
"""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


UpperCamelCase     :    str       =			logging.get_logger(__name__)

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

UpperCamelCase     :    str       =			{
    "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"
        ),
    },
}

UpperCamelCase     :    List[Any]       =			{
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase						       =						["input_ids", "attention_mask"]
     lowercase						       =						RobertaTokenizer





     def __init__(     self     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="replace"     ,					__UpperCAmelCase="<s>"     ,					__UpperCAmelCase="</s>"     ,					__UpperCAmelCase="</s>"     ,					__UpperCAmelCase="<s>"     ,					__UpperCAmelCase="<unk>"     ,					__UpperCAmelCase="<pad>"     ,					__UpperCAmelCase="<mask>"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      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     ,					)

      __UpperCamelCase      =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
      if pre_tok_state.get('add_prefix_space'     ,					__UpperCAmelCase				) != add_prefix_space:
       __UpperCamelCase      =	getattr(__UpperCAmelCase     ,					pre_tok_state.pop('type'				)				)
       __UpperCamelCase      =	add_prefix_space
       __UpperCamelCase      =	pre_tok_class(**__UpperCAmelCase				)

      __UpperCamelCase      =	add_prefix_space

      __UpperCamelCase      =	'post_processor'
      __UpperCamelCase      =	getattr(self.backend_tokenizer     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      if tokenizer_component_instance:
       __UpperCamelCase      =	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:
        __UpperCamelCase      =	tuple(state['sep']				)
       if "cls" in state:
        __UpperCamelCase      =	tuple(state['cls']				)

       __UpperCamelCase      =	False

       if state.get('add_prefix_space'     ,					__UpperCAmelCase				) != add_prefix_space:
        __UpperCamelCase      =	add_prefix_space
        __UpperCamelCase      =	True

       if state.get('trim_offsets'     ,					__UpperCAmelCase				) != trim_offsets:
        __UpperCamelCase      =	trim_offsets
        __UpperCamelCase      =	True

       if changes_to_apply:
        __UpperCamelCase      =	getattr(__UpperCAmelCase     ,					state.pop('type'				)				)
        __UpperCamelCase      =	component_class(**__UpperCAmelCase				)
        setattr(self.backend_tokenizer     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      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 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	AddedToken(__UpperCAmelCase     ,					lstrip=__UpperCAmelCase     ,					rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else value
      __UpperCamelCase      =	value





     def 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	self._tokenizer.model.save(__UpperCAmelCase     ,					name=__UpperCAmelCase				)
      return tuple(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				):

      '''simple docstring'''




      __UpperCamelCase      =	[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 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      __UpperCamelCase      =	[self.sep_token_id]
      __UpperCamelCase      =	[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 typing import Union

import fire
import torch
from tqdm import tqdm






def 							A						(    snake_case				:str    , snake_case				:str = "cpu"    , snake_case				:Union[str, None] = None		)				->     None:
 __UpperCamelCase      =	torch.load(snake_case    , map_location=snake_case		)
 for k, v in tqdm(state_dict.items()		):
  if not isinstance(snake_case    , torch.Tensor		):
   raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'		)
  __UpperCamelCase      =	v.half()
 if save_path is None:  # overwrite src_path
  __UpperCamelCase      =	src_path
 torch.save(snake_case    , snake_case		)


if __name__ == "__main__":
      fire.Fire(convert)



