


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

__UpperCAmelCase			:			List[Any]			      =					logging.get_logger(__name__)

__UpperCAmelCase			:			Any			      =					{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

__UpperCAmelCase			:			int			      =					{
    "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"
        ),
    },
}

__UpperCAmelCase			:			List[str]			      =					{
    "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,
}

__UpperCAmelCase			:			List[Any]			      =					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}


def   a						(				):






       """simple docstring"""





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


def   a						(				SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       UpperCamelCase     :						Optional[int] 						=		set()
       UpperCamelCase     :						Any 						=		word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)							)
              UpperCamelCase     :						Optional[Any] 						=		char
       return pairs



class  UpperCAmelCase_						(				A__):
       '''simple docstring'''
       __UpperCamelCase   :  List[str]				=       VOCAB_FILES_NAMES
       __UpperCamelCase   :  Optional[Any]				=       PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase   :  Dict				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase   :  int				=       ["input_ids", "attention_mask", "token_type_ids"]






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE="replace"			,       __SCREAMING_SNAKE_CASE="[CLS]"			,       __SCREAMING_SNAKE_CASE="[SEP]"			,       __SCREAMING_SNAKE_CASE="[SEP]"			,       __SCREAMING_SNAKE_CASE="[CLS]"			,       __SCREAMING_SNAKE_CASE="[UNK]"			,       __SCREAMING_SNAKE_CASE="[PAD]"			,       __SCREAMING_SNAKE_CASE="[MASK]"			,       __SCREAMING_SNAKE_CASE=False			,       __SCREAMING_SNAKE_CASE=False			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		AddedToken(__A			,       lstrip=__A			,       rstrip=__A    ) if isinstance(__A			,       __A    ) else bos_token
              UpperCamelCase     :						Optional[Any] 						=		AddedToken(__A			,       lstrip=__A			,       rstrip=__A    ) if isinstance(__A			,       __A    ) else eos_token
              UpperCamelCase     :						Optional[int] 						=		AddedToken(__A			,       lstrip=__A			,       rstrip=__A    ) if isinstance(__A			,       __A    ) else sep_token
              UpperCamelCase     :						List[str] 						=		AddedToken(__A			,       lstrip=__A			,       rstrip=__A    ) if isinstance(__A			,       __A    ) else cls_token
              UpperCamelCase     :						List[str] 						=		AddedToken(__A			,       lstrip=__A			,       rstrip=__A    ) if isinstance(__A			,       __A    ) else unk_token
              UpperCamelCase     :						Tuple 						=		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
              UpperCamelCase     :						List[str] 						=		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			,       )
              UpperCamelCase     :						Tuple 						=		add_bos_token

              with open(__A			,       encoding='''utf-8'''    ) as vocab_handle:
                     UpperCamelCase     :						Optional[int] 						=		json.load(__A    )
              UpperCamelCase     :						Union[str, Any] 						=		{v: k for k, v in self.encoder.items()}
              UpperCamelCase     :						int 						=		errors  # how to handle errors in decoding
              UpperCamelCase     :						List[Any] 						=		bytes_to_unicode()
              UpperCamelCase     :						Dict 						=		{v: k for k, v in self.byte_encoder.items()}
              with open(__A			,       encoding='''utf-8'''    ) as merges_handle:
                     UpperCamelCase     :						str 						=		merges_handle.read().split('''\n'''    )[1:-1]
              UpperCamelCase     :						int 						=		[tuple(merge.split()    ) for merge in bpe_merges]
              UpperCamelCase     :						str 						=		dict(zip(__A			,       range(len(__A    )    )    )    )
              UpperCamelCase     :						Union[str, Any] 						=		{}
              UpperCamelCase     :						Tuple 						=		add_prefix_space

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




              """simple docstring"""




              return len(self.encoder    )






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if token in self.cache:
                     return self.cache[token]
              UpperCamelCase     :						str 						=		tuple(__A    )
              UpperCamelCase     :						Union[str, Any] 						=		get_pairs(__A    )

              if not pairs:
                     return token

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              UpperCamelCase     :						Tuple 						=		[self.cls_token_id]
              UpperCamelCase     :						int 						=		[self.sep_token_id]
              return cls + token_ids_a + sep + token_ids_a + sep






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = False    ):




              """simple docstring"""




              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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		[self.sep_token_id]
              UpperCamelCase     :						Union[str, 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    ) * [0] + len(token_ids_a + sep    ) * [1]






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		[]
              for token in re.findall(self.pat			,       __A    ):
                     UpperCamelCase     :						Union[str, 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(__A    ).split(''' '''    )    )
              return bpe_tokens






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return self.decoder.get(__A    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		"""""".join(__A    )
              UpperCamelCase     :						Union[str, Any] 						=		bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8'''			,       errors=self.errors    )
              return text






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




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

              UpperCamelCase     :						Dict 						=		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 __SCREAMING_SNAKE_CASE    : 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!'''    )
                                   UpperCamelCase     :						str 						=		token_index
                            writer.write(''' '''.join(__A    ) + '''\n'''    )
                            index += 1

              return vocab_file, merge_file






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=False			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		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()):
                     UpperCamelCase     :						List[Any] 						=		""" """ + text
              return (text, kwargs)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		[]
              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:
                     UpperCamelCase     :						Optional[int] 						=		input_ids[-self.model_max_length :]
              return input_ids



"""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      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Union[str, Any]												=		"MarkupLMFeatureExtractor"
			UpperCAmelCase_    :Optional[Any]												=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
			UpperCAmelCase_    :List[Any]												=		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`"""			)

									lowerCAmelCase_       :Union[str, Any]     	=    self.feature_extractor(__A			)
									lowerCAmelCase_       :Any     	=    features["""nodes"""]
									lowerCAmelCase_       :Union[str, Any]     	=    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			):
												lowerCAmelCase_       :Dict     	=    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

						lowerCAmelCase_       :Optional[Any]     	=    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 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	Tuple:
						return self.tokenizer.batch_decode(*__A			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	str:
						return self.tokenizer.decode(*__A			,  **__A			)






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Any     	=    self.tokenizer.model_input_names
						return tokenizer_input_names






