



'''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:
							a     :    List[Any]      						=					None

a     :    List[Any]      						=					logging.get_logger(__name__)
a     :    Tuple      						=					{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

a     :    Any      						=					{
    """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"""
        ),
    },
}

a     :    int      						=					{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


a     :    Tuple      						=					"""▁"""

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				BigBirdTokenizer
	lowercase    =				['input_ids', 'attention_mask']
	lowercase    =				[]





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

								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase  :	List[Any]							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else mask_token

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

								UpperCAmelCase  :	List[str]							  =	vocab_file
								UpperCAmelCase  :	Optional[Any]							  =	False if not self.vocab_file else True





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Optional[int]							  =	[self.sep_token_id]
								UpperCAmelCase  :	Tuple							  =	[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 				_lowercase(						self	,	A	,	A = None	,	A = 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(A					)) + [1]
								return [1] + ([0] * len(A					)) + [1] + ([0] * len(A					)) + [1]





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Dict							  =	[self.sep_token_id]
								UpperCAmelCase  :	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					) * [0] + len(token_ids_a + sep					) * [1]






	def 				_lowercase(						self	,	A	,	A = 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(A					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	Tuple							  =	os.path.join(
								    A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

								return (out_vocab_file,)






'''simple docstring'''




from collections.abc import Callable

import numpy as np



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    np.array:
							UpperCAmelCase  :	Optional[Any]							  =	int(np.ceil((x_end - xa) / step_size	)	)
							UpperCAmelCase  :	str							  =	np.zeros((n + 1,)	)
							UpperCAmelCase  :	Optional[Any]							  =	ya
							UpperCAmelCase  :	Union[str, Any]							  =	xa

							for k in range(_lowercase	):
														UpperCAmelCase  :	Dict							  =	y[k] + step_size * ode_func(_lowercase			,       y[k]	)
														UpperCAmelCase  :	Optional[int]							  =	y[k] + (
														    (step_size / 2) * (ode_func(_lowercase			,       y[k]	) + ode_func(x + step_size			,       _lowercase	))
														)
														x += step_size

							return y


if __name__ == "__main__":
							import doctest

							doctest.testmod()

