


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

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


if is_sentencepiece_available():
   from .tokenization_xglm import XGLMTokenizer
else:
   _A									=		None


_A									=		logging.get_logger(__name__)

_A									=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_A									=		{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_A									=		{
    'facebook/xglm-564M': 2048,
}






class        UpperCAmelCase__			(   A_						):



    """simple docstring"""






    UpperCAmelCase__		:						int    =    VOCAB_FILES_NAMES
    UpperCAmelCase__		:						Any    =    PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__		:						Union[str, Any]    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__		:						Tuple    =    ["input_ids", "attention_mask"]
    UpperCAmelCase__		:						Dict    =    XGLMTokenizer






    def __init__(     self   ,  A_=None   ,  A_=None   ,  A_="<s>"   ,  A_="</s>"   ,  A_="</s>"   ,  A_="<s>"   ,  A_="<unk>"   ,  A_="<pad>"   ,  **A_   ,  )    ->			Optional[int]:
         # Compatibility with the original tokenizer
         __UpperCamelCase       				=7
         __UpperCamelCase       				=[f'<madeupword{i}>' for i in range(self.num_madeup_words       )]

         __UpperCamelCase       				=kwargs.get('additional_special_tokens'   ,  []       )
         kwargs["additional_special_tokens"] += [
             word for word in madeup_words if word not in kwargs["additional_special_tokens"]
         ]

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

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






    def _a   (     self   ,  A_   ,  A_ = None       )    ->			List[int]:

         if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
         __UpperCamelCase       				=[self.sep_token_id]
         return sep + token_ids_a + sep + sep + token_ids_a






    def _a   (     self   ,  A_   ,  A_ = None       )    ->			List[int]:
         __UpperCamelCase       				=[self.sep_token_id]

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






    def _a   (     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       				=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,)









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

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

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=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_indices_mapping()





