



"""simple docstring"""







# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__		:				Optional[Any]          =	{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				List[Any]          =	[
         """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """CpmAntForCausalLM""",
         """CpmAntModel""",
         """CpmAntPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
     from .tokenization_cpmant import CpmAntTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_cpmant import (
              CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
              CpmAntForCausalLM,
              CpmAntModel,
              CpmAntPreTrainedModel,
          )


else:
     import sys

     lowercase__		:				str          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""







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)

lowercase__		:				List[str]          =	logging.getLogger()

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				Dict								= {}
  lowerCAmelCase_   :				Union[str, Any]								= os.path.join(lowerCAmelCase__    ,   'all_results.json'						)
  if os.path.exists(lowerCAmelCase__						):
    with open(lowerCAmelCase__    ,   'r'						) as f:
      lowerCAmelCase_   :				Any								= json.load(lowerCAmelCase__						)
  else:
    raise ValueError(f"can't find {path}"						)
  return results


lowercase__		:				Optional[int]          =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        import xla_spawn

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

        with patch.object(SCREAMING_SNAKE_CASE_    ,			'argv'    ,			SCREAMING_SNAKE_CASE_       ):
          lowerCAmelCase_   :				Optional[Any]								= time()
          xla_spawn.main()
          lowerCAmelCase_   :				Any								= time()

          lowerCAmelCase_   :				Dict								= get_results(SCREAMING_SNAKE_CASE_       )
          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      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        import xla_spawn

        lowerCAmelCase_   :				Dict								= '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
        with patch.object(SCREAMING_SNAKE_CASE_    ,			'argv'    ,			SCREAMING_SNAKE_CASE_       ):
          xla_spawn.main()





