





'''simple docstring'''

from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''MarkupLMFeatureExtractor'''
    UpperCamelCase			   =			('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
    UpperCamelCase			   =			True


    def __call__(		self			,		A=None			,		A=None			,		A=None			,		A=None			,		A=None			,		A = True			,		A = False			,		A = None			,		A = None			,		A = 0			,		A = None			,		A = None			,		A = None			,		A = False			,		A = False			,		A = False			,		A = False			,		A = True			,		A = None			,		**A			,		)		->       BatchEncoding:
        # first, create nodes and xpaths
        if self.parse_html:
            if html_strings is None:
                raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

            if nodes is not None or xpaths is not None or node_labels is not None:
                raise ValueError(
                    """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

            _SCREAMING_SNAKE_CASE     =      self.feature_extractor(A	)
            _SCREAMING_SNAKE_CASE     =      features["""nodes"""]
            _SCREAMING_SNAKE_CASE     =      features["""xpaths"""]
        else:
            if html_strings is not None:
                raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
            if nodes is None or xpaths is None:
                raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
        if questions is not None and self.parse_html:
            if isinstance(A			,		A	):
                _SCREAMING_SNAKE_CASE     =      [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

        _SCREAMING_SNAKE_CASE     =      self.tokenizer(
            text=questions if questions is not None else nodes			,		text_pair=nodes if questions is not None else None			,		xpaths=A			,		node_labels=A			,		add_special_tokens=A			,		padding=A			,		truncation=A			,		max_length=A			,		stride=A			,		pad_to_multiple_of=A			,		return_token_type_ids=A			,		return_attention_mask=A			,		return_overflowing_tokens=A			,		return_special_tokens_mask=A			,		return_offsets_mapping=A			,		return_length=A			,		verbose=A			,		return_tensors=A			,		**A			,		)

        return encoded_inputs


    def       snake_case_(		self			,		*A			,		**A	)		->       int:
        return self.tokenizer.batch_decode(*A			,		**A	)


    def       snake_case_(		self			,		*A			,		**A	)		->       Union[str, Any]:
        return self.tokenizer.decode(*A			,		**A	)





    @property
    def       snake_case_(		self	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      self.tokenizer.model_input_names
        return tokenizer_input_names










'''simple docstring'''

import json
import os
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"""}

lowercase_         =			{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

lowercase_         =			{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

lowercase_         =			{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





def lowerCamelCase						(					)						->Dict:
    _SCREAMING_SNAKE_CASE     =      (
        list(range(ord("""!"""   ) ,	ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   ) ,	ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   ) ,	ord("""ÿ"""   ) + 1   )   )
    )
    _SCREAMING_SNAKE_CASE     =      bs[:]
    _SCREAMING_SNAKE_CASE     =      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     =      [chr(__lowerCamelCase   ) for n in cs]
    return dict(zip(__lowerCamelCase ,	__lowerCamelCase   )   )





def lowerCamelCase						(					__lowerCamelCase			:   Dict   )						->Optional[int]:
    _SCREAMING_SNAKE_CASE     =      set()
    _SCREAMING_SNAKE_CASE     =      word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)   )
        _SCREAMING_SNAKE_CASE     =      char
    return pairs






class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			VOCAB_FILES_NAMES
    UpperCamelCase			   =			PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase			   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase			   =			['''input_ids''', '''attention_mask''', '''token_type_ids''']


    def __init__(		self			,		A			,		A			,		A="replace"			,		A="[CLS]"			,		A="[SEP]"			,		A="[SEP]"			,		A="[CLS]"			,		A="[UNK]"			,		A="[PAD]"			,		A="[MASK]"			,		A=False			,		A=False			,		**A			,		)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      AddedToken(A			,		lstrip=A			,		rstrip=A	) if isinstance(A			,		A	) else bos_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(A			,		lstrip=A			,		rstrip=A	) if isinstance(A			,		A	) else eos_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(A			,		lstrip=A			,		rstrip=A	) if isinstance(A			,		A	) else sep_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(A			,		lstrip=A			,		rstrip=A	) if isinstance(A			,		A	) else cls_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(A			,		lstrip=A			,		rstrip=A	) if isinstance(A			,		A	) else unk_token
        _SCREAMING_SNAKE_CASE     =      AddedToken(A			,		lstrip=A			,		rstrip=A	) if isinstance(A			,		A	) else pad_token

