

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    :Tuple      =			logging.get_logger(__name__)

__a    :Tuple      =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__a    :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'
        ),
    },
}

__a    :Union[str, Any]      =			{
    '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					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Tuple      		=       VOCAB_FILES_NAMES
      _lowerCamelCase       :			int      		=       PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase       :			int      		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase       :			Optional[Any]      		=       ['input_ids', 'attention_mask']
      _lowerCamelCase       :			Tuple      		=       RobertaTokenizer





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

            A_						  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
            if pre_tok_state.get("add_prefix_space"						,			UpperCAmelCase	) != add_prefix_space:
                  A_						  =				getattr(UpperCAmelCase						,			pre_tok_state.pop("type"	)	)
                  A_						  =				add_prefix_space
                  A_						  =				pre_tok_class(**UpperCAmelCase	)

            A_						  =				add_prefix_space

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

                  A_						  =				False

                  if state.get("add_prefix_space"						,			UpperCAmelCase	) != add_prefix_space:
                        A_						  =				add_prefix_space
                        A_						  =				True

                  if state.get("trim_offsets"						,			UpperCAmelCase	) != trim_offsets:
                        A_						  =				trim_offsets
                        A_						  =				True

                  if changes_to_apply:
                        A_						  =				getattr(UpperCAmelCase						,			state.pop("type"	)	)
                        A_						  =				component_class(**UpperCAmelCase	)
                        setattr(self.backend_tokenizer						,			UpperCAmelCase						,			UpperCAmelCase	)





      @property
      def 		__A	(   self							:				Tuple	):
            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							:				List[Any]						,			UpperCAmelCase							:				str	):
            A_						  =				AddedToken(UpperCAmelCase						,			lstrip=UpperCAmelCase						,			rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase						,			UpperCAmelCase	) else value
            A_						  =				value





      def 		__A	(   self							:				Tuple						,			*UpperCAmelCase							:				Tuple						,			**UpperCAmelCase							:				List[str]	):
            A_						  =				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 		__A	(   self							:				Tuple						,			*UpperCAmelCase							:				Dict						,			**UpperCAmelCase							:				str	):
            A_						  =				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 		__A	(   self							:				Any						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				Optional[str] = None	):
            A_						  =				self._tokenizer.model.save(UpperCAmelCase						,			name=UpperCAmelCase	)
            return tuple(UpperCAmelCase	)





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Union[str, Any]=None	):
            A_						  =				[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							:				Optional[int]						,			UpperCAmelCase							:				List[int]						,			UpperCAmelCase							:				Optional[List[int]] = None	):
            A_						  =				[self.sep_token_id]
            A_						  =				[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]







from typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


