



'''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)

UpperCamelCase__ :			Optional[int]												=				logging.getLogger()


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


UpperCamelCase__ :			List[Any]												=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






@require_torch_tpu
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					import xla_spawn

					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_auto_remove_tmp_dir()
					__SCREAMING_SNAKE_CASE :	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(_UpperCAmelCase							,  """argv"""							,  _UpperCAmelCase		):
							__SCREAMING_SNAKE_CASE :	str					 =	time()
							xla_spawn.main()
							__SCREAMING_SNAKE_CASE :	int					 =	time()

							__SCREAMING_SNAKE_CASE :	List[Any]					 =	get_results(_UpperCAmelCase		)
							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  :				Union[str, Any]		):





					"""simple docstring"""






					import xla_spawn

					__SCREAMING_SNAKE_CASE :	Tuple					 =	"""\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            """.split()
					with patch.object(_UpperCAmelCase							,  """argv"""							,  _UpperCAmelCase		):
							xla_spawn.main()










'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			Optional[int]												=				{'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			Tuple												=				[
						    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''WavLMForAudioFrameClassification''',
						    '''WavLMForCTC''',
						    '''WavLMForSequenceClassification''',
						    '''WavLMForXVector''',
						    '''WavLMModel''',
						    '''WavLMPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			List[str]												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)