CASE       :		str           =		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.bart.tokenization_bart.BartTokenizer.vocab_size
			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					return len(self.encoder						)

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Any						):





					"""simple docstring"""






					if token in self.cache:
							return self.cache[token]
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		tuple(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		get_pairs(lowerCAmelCase__						)

					if not pairs:
							return token

					while True:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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       :		Dict           =		bigram
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		[]
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		0
							while i < len(lowerCAmelCase__						):
									try:
											__SCREAMING_SNAKE_CASE       :		Dict           =		word.index(lowerCAmelCase__							,     lowerCAmelCase__						)
									except ValueError:
											new_word.extend(word[i:]						)
											break
									else:
											new_word.extend(word[i:j]						)
											__SCREAMING_SNAKE_CASE       :		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
							__SCREAMING_SNAKE_CASE       :		Tuple           =		tuple(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Any           =		new_word
							if len(lowerCAmelCase__						) == 1:
									break
							else:
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		get_pairs(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		""" """.join(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		word
					return word

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[]
					for token in re.findall(self.pat							,     lowerCAmelCase__						):
							__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(lowerCAmelCase__						).split(""" """						)						)
					return bpe_tokens

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					return self.decoder.get(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : List[str]							,     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
					__SCREAMING_SNAKE_CASE       :		int           =		os.path.join(
					    lowerCAmelCase__							,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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       :		Tuple           =		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 UpperCamelCase__					(							self     : str							,     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]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[self.cls_token_id]
					__SCREAMING_SNAKE_CASE       :		str           =		[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep

			def UpperCamelCase__					(							self     : 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     : Any							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		[self.sep_token_id]
					__SCREAMING_SNAKE_CASE       :		List[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     : Optional[Any]							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : List[Any]=False							,     **lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, 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       :		int           =		""" """ + text
					return (text, kwargs)



			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Union[Dict[str, EncodedInput], BatchEncoding]							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,     lowerCAmelCase__     : Optional[int] = None							,     lowerCAmelCase__     : Optional[bool] = None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		super()._pad(
					    encoded_inputs=lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     padding_strategy=lowerCAmelCase__							,     pad_to_multiple_of=lowerCAmelCase__							,     return_attention_mask=lowerCAmelCase__							,     )

					# Load from model defaults
					if return_attention_mask is None:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		"""attention_mask""" in self.model_input_names

					if return_attention_mask and "global_attention_mask" in encoded_inputs:
							__SCREAMING_SNAKE_CASE       :		str           =		encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							__SCREAMING_SNAKE_CASE       :		str           =		len(encoded_inputs["""global_attention_mask"""]						) != len(lowerCAmelCase__						)

							if needs_to_be_padded:
									__SCREAMING_SNAKE_CASE       :		Dict           =		len(lowerCAmelCase__						) - len(encoded_inputs["""global_attention_mask"""]						)

									if self.padding_side == "right":
											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
											__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(
											    encoded_inputs["""global_attention_mask"""] + [-1] * difference
											)
									elif self.padding_side == "left":
											__SCREAMING_SNAKE_CASE       :		Dict           =		[-1] * difference + encoded_inputs[
											    """global_attention_mask"""
											]
									else:
											raise ValueError("""Invalid padding strategy:""" + str(self.padding_side						)						)

					return encoded_inputs