
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)

_snake_case      :							int     	=    logging.getLogger()

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      {}
				lowercase         =      os.path.join(__magic_name__		,    "all_results.json"						)
				if os.path.exists(__magic_name__						):
								with open(__magic_name__		,    "r"						) as f:
												lowercase         =      json.load(__magic_name__						)
				else:
								raise ValueError(F'''can\'t find {path}'''						)
				return results


_snake_case      :							Union[str, Any]     	=    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[Any]:
										import xla_spawn

										lowercase         =      self.get_auto_remove_tmp_dir()
										lowercase         =      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(lowerCAmelCase__		,    "argv"		,    lowerCAmelCase__     ):
														lowercase         =      time()
														xla_spawn.main()
														lowercase         =      time()

														lowercase         =      get_results(lowerCAmelCase__     )
														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		,    500     )





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

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "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

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