




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		field(default='image-classification'	,  metadata={'include_in_asdict_even_if_is_default': True}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'image': Image()}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'labels': ClassLabel}   )
	__magic_name__   :       str      	=		"image"
	__magic_name__   :       str      	=		"labels"






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Optional[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						if self.label_column not in features:
											raise ValueError(F'''Column {self.label_column} is not present in features.'''	)
						if not isinstance(features[self.label_column]					,     lowerCAmelCase	):
											raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''	)
						__UpperCamelCase					:	List[str]             =      copy.deepcopy(self	)
						__UpperCamelCase					:	List[Any]             =      self.label_schema.copy()
						__UpperCamelCase					:	List[str]             =      features[self.label_column]
						__UpperCamelCase					:	List[str]             =      label_schema
						return task_template






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Dict[str, str]:







						"""simple docstring"""







						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}












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)

a__		     =     logging.getLogger()





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


a__		     =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       Dict:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	Dict             =      self.get_auto_remove_tmp_dir()
						__UpperCamelCase					:	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(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Union[str, Any]             =      time()
											xla_spawn.main()
											__UpperCamelCase					:	List[str]             =      time()

											__UpperCamelCase					:	Optional[int]             =      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 					lowerCamelCase__     (					self		:						int	)	->       List[Any]:







						"""simple docstring"""







						import xla_spawn

						__UpperCamelCase					:	List[Any]             =      """
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            """.split()
						with patch.object(lowerCAmelCase					,     """argv"""					,     lowerCAmelCase	):
											xla_spawn.main()