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

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

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

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _SCREAMING_SNAKE_CASE     =      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.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
    def       snake_case_(		self	)		->       List[str]:
        return len(self.encoder	)


    def       snake_case_(		self	)		->       Optional[int]:
        return dict(self.encoder			,		**self.added_tokens_encoder	)


    def       snake_case_(		self			,		A	)		->       Optional[Any]:
        if token in self.cache:
            return self.cache[token]
        _SCREAMING_SNAKE_CASE     =      tuple(A	)
        _SCREAMING_SNAKE_CASE     =      get_pairs(A	)

        if not pairs:
            return token

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

                if word[i] == first and i < len(A	) - 1 and word[i + 1] == second:
                    new_word.append(first + second	)
                    i += 2
                else:
                    new_word.append(word[i]	)
                    i += 1
            _SCREAMING_SNAKE_CASE     =      tuple(A	)
            _SCREAMING_SNAKE_CASE     =      new_word
            if len(A	) == 1:
                break
            else:
                _SCREAMING_SNAKE_CASE     =      get_pairs(A	)
        _SCREAMING_SNAKE_CASE     =      """ """.join(A	)
        _SCREAMING_SNAKE_CASE     =      word
        return word


    def       snake_case_(		self			,		A			,		A = None	)		->       List[int]:
        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _SCREAMING_SNAKE_CASE     =      [self.cls_token_id]
        _SCREAMING_SNAKE_CASE     =      [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep


    def       snake_case_(		self			,		A			,		A = None			,		A = False	)		->       List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=A			,		token_ids_a=A			,		already_has_special_tokens=A	)

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


    def       snake_case_(		self			,		A			,		A = None	)		->       List[int]:
        _SCREAMING_SNAKE_CASE     =      [self.sep_token_id]
        _SCREAMING_SNAKE_CASE     =      [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	) * [0] + len(token_ids_a + sep	) * [1]


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


    def       snake_case_(		self			,		A	)		->       List[Any]:
        return self.encoder.get(A			,		self.encoder.get(self.unk_token	)	)


    def       snake_case_(		self			,		A	)		->       Dict:
        return self.decoder.get(A	)


    def       snake_case_(		self			,		A	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      """""".join(A	)
        _SCREAMING_SNAKE_CASE     =      bytearray([self.byte_decoder[c] for c in text]	).decode("""utf-8"""			,		errors=self.errors	)
        return text


    def       snake_case_(		self			,		A			,		A = None	)		->       Tuple[str]:
        if not os.path.isdir(A	):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
            return
        _SCREAMING_SNAKE_CASE     =      os.path.join(
            A			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)
        _SCREAMING_SNAKE_CASE     =      os.path.join(
            A			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]	)

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

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

        return vocab_file, merge_file


    def       snake_case_(		self			,		A			,		A=False			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      kwargs.pop("""add_prefix_space"""			,		self.add_prefix_space	)
        if (is_split_into_words or add_prefix_space) and (len(A	) > 0 and not text[0].isspace()):
            _SCREAMING_SNAKE_CASE     =      """ """ + text
        return (text, kwargs)





    def       snake_case_(		self			,		A	)		->       List[int]:
        _SCREAMING_SNAKE_CASE     =      []
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(A			,		add_special_tokens=A	) + [self.eos_token_id]	)
        if len(A	) > self.model_max_length:
            _SCREAMING_SNAKE_CASE     =      input_ids[-self.model_max_length :]
        return input_ids



