






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

snake_case_   :	Tuple							      =logging.get_logger(__name__)

snake_case_   :	Optional[Any]							      ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

snake_case_   :	str							      ={
    '''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'''
        ),
    },
}

snake_case_   :	List[Any]							      ={
    '''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,
}

snake_case_   :	Union[str, Any]							      ={
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}



def      UpperCAmelCase						( ):


		'''simple docstring'''

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



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Union[str, Any] 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       Tuple 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Optional[Any] 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       Union[str, Any] 					= ['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    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else bos_token
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else eos_token
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else sep_token
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else cls_token
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else unk_token
				__A    =							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    =							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    =							add_bos_token

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

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				__A    =							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						)				->				int:
				return len(self.encoder						)






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






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Tuple:
				if token in self.cache:
						return self.cache[token]
				__A    =							tuple(lowercase__						)
				__A    =							get_pairs(lowercase__						)

				if not pairs:
						return token

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






		def 					_lowerCamelCase					(      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    =							[self.cls_token_id]
				__A    =							[self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep






		def 					_lowerCamelCase					(      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 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__A    =							[self.sep_token_id]
				__A    =							[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							, lowercase__						)				->				List[Any]:
				__A    =							[]
				for token in re.findall(self.pat							, lowercase__						):
						__A    =							"".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 					_lowerCamelCase					(      self							, lowercase__						)				->				List[str]:
				return self.encoder.get(lowercase__							, self.encoder.get(self.unk_token						)						)






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






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				Dict:
				__A    =							"".join(lowercase__						)
				__A    =							bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"							, errors=self.errors						)
				return text






		def 					_lowerCamelCase					(      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    =							os.path.join(
				    lowercase__							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
				__A    =							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    =							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    =							token_index
								writer.write(" ".join(lowercase__						) + "\n"						)
								index += 1

				return vocab_file, merge_file






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__=False							, **lowercase__						)				->				List[str]:
				__A    =							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    =							" " + text
				return (text, kwargs)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				List[int]:
				__A    =							[]
				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    =							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__)

