import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
      from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{'''vocab_file''': '''spiece.model'''}

UpperCAmelCase_								=							{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_								=							{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

UpperCAmelCase_								=							'''▁'''





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE     :     Tuple		=PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE     :     int		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=["""input_ids""", """attention_mask"""]



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__="</s>"				,      lowerCAmelCase__="<unk>"				,      lowerCAmelCase__="<pad>"				,      lowerCAmelCase__=100				,      lowerCAmelCase__=None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__=True				,      **lowerCAmelCase__				,      ):
            # Add extra_ids to the special token list
            if extra_ids > 0 and additional_special_tokens is None:
                   _A=	[f"<extra_id_{i}>" for i in range(lowerCAmelCase__       )]
            elif extra_ids > 0 and additional_special_tokens is not None:
                   # Check that we have the right number of extra_id special tokens
                   _A=	len(set(filter(lambda lowerCAmelCase__       : bool('extra_id' in str(lowerCAmelCase__       )       )				,      lowerCAmelCase__       )       )       )
                   if extra_tokens != extra_ids:
                          raise ValueError(
                              f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                              ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                              ' tokens'       )
            if legacy:
                   logger.warning_once(
                       f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
                       ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'       )

            _A=	legacy
            _A=	{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                eos_token=lowerCAmelCase__				,      unk_token=lowerCAmelCase__				,      pad_token=lowerCAmelCase__				,      extra_ids=lowerCAmelCase__				,      additional_special_tokens=lowerCAmelCase__				,      sp_model_kwargs=self.sp_model_kwargs				,      legacy=lowerCAmelCase__				,      **lowerCAmelCase__				,      )

            _A=	vocab_file
            _A=	extra_ids

            _A=	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
            self.sp_model.Load(lowerCAmelCase__       )



     @staticmethod
     def 			a__  (					lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                   _A=	TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
                   if init_max_model_length is not None and init_max_model_length != max_model_length:
                          return init_max_model_length
                   elif init_max_model_length is None:
                          warnings.warn(
                              'This tokenizer was incorrectly instantiated with a model max length of'
                              f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
                              ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                              ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                              f" {pretrained_model_name_or_path} automatically truncating your input to"
                              f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
                              f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
                              ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                              ' instantiate this tokenizer with `model_max_length` set to your preferred value.'				,      lowerCAmelCase__				,      )

            return max_model_length



     @property
     def 			a__  (					self       ):
            return self.sp_model.get_piece_size() + self._extra_ids



     def 			a__  (					self       ):
            _A=	{self.convert_ids_to_tokens(lowerCAmelCase__       ): i for i in range(self.vocab_size       )}
            vocab.update(self.added_tokens_encoder       )
            return vocab



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = 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__       )

            # normal case: some special tokens
            if token_ids_a is None:
                   return ([0] * len(lowerCAmelCase__       )) + [1]
            return ([0] * len(lowerCAmelCase__       )) + [1] + ([0] * len(lowerCAmelCase__       )) + [1]



     def 			a__  (					self       ):
            return list(
                set(filter(lambda lowerCAmelCase__       : bool(re.search(r'<extra_id_\d+>'				,      lowerCAmelCase__       )       ) is not None				,      self.additional_special_tokens       )       )       )



     def 			a__  (					self       ):
            return [self._convert_token_to_id(lowerCAmelCase__       ) for token in self.get_sentinel_tokens()]



     def 			a__  (					self				,      lowerCAmelCase__       ):
            if len(lowerCAmelCase__       ) > 0 and token_ids[-1] == self.eos_token_id:
                   warnings.warn(
                       f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
                       ' eos tokens being added.'       )
                   return token_ids
            else:
                   return token_ids + [self.eos_token_id]



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            _A=	[self.eos_token_id]

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



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            _A=	self._add_eos_if_not_present(lowerCAmelCase__       )
            if token_ids_a is None:
                   return token_ids_a
            else:
                   _A=	self._add_eos_if_not_present(lowerCAmelCase__       )
                   return token_ids_a + token_ids_a



     def __getstate__(					self       ):
            _A=	self.__dict__.copy()
            _A=	None
            return state



     def __setstate__(					self				,      lowerCAmelCase__       ):
            _A=	d

            # for backward compatibility
            if not hasattr(self				,      'sp_model_kwargs'       ):
                   _A=	{}

            _A=	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
            self.sp_model.Load(self.vocab_file       )



     def 			a__  (					self				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
            # the beginning of the text
            if not self.legacy:
                   _A=	SPIECE_UNDERLINE + text.replace(lowerCAmelCase__				,      ' '       )
            return super().tokenize(lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            if not self.legacy:
                   _A=	text.startswith(lowerCAmelCase__       )
                   if is_first:
                          _A=	text[1:]

            _A=	self.sp_model.encode(lowerCAmelCase__				,      out_type=lowerCAmelCase__       )

            if not self.legacy and not is_first and not text.startswith(' '       ) and tokens[0].startswith(lowerCAmelCase__       ):
                   _A=	([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
            return tokens



     def 			a__  (					self				,      lowerCAmelCase__       ):
            if token.startswith('<extra_id_'       ):
                   _A=	re.match(r'<extra_id_(\d+)>'				,      lowerCAmelCase__       )
                   _A=	int(match.group(1       )       )
                   return self.vocab_size - num - 1
            return self.sp_model.piece_to_id(lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            if index < self.sp_model.get_piece_size():
                   _A=	self.sp_model.IdToPiece(lowerCAmelCase__       )
            else:
                   _A=	f"<extra_id_{self.vocab_size - 1 - index}>"
            return token



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	[]
            _A=	''
            _A=	False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          if not prev_is_special:
                                 out_string += " "
                          out_string += self.sp_model.decode(lowerCAmelCase__       ) + token
                          _A=	True
                          _A=	[]
                   else:
                          current_sub_tokens.append(lowerCAmelCase__       )
                          _A=	False
            out_string += self.sp_model.decode(lowerCAmelCase__       )
            return out_string.strip()





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

            if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCAmelCase__       ) and os.path.isfile(self.vocab_file       ):
                   copyfile(self.vocab_file				,      lowerCAmelCase__       )
            elif not os.path.isfile(self.vocab_file       ):
                   with open(lowerCAmelCase__				,      'wb'       ) as fi:
                          _A=	self.sp_model.serialized_model_proto()
                          fi.write(lowerCAmelCase__       )

            return (out_vocab_file,)
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		="""convbert"""



     def __init__(					self				,      lowerCAmelCase__=30522				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=512				,      lowerCAmelCase__=2				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=1E-12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=768				,      lowerCAmelCase__=2				,      lowerCAmelCase__=9				,      lowerCAmelCase__=1				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	intermediate_size
            _A=	hidden_act
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	type_vocab_size
            _A=	initializer_range
            _A=	layer_norm_eps
            _A=	embedding_size
            _A=	head_ratio
            _A=	conv_kernel_size
            _A=	num_groups
            _A=	classifier_dropout





class      lowerCAmelCase						(					_a			):



     @property
     def 			a__  (					self       ):
            if self.task == "multiple-choice":
                   _A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _A=	{0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                    ('token_type_ids', dynamic_axis),
                ]       )