


import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
    import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase					:       Union[str, Any]			    =     logging.get_logger(__name__)

UpperCamelCase					:       Tuple			    =     {"""vocab_file""": """vocab.txt"""}

UpperCamelCase					:       Union[str, Any]			    =     {
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

UpperCamelCase					:       int			    =     {
    """openbmb/cpm-ant-10b""": 1024,
}



def UpperCamelCase_				(				__a      )	->   Tuple:
      a__	:  List[str]     =   collections.OrderedDict()
      with open(__a			,				"r"			,				encoding="utf-8"      ) as reader:
            a__	:  Optional[int]     =   reader.readlines()
      for index, token in enumerate(__a      ):
            a__	:  List[str]     =   token.rstrip("\n"      )
            a__	:  Tuple     =   index
      return vocab



class A__     ( A__  ):



  """simple docstring"""



  def __init__( self			:  int				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Union[str, Any]="<unk>"				,				lowerCamelCase__			:  Any=200       ):
        a__	:  Tuple     =   vocab
        a__	:  Tuple     =   unk_token
        a__	:  List[Any]     =   max_input_chars_per_word

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  List[str]       ):
        a__	:  List[str]     =   list(lowerCamelCase__       )
        if len(lowerCamelCase__       ) > self.max_input_chars_per_word:
              return [self.unk_token]

        a__	:  Optional[int]     =   0
        a__	:  Tuple     =   []
        while start < len(lowerCamelCase__       ):
              a__	:  Union[str, Any]     =   len(lowerCamelCase__       )
              a__	:  List[str]     =   None
              while start < end:
                    a__	:  int     =   "".join(chars[start:end]       )
                    if substr in self.vocab:
                          a__	:  Optional[Any]     =   substr
                          break
                    end -= 1
              if cur_substr is None:
                    sub_tokens.append(self.unk_token       )
                    start += 1
              else:
                    sub_tokens.append(lowerCamelCase__       )
                    a__	:  List[Any]     =   end

        return sub_tokens




class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		VOCAB_FILES_NAMES
  _lowercase									=		PRETRAINED_VOCAB_FILES_MAP
  _lowercase									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase									=		['input_ids', 'attention_mask']
  _lowercase									=		False

  def __init__( self			:  List[str]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[int]="<d>"				,				lowerCamelCase__			:  List[Any]="</d>"				,				lowerCamelCase__			:  Union[str, Any]="<s>"				,				lowerCamelCase__			:  Union[str, Any]="</s>"				,				lowerCamelCase__			:  List[Any]="<pad>"				,				lowerCamelCase__			:  str="<unk>"				,				lowerCamelCase__			:  int="</n>"				,				lowerCamelCase__			:  Optional[Any]="</_>"				,				lowerCamelCase__			:  Any="left"				,				**lowerCamelCase__			:  Optional[Any]				,				):
        requires_backends(self				,				["jieba"]       )
        super().__init__(
            bod_token=lowerCamelCase__				,				eod_token=lowerCamelCase__				,				bos_token=lowerCamelCase__				,				eos_token=lowerCamelCase__				,				pad_token=lowerCamelCase__				,				unk_token=lowerCamelCase__				,				line_token=lowerCamelCase__				,				space_token=lowerCamelCase__				,				padding_side=lowerCamelCase__				,				**lowerCamelCase__				,				)
        a__	:  Union[str, Any]     =   bod_token
        a__	:  Optional[Any]     =   eod_token
        a__	:  Any     =   load_vocab(lowerCamelCase__       )
        a__	:  Dict     =   self.encoder[space_token]
        a__	:  List[str]     =   self.encoder[line_token]

        del self.encoder[space_token]
        del self.encoder[line_token]

        a__	:  Optional[Any]     =   collections.OrderedDict(sorted(self.encoder.items()				,				key=lambda lowerCamelCase__       : x[1]       )       )
        a__	:  Union[str, Any]     =   {v: k for k, v in self.encoder.items()}

        a__	:  Optional[int]     =   WordpieceTokenizer(vocab=self.encoder				,				unk_token=self.unk_token       )

  @property
  def    _UpperCamelCase( self			:  List[str]       ):
        return self.encoder[self.bod_token]

