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


__lowerCamelCase					:	Any       =  logging.get_logger(__name__)

__lowerCamelCase					:	Optional[Any]       =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase					:	Optional[Any]       =  {
    '''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'''
        ),
    },
}

__lowerCamelCase					:	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__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							['input_ids', 'attention_mask']
					A	       =							RobertaTokenizer

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







							"""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,)

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

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    add_prefix_space

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

									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    False

									if state.get("add_prefix_space",_A    ) != add_prefix_space:
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    add_prefix_space
											SCREAMING_SNAKE_CASE_  :		int				      =    True

									if state.get("trim_offsets",_A    ) != trim_offsets:
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    trim_offsets
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    True

									if changes_to_apply:
											SCREAMING_SNAKE_CASE_  :		int				      =    getattr(_A,state.pop("type"    )    )
											SCREAMING_SNAKE_CASE_  :		str				      =    component_class(**_A    )
											setattr(self.backend_tokenizer,_A,_A    )

					@property
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""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      :       Tuple,_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else value
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    value

					def 				__UpperCamelCase   (    self      :       Union[str, Any],*_A      :       int,**_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		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()._batch_encode_plus(*_A,**_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int],*_A      :       int,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    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 				__UpperCamelCase   (    self      :       int,_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._tokenizer.model.save(_A,name=_A    )
							return tuple(_A    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Optional[int],_A      :       List[Any]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    [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      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		Dict				      =    [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"""






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Dict:

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  List[str]												=       re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)")
							__lowerCAmelCase      :  List[Any]												=       input_file.read()
							__lowerCAmelCase      :  Any												=       regexp.search(_SCREAMING_SNAKE_CASE)

				return match

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Optional[Any]:

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  Any												=       re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,			re.DOTALL)
							__lowerCAmelCase      :  Optional[int]												=       input_file.read()
							# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
							__lowerCAmelCase      :  int												=       regexp.finditer(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Union[str, Any]												=       [match for match in matches if match is not None and match.group(1) is not None]
				return matches[0] if matches else None

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       Path("./datasets")
				__lowerCAmelCase      :  Optional[int]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       Path("./datasets")
				__lowerCAmelCase      :  Union[str, Any]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_print_statements(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""")