


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


_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE      :			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"
        ),
    },
}

_SCREAMING_SNAKE_CASE      :			Tuple      				=   {
    "roberta-base": 5_12,
    "roberta-large": 5_12,
    "roberta-large-mnli": 5_12,
    "distilroberta-base": 5_12,
    "roberta-base-openai-detector": 5_12,
    "roberta-large-openai-detector": 5_12,
}

class   A__ (   snake_case__     ):







     """simple docstring"""

     __magic_name__        =		VOCAB_FILES_NAMES
     __magic_name__        =		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__        =		['input_ids', 'attention_mask']
     __magic_name__        =		RobertaTokenizer
     def __init__(	self      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case="replace"      , __snake_case="<s>"      , __snake_case="</s>"      , __snake_case="</s>"      , __snake_case="<s>"      , __snake_case="<unk>"      , __snake_case="<pad>"      , __snake_case="<mask>"      , __snake_case=False      , __snake_case=True      , **__snake_case      , ):
          super().__init__(
              __snake_case      , __snake_case      , tokenizer_file=__snake_case      , errors=__snake_case      , bos_token=__snake_case      , eos_token=__snake_case      , sep_token=__snake_case      , cls_token=__snake_case      , unk_token=__snake_case      , pad_token=__snake_case      , mask_token=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case      , **__snake_case      , )

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

          snake_case					=    add_prefix_space

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

               snake_case					=    False

               if state.get('''add_prefix_space'''      , __snake_case   ) != add_prefix_space:
                    snake_case					=    add_prefix_space
                    snake_case					=    True

               if state.get('''trim_offsets'''      , __snake_case   ) != trim_offsets:
                    snake_case					=    trim_offsets
                    snake_case					=    True

               if changes_to_apply:
                    snake_case					=    getattr(__snake_case      , state.pop('''type'''   )   )
                    snake_case					=    component_class(**__snake_case   )
                    setattr(self.backend_tokenizer      , __snake_case      , __snake_case   )
     @property
     def 		a_      (	self   ):
          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 		a_      (	self      , __snake_case   ):
          snake_case					=    AddedToken(__snake_case      , lstrip=__snake_case      , rstrip=__snake_case   ) if isinstance(__snake_case      , __snake_case   ) else value
          snake_case					=    value
     def 		a_      (	self      , *__snake_case      , **__snake_case   ):
          snake_case					=    kwargs.get('''is_split_into_words'''      , __snake_case   )
          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(*__snake_case      , **__snake_case   )
     def 		a_      (	self      , *__snake_case      , **__snake_case   ):
          snake_case					=    kwargs.get('''is_split_into_words'''      , __snake_case   )

          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(*__snake_case      , **__snake_case   )
     def 		a_      (	self      , __snake_case      , __snake_case = None   ):
          snake_case					=    self._tokenizer.model.save(__snake_case      , name=__snake_case   )
          return tuple(__snake_case   )
     def 		a_      (	self      , __snake_case      , __snake_case=None   ):
          snake_case					=    [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 		a_      (	self      , __snake_case      , __snake_case = None   ):
          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 + sep + token_ids_a + sep   ) * [0]








# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(UpperCamelCase_    )

def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_terminal_summary_main

     snake_case					=    terminalreporter.config.getoption('''--make-reports'''    )
     if make_reports:
          pytest_terminal_summary_main(UpperCamelCase_							,id=UpperCamelCase_    )




