


'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_big_bird import BigBirdTokenizer
else:
		__UpperCAmelCase       							=				None

__UpperCAmelCase       							=				logging.get_logger(__name__)
__UpperCAmelCase       							=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCAmelCase       							=				{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCAmelCase       							=				{
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}


__UpperCAmelCase       							=				'''▁'''




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Dict      		= VOCAB_FILES_NAMES
							lowercase__     :  Tuple      		= PRETRAINED_VOCAB_FILES_MAP
							lowercase__     :  Union[str, Any]      		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__     :  List[str]      		= BigBirdTokenizer
							lowercase__     :  Union[str, Any]      		= ["input_ids", "attention_mask"]
							lowercase__     :  List[int]      		= []







							def __init__(						self   ,       lowerCamelCase_=None   ,       lowerCamelCase_=None   ,       lowerCamelCase_="<unk>"   ,       lowerCamelCase_="<s>"   ,       lowerCamelCase_="</s>"   ,       lowerCamelCase_="<pad>"   ,       lowerCamelCase_="[SEP]"   ,       lowerCamelCase_="[MASK]"   ,       lowerCamelCase_="[CLS]"   ,       **lowerCamelCase_   ,       )			->				Any:
														lowerCAmelCase__			  =      AddedToken(lowerCamelCase_   ,       lstrip=lowerCamelCase_   ,       rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ) else bos_token
														lowerCAmelCase__			  =      AddedToken(lowerCamelCase_   ,       lstrip=lowerCamelCase_   ,       rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ) else eos_token
														lowerCAmelCase__			  =      AddedToken(lowerCamelCase_   ,       lstrip=lowerCamelCase_   ,       rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ) else unk_token
														lowerCAmelCase__			  =      AddedToken(lowerCamelCase_   ,       lstrip=lowerCamelCase_   ,       rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ) else pad_token
														lowerCAmelCase__			  =      AddedToken(lowerCamelCase_   ,       lstrip=lowerCamelCase_   ,       rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ) else cls_token
														lowerCAmelCase__			  =      AddedToken(lowerCamelCase_   ,       lstrip=lowerCamelCase_   ,       rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ) else sep_token

														# Mask token behave like a normal word, i.e. include the space before it
														lowerCAmelCase__			  =      AddedToken(lowerCamelCase_   ,       lstrip=lowerCamelCase_   ,       rstrip=lowerCamelCase_  ) if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ) else mask_token

														super().__init__(
														    lowerCamelCase_   ,       tokenizer_file=lowerCamelCase_   ,       bos_token=lowerCamelCase_   ,       eos_token=lowerCamelCase_   ,       unk_token=lowerCamelCase_   ,       sep_token=lowerCamelCase_   ,       pad_token=lowerCamelCase_   ,       cls_token=lowerCamelCase_   ,       mask_token=lowerCamelCase_   ,       **lowerCamelCase_   ,       )

														lowerCAmelCase__			  =      vocab_file
														lowerCAmelCase__			  =      False if not self.vocab_file else True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.sep_token_id]
														lowerCAmelCase__			  =      [self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = False  )			->				List[int]:

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''  )
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

														if token_ids_a is None:
																					return [1] + ([0] * len(lowerCamelCase_  )) + [1]
														return [1] + ([0] * len(lowerCamelCase_  )) + [1] + ([0] * len(lowerCamelCase_  )) + [1]







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.sep_token_id]
														lowerCAmelCase__			  =      [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  ) * [0] + len(token_ids_a + sep  ) * [1]







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				Tuple[str]:
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																					    '''tokenizer.'''  )

														if not os.path.isdir(lowerCamelCase_  ):
																					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
																					return
														lowerCAmelCase__			  =      os.path.join(
														    lowerCamelCase_   ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

														if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowerCamelCase_  ):
																					copyfile(self.vocab_file   ,       lowerCamelCase_  )

														return (out_vocab_file,)



'''simple docstring'''






from collections.abc import Callable

import numpy as np


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       np.array:
							lowerCAmelCase__			  =      int(np.ceil((x_end - xa) / step_size			)			)
							lowerCAmelCase__			  =      np.zeros((n + 1,)			)
							lowerCAmelCase__			  =      ya
							lowerCAmelCase__			  =      xa

							for k in range(A			):
														lowerCAmelCase__			  =      y[k] + step_size * ode_func(A					,       y[k]			)
														lowerCAmelCase__			  =      y[k] + (
														    (step_size / 2) * (ode_func(A					,       y[k]			) + ode_func(x + step_size					,       A			))
														)
														x += step_size

							return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()