






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

__lowerCamelCase						= logging.get_logger(__name__)

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

__lowerCamelCase						= {
    '''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'''
        ),
    },
}

__lowerCamelCase						= {
    '''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,
}

__lowerCamelCase						= {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}






def    UpperCamelCase__  (   )     ->		List[str]:







							"""simple docstring"""





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






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		str:







							"""simple docstring"""





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







class      UpperCamelCase_							(				lowerCAmelCase__       ):
						lowercase									=  VOCAB_FILES_NAMES
						lowercase									=  PRETRAINED_VOCAB_FILES_MAP
						lowercase									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase									=  ['input_ids', 'attention_mask', 'token_type_ids']




						def __init__(    self		,	lowercase		,	lowercase		,	lowercase="replace"		,	lowercase="[CLS]"		,	lowercase="[SEP]"		,	lowercase="[SEP]"		,	lowercase="[CLS]"		,	lowercase="[UNK]"		,	lowercase="[PAD]"		,	lowercase="[MASK]"		,	lowercase=False		,	lowercase=False		,	**lowercase		,	)			->						List[str]:
													_a  :    List[Any]			=						AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__       ) if isinstance(lowercase__		,	lowercase__       ) else bos_token
													_a  :    Union[str, Any]			=						AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__       ) if isinstance(lowercase__		,	lowercase__       ) else eos_token
													_a  :    int			=						AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__       ) if isinstance(lowercase__		,	lowercase__       ) else sep_token
													_a  :    Dict			=						AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__       ) if isinstance(lowercase__		,	lowercase__       ) else cls_token
													_a  :    List[str]			=						AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__       ) if isinstance(lowercase__		,	lowercase__       ) else unk_token
													_a  :    int			=						AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__       ) if isinstance(lowercase__		,	lowercase__       ) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													_a  :    Union[str, Any]			=						AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__       ) if isinstance(lowercase__		,	lowercase__       ) else mask_token

													super().__init__(
													    errors=lowercase__		,	bos_token=lowercase__		,	eos_token=lowercase__		,	unk_token=lowercase__		,	sep_token=lowercase__		,	cls_token=lowercase__		,	pad_token=lowercase__		,	mask_token=lowercase__		,	add_prefix_space=lowercase__		,	add_bos_token=lowercase__		,	**lowercase__		,	)
													_a  :    Optional[int]			=						add_bos_token

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

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													_a  :    Union[str, Any]			=						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       )			->						int:
													return len(self.encoder       )




						def      snake_case__(    self       )			->						Dict:
													return dict(self.encoder		,	**self.added_tokens_encoder       )




						def      snake_case__(    self		,	lowercase       )			->						Tuple:
													if token in self.cache:
																				return self.cache[token]
													_a  :    Any			=						tuple(lowercase__       )
													_a  :    Optional[Any]			=						get_pairs(lowercase__       )

													if not pairs:
																				return token

													while True:
																				_a  :    Dict			=						min(lowercase__		,	key=lambda lowercase       : self.bpe_ranks.get(lowercase__		,	float('''inf'''       )       )       )
																				if bigram not in self.bpe_ranks:
																											break
																				_a		,					_a  :    Optional[int]			=						bigram
																				_a  :    Optional[int]			=						[]
																				_a  :    Any			=						0
																				while i < len(lowercase__       ):
																											try:
																																		_a  :    List[Any]			=						word.index(lowercase__		,	lowercase__       )
																											except ValueError:
																																		new_word.extend(word[i:]       )
																																		break
																											else:
																																		new_word.extend(word[i:j]       )
																																		_a  :    List[Any]			=						j

																											if word[i] == first and i < len(lowercase__       ) - 1 and word[i + 1] == second:
																																		new_word.append(first + second       )
																																		i += 2
																											else:
																																		new_word.append(word[i]       )
																																		i += 1
																				_a  :    Tuple			=						tuple(lowercase__       )
																				_a  :    str			=						new_word
																				if len(lowercase__       ) == 1:
																											break
																				else:
																											_a  :    Dict			=						get_pairs(lowercase__       )
													_a  :    Any			=						''' '''.join(lowercase__       )
													_a  :    Optional[int]			=						word
													return word




						def      snake_case__(    self		,	lowercase		,	lowercase = None       )			->						List[int]:
													if token_ids_a is None:
																				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													_a  :    Optional[int]			=						[self.cls_token_id]
													_a  :    Union[str, Any]			=						[self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep




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

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




						def      snake_case__(    self		,	lowercase		,	lowercase = None       )			->						List[int]:
													_a  :    Optional[Any]			=						[self.sep_token_id]
													_a  :    str			=						[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		,	lowercase       )			->						List[Any]:
													_a  :    Optional[int]			=						[]
													for token in re.findall(self.pat		,	lowercase__       ):
																				_a  :    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(lowercase__       ).split(''' '''       )       )
													return bpe_tokens




						def      snake_case__(    self		,	lowercase       )			->						List[str]:
													return self.encoder.get(lowercase__		,	self.encoder.get(self.unk_token       )       )




						def      snake_case__(    self		,	lowercase       )			->						str:
													return self.decoder.get(lowercase__       )




						def      snake_case__(    self		,	lowercase       )			->						Dict:
													_a  :    Tuple			=						''''''.join(lowercase__       )
													_a  :    List[Any]			=						bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''		,	errors=self.errors       )
													return text




						def      snake_case__(    self		,	lowercase		,	lowercase = None       )			->						Tuple[str]:
													if not os.path.isdir(lowercase__       ):
																				logger.error(F'Vocabulary path ({save_directory}) should be a directory'       )
																				return
													_a  :    Any			=						os.path.join(
													    lowercase__		,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
													_a  :    Union[str, Any]			=						os.path.join(
													    lowercase__		,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

													_a  :    Optional[int]			=						0
													with open(lowercase__		,	'''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 lowercase       : 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!'''       )
																																		_a  :    List[str]			=						token_index
																											writer.write(''' '''.join(lowercase__       ) + '''\n'''       )
																											index += 1

													return vocab_file, merge_file




						def      snake_case__(    self		,	lowercase		,	lowercase=False		,	**lowercase       )			->						List[str]:
													_a  :    List[str]			=						kwargs.pop('''add_prefix_space'''		,	self.add_prefix_space       )
													if (is_split_into_words or add_prefix_space) and (len(lowercase__       ) > 0 and not text[0].isspace()):
																				_a  :    Dict			=						''' ''' + text
													return (text, kwargs)







						def      snake_case__(    self		,	lowercase       )			->						List[int]:
													_a  :    str			=						[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(lowercase__		,	add_special_tokens=lowercase__       ) + [self.eos_token_id]       )
													if len(lowercase__       ) > self.model_max_length:
																				_a  :    Union[str, Any]			=						input_ids[-self.model_max_length :]
													return input_ids







from typing import TYPE_CHECKING

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


snake_case_   :	Dict							      ={'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Any							      =['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[Any]							      =[
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Tuple							      =[
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					snake_case_   :	List[Any]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

