
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_A  					= logging.get_logger(__name__)

_A  					= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_A  					= {
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

_A  					= {
    "openai-gpt": 5_12,
}







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		VOCAB_FILES_NAMES
     UpperCAmelCase__				:							int           =		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__				:							int           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__				:							Union[str, Any]           =		["input_ids", "attention_mask"]
     UpperCAmelCase__				:							int           =		OpenAIGPTTokenizer





     def __init__(self      :    int				,    _A      :    Union[str, Any]=None				,    _A      :    Dict=None				,    _A      :    List[Any]=None				,    _A      :    Dict="<unk>"				,    **_A      :    str   )						->						List[Any]:
         super().__init__(_A				,    _A				,    tokenizer_file=_A				,    unk_token=_A				,    **_A   )





     @property
     def 		UpperCAmelCase(self      :    Tuple   )						->						Optional[Any]:
         return True





     def 		UpperCAmelCase(self      :    Any				,    _A      :    str				,    _A      :    Optional[str] = None   )						->						Tuple[str]:
         snake_case      =					self._tokenizer.model.save(_A				,    name=_A   )
         return tuple(_A   )



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

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



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

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

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


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

