


"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowercase    =				{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

__lowercase    =				{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							int     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							Union[str, Any]     =	PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							Any     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase	:							List[str]     =	["""input_ids""", """attention_mask"""]
    UpperCAmelCase	:							List[Any]     =	RobertaTokenizer






    def __init__( self    :   Any    ,			__UpperCAmelCase    :   str=None    ,			__UpperCAmelCase    :   str=None    ,			__UpperCAmelCase    :   Optional[Any]=None    ,			__UpperCAmelCase    :   Optional[int]="replace"    ,			__UpperCAmelCase    :   List[Any]="<s>"    ,			__UpperCAmelCase    :   List[str]="</s>"    ,			__UpperCAmelCase    :   List[Any]="</s>"    ,			__UpperCAmelCase    :   Dict="<s>"    ,			__UpperCAmelCase    :   Optional[int]="<unk>"    ,			__UpperCAmelCase    :   List[Any]="<pad>"    ,			__UpperCAmelCase    :   Union[str, Any]="<mask>"    ,			__UpperCAmelCase    :   Optional[int]=False    ,			__UpperCAmelCase    :   Dict=True    ,			**__UpperCAmelCase    :   str    ,			):
      super().__init__(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			tokenizer_file=__UpperCAmelCase    ,			errors=__UpperCAmelCase    ,			bos_token=__UpperCAmelCase    ,			eos_token=__UpperCAmelCase    ,			sep_token=__UpperCAmelCase    ,			cls_token=__UpperCAmelCase    ,			unk_token=__UpperCAmelCase    ,			pad_token=__UpperCAmelCase    ,			mask_token=__UpperCAmelCase    ,			add_prefix_space=__UpperCAmelCase    ,			trim_offsets=__UpperCAmelCase    ,			**__UpperCAmelCase    ,			)

      a						:		Any											=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
      if pre_tok_state.get("add_prefix_space"    ,			__UpperCAmelCase) != add_prefix_space:
        a						:		str											=      getattr(__UpperCAmelCase    ,			pre_tok_state.pop("type"))
        a						:		Union[str, Any]											=      add_prefix_space
        a						:		int											=      pre_tok_class(**__UpperCAmelCase)

      a						:		Dict											=      add_prefix_space

      a						:		str											=      "post_processor"
      a						:		Optional[int]											=      getattr(self.backend_tokenizer    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      if tokenizer_component_instance:
        a						:		str											=      json.loads(tokenizer_component_instance.__getstate__())

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
          a						:		Dict											=      tuple(state["sep"])
        if "cls" in state:
          a						:		Tuple											=      tuple(state["cls"])

        a						:		Tuple											=      False

        if state.get("add_prefix_space"    ,			__UpperCAmelCase) != add_prefix_space:
          a						:		int											=      add_prefix_space
          a						:		Tuple											=      True

        if state.get("trim_offsets"    ,			__UpperCAmelCase) != trim_offsets:
          a						:		Optional[Any]											=      trim_offsets
          a						:		Dict											=      True

        if changes_to_apply:
          a						:		Optional[int]											=      getattr(__UpperCAmelCase    ,			state.pop("type"))
          a						:		List[Any]											=      component_class(**__UpperCAmelCase)
          setattr(self.backend_tokenizer    ,			__UpperCAmelCase    ,			__UpperCAmelCase)






    @property
    def      __snake_case							( self    :   Tuple):
      if self._mask_token is None:
        if self.verbose:
          logger.error("Using mask_token, but it is not set yet.")
        return None
      return str(self._mask_token)






    @mask_token.setter
    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   List[Any]):
      a						:		Optional[int]											=      AddedToken(__UpperCAmelCase    ,			lstrip=__UpperCAmelCase    ,			rstrip=__UpperCAmelCase) if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) else value
      a						:		Optional[int]											=      value






    def      __snake_case							( self    :   Dict    ,			*__UpperCAmelCase    :   Any    ,			**__UpperCAmelCase    :   str):
      a						:		str											=      kwargs.get("is_split_into_words"    ,			__UpperCAmelCase)
      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   int    ,			*__UpperCAmelCase    :   Optional[Any]    ,			**__UpperCAmelCase    :   List[Any]):
      a						:		Tuple											=      kwargs.get("is_split_into_words"    ,			__UpperCAmelCase)

      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__UpperCAmelCase    ,			**__UpperCAmelCase)






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Optional[str] = None):
      a						:		Tuple											=      self._tokenizer.model.save(__UpperCAmelCase    ,			name=__UpperCAmelCase)
      return tuple(__UpperCAmelCase)






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Tuple=None):
      a						:		Optional[Any]											=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
        return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None):
      a						:		Dict											=      [self.sep_token_id]
      a						:		str											=      [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 + sep + token_ids_a + sep) * [0]










"""simple docstring"""







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


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	"""gpt_neox"""






    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple=50432    ,			__UpperCAmelCase    :   str=6144    ,			__UpperCAmelCase    :   Any=44    ,			__UpperCAmelCase    :   Union[str, Any]=64    ,			__UpperCAmelCase    :   Dict=24576    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   List[Any]=0.25    ,			__UpperCAmelCase    :   Optional[Any]=10000    ,			__UpperCAmelCase    :   Tuple=0.0    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[Any]=2048    ,			__UpperCAmelCase    :   Optional[Any]=0.02    ,			__UpperCAmelCase    :   Optional[Any]=1e-5    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   Union[str, Any]=0    ,			__UpperCAmelCase    :   Union[str, Any]=2    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   List[Any]=True    ,			__UpperCAmelCase    :   int=None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      super().__init__(bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)
      a						:		List[Any]											=      vocab_size
      a						:		Optional[int]											=      max_position_embeddings
      a						:		List[Any]											=      hidden_size
      a						:		Union[str, Any]											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Optional[Any]											=      hidden_act
      a						:		Dict											=      rotary_pct
      a						:		Any											=      rotary_emb_base
      a						:		Dict											=      attention_dropout
      a						:		List[str]											=      hidden_dropout
      a						:		List[str]											=      classifier_dropout
      a						:		Any											=      initializer_range
      a						:		Union[str, Any]											=      layer_norm_eps
      a						:		int											=      use_cache
      a						:		int											=      tie_word_embeddings
      a						:		str											=      use_parallel_residual
      a						:		Union[str, Any]											=      rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            "The hidden size is not divisble by the number of attention heads! Make sure to update them!")






    def      __snake_case							( self    :   Any):
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,			__UpperCAmelCase) or len(self.rope_scaling) != 2:
        raise ValueError(
            "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
            f'''got {self.rope_scaling}''')
      a						:		str											=      self.rope_scaling.get("type"    ,			__UpperCAmelCase)
      a						:		List[str]											=      self.rope_scaling.get("factor"    ,			__UpperCAmelCase)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''')
      if rope_scaling_factor is None or not isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) or rope_scaling_factor <= 1.0:
        raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''')






