
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCamelCase__     :		Dict        = {
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		str        = [
         """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTNeoForCausalLM""",
         """GPTNeoForQuestionAnswering""",
         """GPTNeoForSequenceClassification""",
         """GPTNeoForTokenClassification""",
         """GPTNeoModel""",
         """GPTNeoPreTrainedModel""",
         """load_tf_weights_in_gpt_neo""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		List[Any]        = [
         """FlaxGPTNeoForCausalLM""",
         """FlaxGPTNeoModel""",
         """FlaxGPTNeoPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

     lowerCamelCase__     :		Any        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case   					=				{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case   					=				{
    '''junnyu/roformer_chinese_small''': 1_5_3_6,
    '''junnyu/roformer_chinese_base''': 1_5_3_6,
    '''junnyu/roformer_chinese_char_small''': 5_1_2,
    '''junnyu/roformer_chinese_char_base''': 5_1_2,
    '''junnyu/roformer_small_discriminator''': 1_2_8,
    '''junnyu/roformer_small_generator''': 1_2_8,
}


snake_case   					=				{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  VOCAB_FILES_NAMES
   A__		:	Tuple           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	List[Any]           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Dict           =  PRETRAINED_INIT_CONFIGURATION
   A__		:	Any           =  RoFormerTokenizer



   def __init__(       self    :	Optional[int]							,       __lowerCamelCase    :	Any=None							,       __lowerCamelCase    :	Optional[Any]=None							,       __lowerCamelCase    :	int=True							,       __lowerCamelCase    :	Any="[UNK]"							,       __lowerCamelCase    :	Tuple="[SEP]"							,       __lowerCamelCase    :	Optional[Any]="[PAD]"							,       __lowerCamelCase    :	Union[str, Any]="[CLS]"							,       __lowerCamelCase    :	int="[MASK]"							,       __lowerCamelCase    :	str=True							,       __lowerCamelCase    :	List[Any]=None							,       **__lowerCamelCase    :	Any							,       ):



     """simple docstring"""
     super().__init__(
         __lowerCamelCase							,       tokenizer_file=__lowerCamelCase							,       do_lower_case=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       sep_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       cls_token=__lowerCamelCase							,       mask_token=__lowerCamelCase							,       tokenize_chinese_chars=__lowerCamelCase							,       strip_accents=__lowerCamelCase							,       **__lowerCamelCase							,       )

     _snake_case   	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
     if (
         pre_tok_state.get('''lowercase'''							,       __lowerCamelCase				) != do_lower_case
         or pre_tok_state.get('''strip_accents'''							,       __lowerCamelCase				) != strip_accents
     ):
       _snake_case   	=      getattr(__lowerCamelCase							,       pre_tok_state.pop('''type'''				)				)
       _snake_case   	=      do_lower_case
       _snake_case   	=      strip_accents
       _snake_case   	=      pre_tok_class(**__lowerCamelCase				)

     _snake_case   	=      do_lower_case



   def __getstate__(       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.__dict__.copy()
     _snake_case   	=      BertPreTokenizer()
     return state



   def __setstate__(       self    :	Dict							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      d
     _snake_case   	=      self.__dict__['''_tokenizer'''].get_vocab()
     _snake_case   	=      PreTokenizer.custom(JiebaPreTokenizer(__lowerCamelCase				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	List[Any]=None				):



     """simple docstring"""
     _snake_case   	=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     _snake_case   	=      [self.sep_token_id]
     _snake_case   	=      [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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     _snake_case   	=      self._tokenizer.model.save(__lowerCamelCase							,       name=__lowerCamelCase				)
     return tuple(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	List[str]=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       __lowerCamelCase    :	List[str]=False							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      BertPreTokenizer()
     return super().save_pretrained(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       **__lowerCamelCase				)





