





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


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{'vocab_file': 'vocab.txt'}

snake_case_				=			{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

snake_case_				=			{
    'openbmb/cpm-ant-10b': 1024,
}






def lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				Optional[Any]:
   __snake_case							       =   collections.OrderedDict()
   with open(snake_case_       ,      '''r'''       ,      encoding='''utf-8'''       ) as reader:
      __snake_case							       =   reader.readlines()
   for index, token in enumerate(snake_case_       ):
      __snake_case							       =   token.rstrip('''\n'''       )
      __snake_case							       =   index
   return vocab

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			a__       :      Dict				,			a__       :      Tuple="<unk>"				,			a__       :      Tuple=200      ):





         """simple docstring"""



         __snake_case							       =   vocab
         __snake_case							       =   unk_token
         __snake_case							       =   max_input_chars_per_word


      def    a (self       :      str				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   list(a__      )
         if len(a__      ) > self.max_input_chars_per_word:
            return [self.unk_token]

         __snake_case							       =   0
         __snake_case							       =   []
         while start < len(a__      ):
            __snake_case							       =   len(a__      )
            __snake_case							       =   None
            while start < end:
               __snake_case							       =   ''''''.join(chars[start:end]      )
               if substr in self.vocab:
                  __snake_case							       =   substr
                  break
               end -= 1
            if cur_substr is None:
               sub_tokens.append(self.unk_token      )
               start += 1
            else:
               sub_tokens.append(a__      )
               __snake_case							       =   end

         return sub_tokens






class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				Tuple							=    VOCAB_FILES_NAMES
      A_						:				str							=    PRETRAINED_VOCAB_FILES_MAP
      A_						:				Any							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:				Optional[Any]							=    ['input_ids', 'attention_mask']
      A_						:				str							=    False


      def __init__(self       :      Any				,			a__       :      Any				,			a__       :      Any="<d>"				,			a__       :      List[str]="</d>"				,			a__       :      Dict="<s>"				,			a__       :      Tuple="</s>"				,			a__       :      Optional[int]="<pad>"				,			a__       :      Optional[int]="<unk>"				,			a__       :      str="</n>"				,			a__       :      Any="</_>"				,			a__       :      Optional[int]="left"				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         requires_backends(self				,			['''jieba''']      )
         super().__init__(
             bod_token=a__				,			eod_token=a__				,			bos_token=a__				,			eos_token=a__				,			pad_token=a__				,			unk_token=a__				,			line_token=a__				,			space_token=a__				,			padding_side=a__				,			**a__				,			)
         __snake_case							       =   bod_token
         __snake_case							       =   eod_token
         __snake_case							       =   load_vocab(a__      )
         __snake_case							       =   self.encoder[space_token]
         __snake_case							       =   self.encoder[line_token]

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

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

         __snake_case							       =   WordpieceTokenizer(vocab=self.encoder				,			unk_token=self.unk_token      )


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         return self.encoder[self.bod_token]


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         return self.encoder[self.eod_token]


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         return self.encoder["\n"]


      @property
      def    a (self       :      str      ):





         """simple docstring"""



         return len(self.encoder      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         return dict(self.encoder				,			**self.added_tokens_encoder      )


      def    a (self       :      int				,			a__       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   []
         for x in jieba.cut(a__				,			cut_all=a__      ):
            output_tokens.extend(self.wordpiece_tokenizer.tokenize(a__      )      )
         return output_tokens


      def    a (self       :      Optional[int]				,			a__       :      str				,			**a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   [i for i in token_ids if i >= 0]
         __snake_case							       =   [
             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(a__				,			**a__      )


      def    a (self       :      List[Any]				,			a__       :      Any      ):





         """simple docstring"""



         return token in self.encoder


      def    a (self       :      List[Any]				,			a__       :      List[str]      ):





         """simple docstring"""



         return "".join(a__      )


      def    a (self       :      Any				,			a__       :      int      ):





         """simple docstring"""



         return self.encoder.get(a__				,			self.encoder.get(self.unk_token      )      )


      def    a (self       :      List[Any]				,			a__       :      List[Any]      ):





         """simple docstring"""



         return self.decoder.get(a__				,			self.unk_token      )


      def    a (self       :      Optional[Any]				,			a__       :      str				,			a__       :      Optional[str] = None      ):





         """simple docstring"""



         if os.path.isdir(a__      ):
            __snake_case							       =   os.path.join(
                a__				,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
         else:
            __snake_case							       =   (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         __snake_case							       =   0
         if " " in self.encoder:
            __snake_case							       =   self.encoder[''' ''']
            del self.encoder[" "]
         if "\n" in self.encoder:
            __snake_case							       =   self.encoder['''\n''']
            del self.encoder["\n"]
         __snake_case							       =   collections.OrderedDict(sorted(self.encoder.items()				,			key=lambda a__      : x[1]      )      )
         with open(a__				,			'''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!'''      )
                  __snake_case							       =   token_index
               writer.write(token + '''\n'''      )
               index += 1
         return (vocab_file,)


      def    a (self       :      Dict				,			a__       :      List[int]				,			a__       :      List[int] = None      ):





         """simple docstring"""



         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    a (self       :      Any				,			a__       :      List[int]				,			a__       :      Optional[List[int]] = None				,			a__       :      bool = False      ):





         """simple docstring"""



         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=a__				,			token_ids_a=a__				,			already_has_special_tokens=a__      )

         if token_ids_a is not None:
            return [1] + ([0] * len(a__      )) + [1] + ([0] * len(a__      ))
         return [1] + ([0] * len(a__      ))








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

