






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_xlnet import XLNetTokenizer
else:
							A__			:    Dict         =				None


A__			:    int         =				logging.get_logger(__name__)

A__			:    int         =				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__			:    str         =				{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

A__			:    Any         =				{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

A__			:    Optional[int]         =				'▁'

# Segments (not really needed)
A__			:    Tuple         =				0
A__			:    Tuple         =				1
A__			:    Dict         =				2
A__			:    Any         =				3
A__			:    Any         =				4
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= """left"""
				lowercase__       							= XLNetTokenizer




				def __init__(  self		:					Optional[Any],						lowerCamelCase		:					Tuple=None,						lowerCamelCase		:					Union[str, Any]=None,						lowerCamelCase		:					Tuple=False,						lowerCamelCase		:					List[Any]=True,						lowerCamelCase		:					Optional[int]=False,						lowerCamelCase		:					Optional[int]="<s>",						lowerCamelCase		:					Any="</s>",						lowerCamelCase		:					Dict="<unk>",						lowerCamelCase		:					Optional[Any]="<sep>",						lowerCamelCase		:					Optional[Any]="<pad>",						lowerCamelCase		:					List[Any]="<cls>",						lowerCamelCase		:					Optional[int]="<mask>",						lowerCamelCase		:					List[Any]=["<eop>", "<eod>"],						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								# Mask token behave like a normal word, i.e. include the space before it
								lowercase__     				=    AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase						) if isinstance(lowerCamelCase,						lowerCamelCase						) else mask_token

								super().__init__(
								    vocab_file=lowerCamelCase,						tokenizer_file=lowerCamelCase,						do_lower_case=lowerCamelCase,						remove_space=lowerCamelCase,						keep_accents=lowerCamelCase,						bos_token=lowerCamelCase,						eos_token=lowerCamelCase,						unk_token=lowerCamelCase,						sep_token=lowerCamelCase,						pad_token=lowerCamelCase,						cls_token=lowerCamelCase,						mask_token=lowerCamelCase,						additional_special_tokens=lowerCamelCase,						**lowerCamelCase,						)

								lowercase__     				=    3
								lowercase__     				=    do_lower_case
								lowercase__     				=    remove_space
								lowercase__     				=    keep_accents
								lowercase__     				=    vocab_file
								lowercase__     				=    False if not self.vocab_file else True




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[int],						lowerCamelCase		:					Optional[List[int]] = None						):

								'''simple docstring'''
								lowercase__     				=    [self.sep_token_id]
								lowercase__     				=    [self.cls_token_id]
								if token_ids_a is None:
												return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[int],						lowerCamelCase		:					Optional[List[int]] = None						):

								'''simple docstring'''
								lowercase__     				=    [self.sep_token_id]
								lowercase__     				=    [2]

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




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[str] = None						):

								'''simple docstring'''
								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
								lowercase__     				=    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,)












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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




