



import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)







"""simple docstring"""




import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__UpperCamelCase						=      [
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None				)     ->							int:
								SCREAMING_SNAKE_CASE													=					None
								SCREAMING_SNAKE_CASE													=					os.path.abspath(os.path.join('examples'						,       'by_feature'				)				)
								SCREAMING_SNAKE_CASE													=					os.path.abspath('examples'				)
								for item in os.listdir(lowerCAmelCase__				):
														if item not in EXCLUDE_EXAMPLES:
																				SCREAMING_SNAKE_CASE													=					os.path.join(lowerCAmelCase__						,       lowerCAmelCase__				)
																				if os.path.isfile(lowerCAmelCase__				) and ".py" in item_path:
																										with self.subTest(
																										    tested_script=lowerCAmelCase__						,       feature_script=lowerCAmelCase__						,       tested_section='main()' if parser_only else 'training_function()'						,       ):
																																SCREAMING_SNAKE_CASE													=					compare_against_test(
																																    os.path.join(lowerCAmelCase__						,       lowerCAmelCase__				)						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)
																																SCREAMING_SNAKE_CASE													=					'\n'.join(lowerCAmelCase__				)
																																if special_strings is not None:
																																						for string in special_strings:
																																												SCREAMING_SNAKE_CASE													=					diff.replace(lowerCAmelCase__						,       ''				)
																																self.assertEqual(lowerCAmelCase__						,       ''				)







		def 							__A						(	self				)     ->							Optional[int]:
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					os.path.abspath(os.path.join('examples'						,       'cv_example.py'				)				)
								SCREAMING_SNAKE_CASE													=					[
								    ' ' * 16 + '{\n\n',
								    ' ' * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
								    ' ' * 20 + '"f1": eval_metric["f1"],\n\n',
								    ' ' * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
								    ' ' * 20 + '"epoch": epoch,\n\n',
								    ' ' * 16 + '},\n\n',
								    ' ' * 16 + 'step=epoch,\n',
								    ' ' * 12,
								    ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
								]
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)





@mock.patch.dict(os.environ	,    {"""TESTING_MOCKED_DATALOADERS""": """1"""}    )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[str]								=     False







		@classmethod
		def 							__A						(	cls				)     ->							List[str]:
								super().setUpClass()
								SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()
								SCREAMING_SNAKE_CASE													=					os.path.join(cls._tmpdir						,       'default_config.yml'				)

								write_basic_config(save_location=cls.configPath				)
								SCREAMING_SNAKE_CASE													=					['accelerate', 'launch', '--config_file', cls.configPath]







		@classmethod
		def 							__A						(	cls				)     ->							Dict:
								super().tearDownClass()
								shutil.rmtree(cls._tmpdir				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
								run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'epoch_0'				)				)				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'step_2'				)				)				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "epoch_0"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
								self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "step_2"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE													=					torch.cuda.device_count()
								else:
														SCREAMING_SNAKE_CASE													=					1
								if num_processes > 1:
														self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)
								else:
														self.assertIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







		@slow
		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
								with mock.patch.dict(os.environ						,       {'TESTING_MOCKED_DATALOADERS': '0'}				):
														SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					re.findall('({.+})'						,       lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					[r for r in results if 'accuracy' in r][-1]
														SCREAMING_SNAKE_CASE													=					ast.literal_eval(lowerCAmelCase__				)
														self.assertGreaterEqual(results['accuracy']						,       0.75				)







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/multi_process_metrics.py']
								run_command(self._launch_args + testargs				)







		@require_trackers
		@mock.patch.dict(os.environ						,       {'WANDB_MODE': 'offline'}				)
		def 							__A						(	self				)     ->							Union[str, Any]:
								with tempfile.TemporaryDirectory() as tmpdir:
														SCREAMING_SNAKE_CASE													=					F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
														run_command(self._launch_args + testargs				)
														self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__						,       'tracking'				)				)				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/gradient_accumulation.py']
								run_command(self._launch_args + testargs				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/local_sgd.py']
								run_command(self._launch_args + testargs				)


