




'''simple docstring'''


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_longformer import LongformerTokenizer


_snake_case      :      Optional[int]   							=		logging.get_logger(__name__)

_snake_case      :      Union[str, Any]   							=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case      :      List[Any]   							=		{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case      :      str   							=		{
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				["input_ids", "attention_mask"]
							lowercase_							=				LongformerTokenizer







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


														"""simple docstring"""


														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__							,			)

														_a	     =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
														if pre_tok_state.get('''add_prefix_space'''							,			lowercase__					) != add_prefix_space:
																					_a	     =				getattr(lowercase__							,			pre_tok_state.pop('''type'''					)					)
																					_a	     =				add_prefix_space
																					_a	     =				pre_tok_class(**lowercase__					)

														_a	     =				add_prefix_space

														_a	     =				'''post_processor'''
														_a	     =				getattr(self.backend_tokenizer							,			lowercase__							,			lowercase__					)
														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'''							,			lowercase__					) != add_prefix_space:
																												_a	     =				add_prefix_space
																												_a	     =				True

																					if state.get('''trim_offsets'''							,			lowercase__					) != trim_offsets:
																												_a	     =				trim_offsets
																												_a	     =				True

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







							@property
							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							str:


														"""simple docstring"""


														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    __lowerCAmelCase			(  self       :  str							,			lowerCAmelCase_       :  Union[str, Any]					)			->							Tuple:


														"""simple docstring"""


														_a	     =				AddedToken(lowercase__							,			lstrip=lowercase__							,			rstrip=lowercase__					) if isinstance(lowercase__							,			lowercase__					) else value
														_a	     =				value







							def    __lowerCAmelCase			(  self       :  Dict							,			*lowerCAmelCase_       :  Any							,			**lowerCAmelCase_       :  int					)			->							BatchEncoding:


														"""simple docstring"""


														_a	     =				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    __lowerCAmelCase			(  self       :  List[Any]							,			*lowerCAmelCase_       :  Any							,			**lowerCAmelCase_       :  Dict					)			->							BatchEncoding:


														"""simple docstring"""


														_a	     =				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    __lowerCAmelCase			(  self       :  List[str]							,			lowerCAmelCase_       :  Any							,			lowerCAmelCase_       :  Tuple = None					)			->							Tuple[str]:


														"""simple docstring"""


														_a	     =				self._tokenizer.model.save(lowercase__							,			name=lowercase__					)
														return tuple(lowercase__					)







							def    __lowerCAmelCase			(  self       :  Dict							,			lowerCAmelCase_       :  List[str]							,			lowerCAmelCase_       :  Dict=None					)			->							Optional[Any]:


														"""simple docstring"""


														_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    __lowerCAmelCase			(  self       :  Tuple							,			lowerCAmelCase_       :  Optional[Any]							,			lowerCAmelCase_       :  List[str] = None					)			->							List[int]:


														"""simple docstring"""


														_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]



"""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


snake_case    =		1_0_0_0_0_0_0_0_0_0_0_0
snake_case    =		1_0_0_0_0

snake_case   ,			snake_case    =		os.path.split(__file__)
snake_case    =		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	pa.Table.from_pydict({'col': [0] * chunk_size}			)
					SCREAMING_SNAKE_CASE				=	pa.concat_tables([table] * (num_examples // chunk_size)			)
					return datasets.Dataset(SCREAMING_SNAKE_CASE_,     fingerprint='table_100B'			)




@dataclass
class      UpperCamelCase	:

				"""simple docstring"""



				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	np.random.default_rng(self.seed			)
									SCREAMING_SNAKE_CASE				=	rng.integers(low=self.low		,	high=self.high		,	size=self.size			).tolist()


				def __iter__(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									return iter(self._sampled_values			)




				def __len__(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									return self.size






@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[0]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[-1]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[range(len(SCREAMING_SNAKE_CASE_			) // 2,     len(SCREAMING_SNAKE_CASE_			) // 2 + 1_0_2_4			)]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[RandIter(0,     len(SCREAMING_SNAKE_CASE_			),     1_0_2_4,     seed=4_2			)]

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	{'num examples': SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('generating dataset'			)
					SCREAMING_SNAKE_CASE				=	generate_100B_dataset(num_examples=SCREAMING_SNAKE_CASE_,     chunk_size=SCREAMING_SNAKE_CASE_			)
					print('Functions'			)
					for func in functions:
										print(func.__name__			)
										SCREAMING_SNAKE_CASE				=	func(SCREAMING_SNAKE_CASE_			)

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


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