






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': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}


class 							__a							(  _UpperCAmelCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Any]				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Any				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Optional[Any]				    =						["""input_ids""", """attention_mask"""]
		UpperCAmelCase__      :				Optional[Any]				    =						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__ ,    ):
						super().__init__(
						    lowercase__ ,    lowercase__ ,    tokenizer_file=lowercase__ ,    errors=lowercase__ ,    bos_token=lowercase__ ,    eos_token=lowercase__ ,    sep_token=lowercase__ ,    cls_token=lowercase__ ,    unk_token=lowercase__ ,    pad_token=lowercase__ ,    mask_token=lowercase__ ,    add_prefix_space=lowercase__ ,    trim_offsets=lowercase__ ,    **lowercase__ ,    )

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

						SCREAMING_SNAKE_CASE_   :	int													=   add_prefix_space

						SCREAMING_SNAKE_CASE_   :	Dict													=   """post_processor"""
						SCREAMING_SNAKE_CASE_   :	Dict													=   getattr(self.backend_tokenizer ,    lowercase__ ,    lowercase__       )
						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_   :	Dict													=   tuple(state['sep']       )
										if "cls" in state:
														SCREAMING_SNAKE_CASE_   :	Dict													=   tuple(state['cls']       )

										SCREAMING_SNAKE_CASE_   :	List[Any]													=   False

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

										if state.get('trim_offsets' ,    lowercase__       ) != trim_offsets:
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   trim_offsets
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   True

										if changes_to_apply:
														SCREAMING_SNAKE_CASE_   :	Dict													=   getattr(lowercase__ ,    state.pop('type'       )       )
														SCREAMING_SNAKE_CASE_   :	Any													=   component_class(**lowercase__       )
														setattr(self.backend_tokenizer ,    lowercase__ ,    lowercase__       )


		@property
		def   __snake_case			(						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   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AddedToken(lowercase__ ,    lstrip=lowercase__ ,    rstrip=lowercase__       ) if isinstance(lowercase__ ,    lowercase__       ) else value
						SCREAMING_SNAKE_CASE_   :	Any													=   value


		def   __snake_case			(						self ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   kwargs.get('is_split_into_words' ,    lowercase__       )
						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(*lowercase__ ,    **lowercase__       )


		def   __snake_case			(						self ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   kwargs.get('is_split_into_words' ,    lowercase__       )

						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(*lowercase__ ,    **lowercase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self._tokenizer.model.save(lowercase__ ,    name=lowercase__       )
						return tuple(lowercase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, 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 ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE_   :	int													=   [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 math



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return math.pow(lowerCAmelCase__    ,    2      ) - a



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return 2 * x



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   2.0

				while start <= a:
								SCREAMING_SNAKE_CASE_   :	str													=   math.pow(lowerCAmelCase__    ,    2      )

				return start



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int = 9999    ,    lowerCAmelCase__				:		float = 0.00_000_000_000_001      ):





				'''simple docstring'''







				if a < 0:
								raise ValueError('math domain error'      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_initial_point(lowerCAmelCase__      )

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   value
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   value - fx(lowerCAmelCase__    ,    lowerCAmelCase__      ) / fx_derivative(lowerCAmelCase__      )
								if abs(prev_value - value      ) < tolerance:
												return value

				return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()