

'''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_536,
    "junnyu/roformer_chinese_base": 1_536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


_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   a__    (				a_    ):
   _SCREAMING_SNAKE_CASE       :	Union[str, Any]      			=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE       :	Tuple      			=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE       :	Dict      			=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE       :	Dict      			=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE       :	List[Any]      			=				RoFormerTokenizer


   def __init__(					self     ,							_UpperCamelCase=None     ,							_UpperCamelCase=None     ,							_UpperCamelCase=True     ,							_UpperCamelCase="[UNK]"     ,							_UpperCamelCase="[SEP]"     ,							_UpperCamelCase="[PAD]"     ,							_UpperCamelCase="[CLS]"     ,							_UpperCamelCase="[MASK]"     ,							_UpperCamelCase=True     ,							_UpperCamelCase=None     ,							**_UpperCamelCase     ,							):





      """simple docstring"""





      super().__init__(
          _UpperCamelCase     ,							tokenizer_file=_UpperCamelCase     ,							do_lower_case=_UpperCamelCase     ,							unk_token=_UpperCamelCase     ,							sep_token=_UpperCamelCase     ,							pad_token=_UpperCamelCase     ,							cls_token=_UpperCamelCase     ,							mask_token=_UpperCamelCase     ,							tokenize_chinese_chars=_UpperCamelCase     ,							strip_accents=_UpperCamelCase     ,							**_UpperCamelCase     ,							)

      _lowercase     : Any     	=  json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
      if (
          pre_tok_state.get("lowercase"     ,							_UpperCamelCase    ) != do_lower_case
          or pre_tok_state.get("strip_accents"     ,							_UpperCamelCase    ) != strip_accents
      ):
         _lowercase     : Dict     	=  getattr(_UpperCamelCase     ,							pre_tok_state.pop("type"    )    )
         _lowercase     : int     	=  do_lower_case
         _lowercase     : List[Any]     	=  strip_accents
         _lowercase     : List[Any]     	=  pre_tok_class(**_UpperCamelCase    )

      _lowercase     : Optional[Any]     	=  do_lower_case


   def __getstate__(					self    ):





      """simple docstring"""





      _lowercase     : str     	=  self.__dict__.copy()
      _lowercase     : Optional[Any]     	=  BertPreTokenizer()
      return state


   def __setstate__(					self     ,							_UpperCamelCase    ):





      """simple docstring"""





      _lowercase     : int     	=  d
      _lowercase     : List[Any]     	=  self.__dict__["_tokenizer"].get_vocab()
      _lowercase     : List[str]     	=  PreTokenizer.custom(JiebaPreTokenizer(_UpperCamelCase    )    )


   def 		_lowerCamelCase  (					self     ,							_UpperCamelCase     ,							_UpperCamelCase=None    ):





      """simple docstring"""





      _lowercase     : Optional[int]     	=  [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 		_lowerCamelCase  (					self     ,							_UpperCamelCase     ,							_UpperCamelCase = None    ):





      """simple docstring"""





      _lowercase     : int     	=  [self.sep_token_id]
      _lowercase     : Optional[Any]     	=  [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 		_lowerCamelCase  (					self     ,							_UpperCamelCase     ,							_UpperCamelCase = None    ):





      """simple docstring"""





      _lowercase     : Optional[int]     	=  self._tokenizer.model.save(_UpperCamelCase     ,							name=_UpperCamelCase    )
      return tuple(_UpperCamelCase    )






   def 		_lowerCamelCase  (					self     ,							_UpperCamelCase     ,							_UpperCamelCase=None     ,							_UpperCamelCase=None     ,							_UpperCamelCase=False     ,							**_UpperCamelCase     ,							):





      """simple docstring"""





      _lowercase     : List[Any]     	=  BertPreTokenizer()
      return super().save_pretrained(_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							_UpperCamelCase     ,							**_UpperCamelCase    )










import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

