


"""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

__a			 =   logging.get_logger(__name__)

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

__a			 =   {
    "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"
        ),
    },
}

__a			 =   {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

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






def 					a__ (       ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    set()
						UpperCAmelCase__		=    word[0]
						for char in word[1:]:
												pairs.add((prev_char, char) )
												UpperCAmelCase__		=    char
						return pairs


class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE										=      PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE										=      ["""input_ids""", """attention_mask""", """token_type_ids"""]






							def __init__(						self :     Optional[Any]		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     List[Any]		, lowerCamelCase_ :     str="replace"		, lowerCamelCase_ :     List[str]="[CLS]"		, lowerCamelCase_ :     List[str]="[SEP]"		, lowerCamelCase_ :     Optional[int]="[SEP]"		, lowerCamelCase_ :     str="[CLS]"		, lowerCamelCase_ :     Optional[Any]="[UNK]"		, lowerCamelCase_ :     int="[PAD]"		, lowerCamelCase_ :     int="[MASK]"		, lowerCamelCase_ :     Optional[Any]=False		, lowerCamelCase_ :     Tuple=False		, **lowerCamelCase_ :     Union[str, Any]		, ):
													UpperCAmelCase__		=    AddedToken(lowerCamelCase_		, lstrip=lowerCamelCase_		, rstrip=lowerCamelCase_				) if isinstance(lowerCamelCase_		, lowerCamelCase_				) else bos_token
													UpperCAmelCase__		=    AddedToken(lowerCamelCase_		, lstrip=lowerCamelCase_		, rstrip=lowerCamelCase_				) if isinstance(lowerCamelCase_		, lowerCamelCase_				) else eos_token
													UpperCAmelCase__		=    AddedToken(lowerCamelCase_		, lstrip=lowerCamelCase_		, rstrip=lowerCamelCase_				) if isinstance(lowerCamelCase_		, lowerCamelCase_				) else sep_token
													UpperCAmelCase__		=    AddedToken(lowerCamelCase_		, lstrip=lowerCamelCase_		, rstrip=lowerCamelCase_				) if isinstance(lowerCamelCase_		, lowerCamelCase_				) else cls_token
													UpperCAmelCase__		=    AddedToken(lowerCamelCase_		, lstrip=lowerCamelCase_		, rstrip=lowerCamelCase_				) if isinstance(lowerCamelCase_		, lowerCamelCase_				) else unk_token
													UpperCAmelCase__		=    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
													UpperCAmelCase__		=    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_		, add_bos_token=lowerCamelCase_		, **lowerCamelCase_		, )
													UpperCAmelCase__		=    add_bos_token

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

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													UpperCAmelCase__		=    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 					lowerCAmelCase     (						self :     Optional[int]				):
													return len(self.encoder				)






							def 					lowerCAmelCase     (						self :     str				):
													return dict(self.encoder		, **self.added_tokens_encoder				)






							def 					lowerCAmelCase     (						self :     Optional[int]		, lowerCamelCase_ :     Any				):
													if token in self.cache:
																			return self.cache[token]
													UpperCAmelCase__		=    tuple(lowerCamelCase_				)
													UpperCAmelCase__		=    get_pairs(lowerCamelCase_				)

													if not pairs:
																			return token

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






							def 					lowerCAmelCase     (						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]
													UpperCAmelCase__		=    [self.cls_token_id]
													UpperCAmelCase__		=    [self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep






							def 					lowerCAmelCase     (						self :     Dict		, 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] + ([0] * len(lowerCamelCase_				)) + [1]






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     List[int]		, lowerCamelCase_ :     Optional[List[int]] = None				):
													UpperCAmelCase__		=    [self.sep_token_id]
													UpperCAmelCase__		=    [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 					lowerCAmelCase     (						self :     Optional[int]		, lowerCamelCase_ :     Tuple				):
													UpperCAmelCase__		=    []
													for token in re.findall(self.pat		, lowerCamelCase_				):
																			UpperCAmelCase__		=    ''''''.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 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     int				):
													return self.encoder.get(lowerCamelCase_		, self.encoder.get(self.unk_token				)				)






							def 					lowerCAmelCase     (						self :     Optional[Any]		, lowerCamelCase_ :     str				):
													return self.decoder.get(lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     Union[str, Any]				):
													UpperCAmelCase__		=    ''''''.join(lowerCamelCase_				)
													UpperCAmelCase__		=    bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''		, errors=self.errors				)
													return text






							def 					lowerCAmelCase     (						self :     int		, lowerCamelCase_ :     str		, lowerCamelCase_ :     Optional[str] = None				):
													if not os.path.isdir(lowerCamelCase_				):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																			return
													UpperCAmelCase__		=    os.path.join(
													    lowerCamelCase_		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
													UpperCAmelCase__		=    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'''				)

													UpperCAmelCase__		=    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!'''				)
																															UpperCAmelCase__		=    token_index
																									writer.write(''' '''.join(lowerCamelCase_				) + '''\n'''				)
																									index += 1

													return vocab_file, merge_file






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     Optional[int]		, lowerCamelCase_ :     Union[str, Any]=False		, **lowerCamelCase_ :     List[str]				):
													UpperCAmelCase__		=    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()):
																			UpperCAmelCase__		=    ''' ''' + text
													return (text, kwargs)






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     "Conversation"				):
													UpperCAmelCase__		=    []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(lowerCamelCase_		, add_special_tokens=lowerCamelCase_				) + [self.eos_token_id]				)
													if len(lowerCamelCase_				) > self.model_max_length:
																			UpperCAmelCase__		=    input_ids[-self.model_max_length :]
													return input_ids



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						__a			 =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)