



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


__A   			=logging.get_logger(__name__)

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

__A   			={
    "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"
        ),
    },
}

__A   			={
    "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 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       VOCAB_FILES_NAMES
			UpperCamelCase									=       PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase									=       ["""input_ids""", """attention_mask"""]
			UpperCamelCase									=       RobertaTokenizer




			def __init__(   self							: Optional[int]       ,       a_							: Dict=None       ,       a_							: Union[str, Any]=None       ,       a_							: str=None       ,       a_							: Tuple="replace"       ,       a_							: int="<s>"       ,       a_							: List[str]="</s>"       ,       a_							: Optional[int]="</s>"       ,       a_							: List[str]="<s>"       ,       a_							: List[str]="<unk>"       ,       a_							: str="<pad>"       ,       a_							: Dict="<mask>"       ,       a_							: Optional[int]=False       ,       a_							: Optional[int]=True       ,       **a_							: Any       ,       ):
					'''simple docstring'''





					super().__init__(
					    a_       ,       a_       ,       tokenizer_file=a_       ,       errors=a_       ,       bos_token=a_       ,       eos_token=a_       ,       sep_token=a_       ,       cls_token=a_       ,       unk_token=a_       ,       pad_token=a_       ,       mask_token=a_       ,       add_prefix_space=a_       ,       trim_offsets=a_       ,       **a_       ,       )

					__UpperCAmelCase    :     Tuple													=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
					if pre_tok_state.get('''add_prefix_space'''       ,       a_ ) != add_prefix_space:
							__UpperCAmelCase    :     Optional[Any]													=	getattr(a_       ,       pre_tok_state.pop('''type''' ) )
							__UpperCAmelCase    :     int													=	add_prefix_space
							__UpperCAmelCase    :     List[str]													=	pre_tok_class(**a_ )

					__UpperCAmelCase    :     Union[str, Any]													=	add_prefix_space

					__UpperCAmelCase    :     Any													=	'''post_processor'''
					__UpperCAmelCase    :     Any													=	getattr(self.backend_tokenizer       ,       a_       ,       a_ )
					if tokenizer_component_instance:
							__UpperCAmelCase    :     Optional[int]													=	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													=	tuple(state['''sep'''] )
							if "cls" in state:
									__UpperCAmelCase    :     Tuple													=	tuple(state['''cls'''] )

							__UpperCAmelCase    :     int													=	False

							if state.get('''add_prefix_space'''       ,       a_ ) != add_prefix_space:
									__UpperCAmelCase    :     str													=	add_prefix_space
									__UpperCAmelCase    :     Optional[Any]													=	True

							if state.get('''trim_offsets'''       ,       a_ ) != trim_offsets:
									__UpperCAmelCase    :     int													=	trim_offsets
									__UpperCAmelCase    :     Tuple													=	True

							if changes_to_apply:
									__UpperCAmelCase    :     List[str]													=	getattr(a_       ,       state.pop('''type''' ) )
									__UpperCAmelCase    :     List[str]													=	component_class(**a_ )
									setattr(self.backend_tokenizer       ,       a_       ,       a_ )




			@property
			def  snake_case__     (   self							: str ):
					'''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  snake_case__     (   self							: Dict       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	AddedToken(a_       ,       lstrip=a_       ,       rstrip=a_ ) if isinstance(a_       ,       a_ ) else value
					__UpperCAmelCase    :     Optional[Any]													=	value




			def  snake_case__     (   self							: Any       ,       *a_							: List[str]       ,       **a_							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	kwargs.get('''is_split_into_words'''       ,       a_ )
					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(*a_       ,       **a_ )




			def  snake_case__     (   self							: Tuple       ,       *a_							: List[Any]       ,       **a_							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	kwargs.get('''is_split_into_words'''       ,       a_ )

					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(*a_       ,       **a_ )




			def  snake_case__     (   self							: Any       ,       a_							: str       ,       a_							: Optional[str] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self._tokenizer.model.save(a_       ,       name=a_ )
					return tuple(a_ )




			def  snake_case__     (   self							: str       ,       a_							: Dict       ,       a_							: str=None ):
					'''simple docstring'''





					__UpperCAmelCase    :     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							: Any       ,       a_							: List[int]       ,       a_							: Optional[List[int]] = None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	[self.sep_token_id]
					__UpperCAmelCase    :     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 + sep + token_ids_a + sep ) * [0]










import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       None




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	self.feature_extraction_class(**self.feat_extract_dict )
					__UpperCAmelCase    :     Optional[int]													=	json.loads(feat_extract.to_json_string() )
					for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]       ,       a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(a_       ,       '''feat_extract.json''' )
							feat_extract_first.to_json_file(a_ )
							__UpperCAmelCase    :     Any													=	self.feature_extraction_class.from_json_file(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     List[str]													=	feat_extract_first.save_pretrained(a_ )[0]
							check_json_file_has_correct_format(a_ )
							__UpperCAmelCase    :     Optional[Any]													=	self.feature_extraction_class.from_pretrained(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.feature_extraction_class()
					self.assertIsNotNone(a_ )





