import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   GPTNeoXJapaneseTokenizer
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   {'do_clean_text': False, 'add_prefix_space': False}




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        super().setUp()

        lowerCAmelCase__	      =   [
            'こん',
            'こんに',
            'にちは',
            'ばんは',
            '世界,㔺界',
            '、',
            '。',
            '<BR>',
            '<SP>',
            '<TAB>',
            '<URL>',
            '<EMAIL>',
            '<TEL>',
            '<DATE>',
            '<PRICE>',
            '<BLOCK>',
            '<KIGOU>',
            '<U2000U2BFF>',
            '<|emoji1|>',
            '<unk>',
            '<|startoftext|>',
            '<|endoftext|>',
        ]
        lowerCAmelCase__	      =   {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
        lowerCAmelCase__	      =   {'unk_token': '<unk>'}

        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['vocab_file'])
        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['emoji_file'])
        with open(self.vocab_file     ,       'w'     ,       encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
        with open(self.emoji_file     ,       'w') as emoji_writer:
               emoji_writer.write(json.dumps(lowercase__))




 def 						__snake_case		(     self   :    Tuple     ,       **lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        kwargs.update(self.special_tokens_map)
        return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname     ,       **lowercase__)




 def 						__snake_case		(     self   :    List[str]     ,       lowercase__   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'こんにちは、世界。 \nこんばんは、㔺界。😀'
        lowerCAmelCase__	      =   'こんにちは、世界。 \nこんばんは、世界。😀'
        return input_text, output_text




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.get_input_output_texts(lowercase__)
        lowerCAmelCase__	      =   tokenizer.encode(lowercase__     ,       add_special_tokens=lowercase__)
        lowerCAmelCase__	      =   tokenizer.decode(lowercase__     ,       clean_up_tokenization_spaces=lowercase__)
        return text, ids




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        pass  # TODO add if relevant




 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        pass  # TODO add if relevant




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        pass  # TODO add if relevant




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.get_tokenizer()

        # Testing tokenization
        lowerCAmelCase__	      =   'こんにちは、世界。　こんばんは、㔺界。'
        lowerCAmelCase__	      =   ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
        lowerCAmelCase__	      =   tokenizer.tokenize(lowercase__)
        self.assertListEqual(lowercase__     ,       lowercase__)

        # Testing conversion to ids without special tokens
        lowerCAmelCase__	      =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
        lowerCAmelCase__	      =   tokenizer.convert_tokens_to_ids(lowercase__)
        self.assertListEqual(lowercase__     ,       lowercase__)

        # Testing conversion to ids with special tokens
        lowerCAmelCase__	      =   tokens + [tokenizer.unk_token]
        lowerCAmelCase__	      =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
        lowerCAmelCase__	      =   tokenizer.convert_tokens_to_ids(lowercase__)
        self.assertListEqual(lowercase__     ,       lowercase__)




 @slow
 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b')

        lowerCAmelCase__	      =   tokenizer.encode('ありがとう。'     ,       add_special_tokens=lowercase__)
        lowerCAmelCase__	      =   tokenizer.encode('どういたしまして。'     ,       add_special_tokens=lowercase__)

        lowerCAmelCase__	      =   tokenizer.build_inputs_with_special_tokens(lowercase__)
        lowerCAmelCase__	      =   tokenizer.build_inputs_with_special_tokens(lowercase__     ,       lowercase__)

        assert encoded_sentence == ids_a
        assert encoded_pair == ids_a + ids_a




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        pass

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   ArgumentParser(
           description=(
               'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
           )      )

       # Optional arguments for the launch helper
       parser.add_argument('--num_cores'      ,    type=lowerCAmelCase__      ,    default=1      ,    help='Number of TPU cores to use (1 or 8).'      )

       # positional
       parser.add_argument(
           'training_script'      ,    type=lowerCAmelCase__      ,    help=(
               'The full path to the single TPU training '
               'program/script to be launched in parallel, '
               'followed by all the arguments for the '
               'training script'
           )      ,    )

       # rest from the training program
       parser.add_argument('training_script_args'      ,    nargs=lowerCAmelCase__      )

       return parser.parse_args()






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   parse_args()

       # Import training_script as a module.
       lowerCAmelCase__	      =   Path(args.training_script      )
       sys.path.append(str(script_fpath.parent.resolve()      )      )
       lowerCAmelCase__	      =   script_fpath.stem
       lowerCAmelCase__	      =   importlib.import_module(lowerCAmelCase__      )

       # Patch sys.argv
       lowerCAmelCase__	      =   [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

       xmp.spawn(mod._mp_fn      ,    args=()      ,    nprocs=args.num_cores      )


if __name__ == "__main__":
    main()