  @property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        return self.encoder[self.eod_token]

  @property
  def    _UpperCamelCase( self			:  Tuple       ):
        return self.encoder["\n"]

  @property
  def    _UpperCamelCase( self			:  Optional[int]       ):
        return len(self.encoder       )

  def    _UpperCamelCase( self			:  Dict       ):
        return dict(self.encoder				,				**self.added_tokens_encoder       )

  def    _UpperCamelCase( self			:  Optional[Any]				,				lowerCamelCase__			:  Optional[Any]       ):
        a__	:  Optional[Any]     =   []
        for x in jieba.cut(lowerCamelCase__				,				cut_all=lowerCamelCase__       ):
              output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase__       )       )
        return output_tokens

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  Optional[int]				,				**lowerCamelCase__			:  Tuple       ):
        a__	:  Tuple     =   [i for i in token_ids if i >= 0]
        a__	:  List[str]     =   [
            x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
        ]
        return super()._decode(lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  Optional[int]       ):
        return token in self.encoder

  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  List[str]       ):
        return "".join(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[Any]				,				lowerCamelCase__			:  str       ):
        return self.encoder.get(lowerCamelCase__				,				self.encoder.get(self.unk_token       )       )

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  Tuple       ):
        return self.decoder.get(lowerCamelCase__				,				self.unk_token       )

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[str] = None       ):
        if os.path.isdir(lowerCamelCase__       ):
              a__	:  Optional[int]     =   os.path.join(
                  lowerCamelCase__				,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
        else:
              a__	:  Optional[int]     =   (filename_prefix + "-" if filename_prefix else "") + save_directory
        a__	:  Dict     =   0
        if " " in self.encoder:
              a__	:  int     =   self.encoder[" "]
              del self.encoder[" "]
        if "\n" in self.encoder:
              a__	:  int     =   self.encoder["\n"]
              del self.encoder["\n"]
        a__	:  Optional[int]     =   collections.OrderedDict(sorted(self.encoder.items()				,				key=lambda lowerCamelCase__       : x[1]       )       )
        with open(lowerCamelCase__				,				"w"				,				encoding="utf-8"       ) as writer:
              for token, token_index in self.encoder.items():
                    if index != token_index:
                          logger.warning(
                              f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                              " Please check that the vocabulary is not corrupted!"       )
                          a__	:  List[Any]     =   token_index
                    writer.write(token + "\n"       )
                    index += 1
        return (vocab_file,)

  def    _UpperCamelCase( self			:  Optional[Any]				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  List[int] = None       ):
        if token_ids_a is None:
              return [self.bos_token_id] + token_ids_a
        return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a

  def    _UpperCamelCase( self			:  Optional[int]				,				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 not None:
              return [1] + ([0] * len(lowerCamelCase__       )) + [1] + ([0] * len(lowerCamelCase__       ))
        return [1] + ([0] * len(lowerCamelCase__       ))





'''simple docstring'''





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__  			=				{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Tuple					      =			'''xlm-roberta-xl'''
			def __init__(  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Tuple=250_880			,    lowerCAmelCase_							:			Optional[int]=2_560			,    lowerCAmelCase_							:			Optional[Any]=36			,    lowerCAmelCase_							:			List[str]=32			,    lowerCAmelCase_							:			Optional[Any]=10_240			,    lowerCAmelCase_							:			Union[str, Any]="gelu"			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[Any]=0.1			,    lowerCAmelCase_							:			Optional[int]=514			,    lowerCAmelCase_							:			List[str]=1			,    lowerCAmelCase_							:			Optional[Any]=0.02			,    lowerCAmelCase_							:			List[str]=1E-05			,    lowerCAmelCase_							:			int=1			,    lowerCAmelCase_							:			List[str]=0			,    lowerCAmelCase_							:			List[Any]=2			,    lowerCAmelCase_							:			Optional[int]="absolute"			,    lowerCAmelCase_							:			List[Any]=True			,    lowerCAmelCase_							:			Tuple=None			,    **lowerCAmelCase_							:			List[str]			,    )      ->		str:
						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=	hidden_act
						__A=	intermediate_size
						__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=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Mapping[str, Mapping[int, str]]:
						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),
						    ]	)



