




"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


UpperCamelCase__ :    List[str] 					=							logging.get_logger(__name__)

UpperCamelCase__ :    Any 					=							{"""vocab_file""": """vocab.txt"""}

UpperCamelCase__ :    Optional[int] 					=							{
    """vocab_file""": {
        """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt""",
        """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt""",
        """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt""",
        """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt""",
    }
}

UpperCamelCase__ :    str 					=							{
    """tau/splinter-base""": 5_12,
    """tau/splinter-base-qass""": 5_12,
    """tau/splinter-large""": 5_12,
    """tau/splinter-large-qass""": 5_12,
}

UpperCamelCase__ :    Optional[Any] 					=							{
    """tau/splinter-base""": {"""do_lower_case""": False},
    """tau/splinter-base-qass""": {"""do_lower_case""": False},
    """tau/splinter-large""": {"""do_lower_case""": False},
    """tau/splinter-large-qass""": {"""do_lower_case""": False},
}
class     A__      (      a_     ):
	A_							:				Any						     =      VOCAB_FILES_NAMES
	A_							:				List[str]						     =      PRETRAINED_VOCAB_FILES_MAP
	A_							:				str						     =      PRETRAINED_INIT_CONFIGURATION
	A_							:				List[str]						     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_							:				Any						     =      SplinterTokenizer




	def __init__(   self  :	Dict      ,    lowercase  :	str=None      ,    lowercase  :	Union[str, Any]=None      ,    lowercase  :	int=True      ,    lowercase  :	int="[UNK]"      ,    lowercase  :	Optional[int]="[SEP]"      ,    lowercase  :	List[Any]="[PAD]"      ,    lowercase  :	Optional[Any]="[CLS]"      ,    lowercase  :	int="[MASK]"      ,    lowercase  :	Dict="[QUESTION]"      ,    lowercase  :	Optional[Any]=True      ,    lowercase  :	str=None      ,    **lowercase  :	Optional[Any]      ,    )					-> int:
						super().__init__(
						    _snake_case      ,    tokenizer_file=_snake_case      ,    do_lower_case=_snake_case      ,    unk_token=_snake_case      ,    sep_token=_snake_case      ,    pad_token=_snake_case      ,    cls_token=_snake_case      ,    mask_token=_snake_case      ,    tokenize_chinese_chars=_snake_case      ,    strip_accents=_snake_case      ,    additional_special_tokens=(question_token,)      ,    **_snake_case      ,    )

						__snake_case          =	json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
						if (
						    pre_tok_state.get("""lowercase"""      ,    _snake_case	) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""      ,    _snake_case	) != strip_accents
						):
											__snake_case          =	getattr(_snake_case      ,    pre_tok_state.pop("""type"""	)	)
											__snake_case          =	do_lower_case
											__snake_case          =	strip_accents
											__snake_case          =	pre_tok_class(**_snake_case	)

						__snake_case          =	do_lower_case




	@property
	def 							a_		(   self  :	Union[str, Any]	)					-> str:
						return self.convert_tokens_to_ids(self.question_token	)




	def 							a_		(   self  :	List[Any]      ,    lowercase  :	str      ,    lowercase  :	Dict = None	)					-> List[int]:
						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						__snake_case          =	[self.cls_token_id]
						__snake_case          =	[self.sep_token_id]
						__snake_case          =	[self.question_token_id] + [self.convert_tokens_to_ids("""."""	)]
						if self.padding_side == "right":
											# Input is question-then-context
											return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
						else:
											# Input is context-then-question
											return cls + token_ids_a + sep + token_ids_a + question_suffix + sep




	def 							a_		(   self  :	Optional[int]      ,    lowercase  :	str      ,    lowercase  :	str = None	)					-> List[int]:
						__snake_case          =	[self.sep_token_id]
						__snake_case          =	[self.cls_token_id]
						__snake_case          =	[self.question_token_id] + [self.convert_tokens_to_ids("""."""	)]
						if token_ids_a is None:
											return len(cls + token_ids_a + sep	) * [0]

						if self.padding_side == "right":
											# Input is question-then-context
											return len(cls + token_ids_a + question_suffix + sep	) * [0] + len(token_ids_a + sep	) * [1]
						else:
											# Input is context-then-question
											return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + question_suffix + sep	) * [1]




	def 							a_		(   self  :	Optional[Any]      ,    lowercase  :	Optional[Any]      ,    lowercase  :	str = None	)					-> Tuple[str]:
						__snake_case          =	self._tokenizer.model.save(_snake_case      ,    name=_snake_case	)
						return tuple(_snake_case	)












"""simple docstring"""

import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__ :    Optional[int] 					=							10_00_00_00_00_00
UpperCamelCase__ :    int 					=							1_00_00

UpperCamelCase__					,					UpperCamelCase__ :    List[str] 					=							os.path.split(__file__)
UpperCamelCase__ :    Any 					=							os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




def       snake_case__	(       lowerCAmelCase   :		int				,					lowerCAmelCase   :		int		):
					__snake_case          =	pa.Table.from_pydict({"""col""": [0] * chunk_size}		)
					__snake_case          =	pa.concat_tables([table] * (num_examples // chunk_size)		)
					return datasets.Dataset(lowerCAmelCase				,					fingerprint="""table_100B"""		)
@dataclass
class     A__      :
	A_							:				int
	A_							:				int
	A_							:				int
	A_							:				int




	def 							a_		(   self  :	Optional[Any]	)					-> Any:
						__snake_case          =	np.random.default_rng(self.seed	)
						__snake_case          =	rng.integers(low=self.low      ,    high=self.high      ,    size=self.size	).tolist()




	def __iter__(   self  :	str	)					-> int:
						return iter(self._sampled_values	)




	def __len__(   self  :	Union[str, Any]	)					-> Any:
						return self.size




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[0]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[-1]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[range(len(lowerCAmelCase		) // 2				,					len(lowerCAmelCase		) // 2 + 1_024		)]




@get_duration
def       snake_case__	(       lowerCAmelCase   :		datasets.Dataset		):
					__snake_case          =	dataset[RandIter(0				,					len(lowerCAmelCase		)				,					1_024				,					seed=42		)]




def       snake_case__	(       ):
					__snake_case          =	{"""num examples""": SPEED_TEST_N_EXAMPLES}
					__snake_case          =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("""generating dataset"""		)
					__snake_case          =	generate_100B_dataset(num_examples=lowerCAmelCase				,					chunk_size=lowerCAmelCase		)
					print("""Functions"""		)
					for func in functions:
										print(func.__name__		)
										__snake_case          =	func(lowerCAmelCase		)

					with open(lowerCAmelCase				,					"""wb"""		) as f:
										f.write(json.dumps(lowerCAmelCase		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()


