'''simple docstring'''

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_lowerCamelCase :				Optional[Any]														= logging.get_logger(__name__)


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset

      from transformers import Trainer




      class    lowerCamelCase__  (   SCREAMING_SNAKE_CASE_     ):



         def __init__(    self					,      lowerCAmelCase__ = 101     )				->     Any:
              """simple docstring"""





              _UpperCamelCase  :Dict       =length



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





              return self.length




         def __getitem__(    self					,      lowerCAmelCase__     )				->     int:
              """simple docstring"""





              return i




      class    lowerCamelCase__  :



         def __call__(    self					,      lowerCAmelCase__     )				->     Any:
              """simple docstring"""





              return {"input_ids": torch.tensor(__UpperCAmelCase     ), "labels": torch.tensor(__UpperCAmelCase     )}




      class    lowerCamelCase__  (   nn.Module     ):



         def __init__(    self     )				->     Any:
              """simple docstring"""





              super().__init__()
              # Add some (unused) params otherwise DDP will complain.
              _UpperCamelCase  :Tuple       =nn.Linear(120					,      80     )




         def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None     )				->     List[str]:
              """simple docstring"""





              if labels is not None:
                   return torch.tensor(0.0					,      device=input_ids.device     ), input_ids
              else:
                   return input_ids



def  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :str       =HfArgumentParser((TrainingArguments,)      )
     sys.argv += ["--output_dir", "./examples"]
     _UpperCamelCase  :List[Any]       =parser.parse_args_into_dataclasses()[0]

     logger.warning(
         F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
         F'''tpu_num_cores: {training_args.tpu_num_cores}'''			,      )

     # Essentially, what we want to verify in the distributed case is
     # that we get all samples back, in the right order.
     # (this is crucial for prediction for instance)
     for dataset_length in [10_01, 2_56, 15]:
          _UpperCamelCase  :Tuple       =DummyDataset(__a      )

          def compute_metrics(__a      ) -> Dict:
               _UpperCamelCase  :int       =list(range(len(__a      )      )      )
               _UpperCamelCase  :int       =p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
               return {"success": success}

          _UpperCamelCase  :Union[str, Any]       =Trainer(
              model=DummyModel()			,      args=__a			,      data_collator=DummyDataCollator()			,      eval_dataset=__a			,      compute_metrics=__a			,      )
          _UpperCamelCase  :Any       =trainer.evaluate()
          logger.info(__a      )
          if metrics["eval_success"] is not True:
               logger.error(__a      )
               exit(1      )

          _UpperCamelCase  :Union[str, Any]       =trainer.predict(__a      )
          logger.info(p.metrics      )
          if p.metrics["test_success"] is not True:
               logger.error(p.metrics      )
               exit(1      )

          _UpperCamelCase  :Any       =2

          _UpperCamelCase  :int       =trainer.evaluate()
          logger.info(__a      )
          if metrics["eval_success"] is not True:
               logger.error(__a      )
               exit(1      )

          _UpperCamelCase  :List[Any]       =trainer.predict(__a      )
          logger.info(p.metrics      )
          if p.metrics["test_success"] is not True:
               logger.error(p.metrics      )
               exit(1      )

          _UpperCamelCase  :int       =None

     logger.info("""🔥 All distributed tests successful"""      )



def  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     main()


if __name__ == "__main__":
      main()

"""simple docstring"""






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 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Optional[int]     					=     GPTNeoXJapaneseTokenizer
   a_					:		Dict     					=     False
   a_					:		Union[str, Any]     					=     {"""do_clean_text""": False, """add_prefix_space""": False}







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      super().setUp()

      a_						   =     [
          "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|>",
      ]
      a_						   =     {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
      a_						   =     {"unk_token": "<unk>"}

      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["vocab_file"])
      a_						   =     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(__UpperCAmelCase))







   def    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->int:
      kwargs.update(self.special_tokens_map)
      return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Dict:
      a_						   =     "こんにちは、世界。 \nこんばんは、㔺界。😀"
      a_						   =     "こんにちは、世界。 \nこんばんは、世界。😀"
      return input_text, output_text







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_   ,			a_						   =     self.get_input_output_texts(__UpperCAmelCase)
      a_						   =     tokenizer.encode(__UpperCAmelCase       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.decode(__UpperCAmelCase       ,							clean_up_tokenization_spaces=__UpperCAmelCase)
      return text, ids







   def    UpperCAmelCase__	( self)   ->Any:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->int:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->List[str]:
      a_						   =     self.get_tokenizer()

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

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

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







   @slow
   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")

      a_						   =     tokenizer.encode("ありがとう。"       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.encode("どういたしまして。"       ,							add_special_tokens=__UpperCAmelCase)

      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase)
      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase       ,							__UpperCAmelCase)

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







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Intentionally convert some words to accommodate character fluctuations unique to Japanese
      pass







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      # tokenizer has no padding token
      pass