




import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase       :       Union[str, Any]        =    logging.getLogger()




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   int:
   snake_case      :				Any				=       {}
   snake_case      :				List[str]				=       os.path.join(__UpperCamelCase						,"""all_results.json"""     )
   if os.path.exists(__UpperCamelCase     ):
      with open(__UpperCamelCase						,"""r"""     ) as f:
         snake_case      :				int				=       json.load(__UpperCamelCase     )
   else:
      raise ValueError(f"""can\'t find {path}"""     )
   return results


lowerCamelCase       :       Tuple        =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




@require_torch_tpu
class 						__lowercase		(SCREAMING_SNAKE_CASE__							):




      """simple docstring"""





      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         import xla_spawn

         snake_case      :				Optional[int]				=       self.get_auto_remove_tmp_dir()
         snake_case      :				List[str]				=       f"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

         with patch.object(A				,      """argv"""				,      A	):
            snake_case      :				int				=       time()
            xla_spawn.main()
            snake_case      :				Optional[int]				=       time()

            snake_case      :				Dict				=       get_results(A	)
            self.assertGreaterEqual(result["""eval_accuracy"""]				,      0.75	)

            # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
            self.assertLess(end - start				,      5_0_0	)

      def 	UpperCAmelCase      (      self	)		->				Any:
         import xla_spawn

         snake_case      :				Union[str, Any]				=       """\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            """.split()
         with patch.object(A				,      """argv"""				,      A	):
            xla_spawn.main()






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  CpmAntTokenizer
  lowerCamelCase__   =  False







  def       __A   (							self       :				List[str]      )     ->		str:
        super().setUp()

        SCREAMING_SNAKE_CASE_      =     [
            "<d>",
            "</d>",
            "<s>",
            "</s>",
            "</_>",
            "<unk>",
            "<pad>",
            "</n>",
            "我",
            "是",
            "C",
            "P",
            "M",
            "A",
            "n",
            "t",
        ]
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







  @tooslow
  def       __A   (							self       :				int      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     ["今天", "天气", "真", "好", "！"]
        SCREAMING_SNAKE_CASE_      =     tokenizer.tokenize(__magic_name__      )
        self.assertListEqual(__magic_name__     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     "今天天气真好！"
        SCREAMING_SNAKE_CASE_      =     [tokenizer.bos_token] + tokens

        SCREAMING_SNAKE_CASE_      =     [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__      )     ,   __magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer.decode(__magic_name__      )
        self.assertEqual(__magic_name__     ,   __magic_name__      )


