
'''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)

A         =   [
    '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      __snake_case		( unittest.TestCase):
					def      UpperCAmelCase_				(  self,     A,     A,     A = None,     A = None   ):

									"""simple docstring"""


									lowerCamelCase       :      List[str]          =			None
									lowerCamelCase       :      Union[str, Any]          =			os.path.abspath(os.path.join('examples',     'by_feature'   )   )
									lowerCamelCase       :      Dict          =			os.path.abspath('examples'   )
									for item in os.listdir(_UpperCAmelCase   ):
													if item not in EXCLUDE_EXAMPLES:
																	lowerCamelCase       :      Union[str, Any]          =			os.path.join(_UpperCAmelCase,     _UpperCAmelCase   )
																	if os.path.isfile(_UpperCAmelCase   ) and ".py" in item_path:
																					with self.subTest(
																					    tested_script=_UpperCAmelCase,     feature_script=_UpperCAmelCase,     tested_section='main()' if parser_only else 'training_function()',     ):
																									lowerCamelCase       :      Optional[int]          =			compare_against_test(
																									    os.path.join(_UpperCAmelCase,     _UpperCAmelCase   ),     _UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase   )
																									lowerCamelCase       :      int          =			'\n'.join(_UpperCAmelCase   )
																									if special_strings is not None:
																													for string in special_strings:
																																	lowerCamelCase       :      Optional[Any]          =			diff.replace(_UpperCAmelCase,     ''   )
																									self.assertEqual(_UpperCAmelCase,     ''   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									self.one_complete_example('complete_nlp_example.py',     _UpperCAmelCase   )
									self.one_complete_example('complete_nlp_example.py',     _UpperCAmelCase   )




					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Any          =			os.path.abspath(os.path.join('examples',     'cv_example.py'   )   )
									lowerCamelCase       :      str          =			[
									    ' ' * 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',     _UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase   )
									self.one_complete_example('complete_cv_example.py',     _UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase   )





@mock.patch.dict(os.environ     , {'''TESTING_MOCKED_DATALOADERS''': '''1'''})
class      __snake_case		( UpperCAmelCase__):
					_lowerCAmelCase        =						False
					@classmethod
					def      UpperCAmelCase_				(  cls   ):

									"""simple docstring"""


									super().setUpClass()
									lowerCamelCase       :      Any          =			tempfile.mkdtemp()
									lowerCamelCase       :      List[Any]          =			os.path.join(cls._tmpdir,     'default_config.yml'   )

									write_basic_config(save_location=cls.configPath   )
									lowerCamelCase       :      str          =			['accelerate', 'launch', '--config_file', cls.configPath]
					@classmethod
					def      UpperCAmelCase_				(  cls   ):

									"""simple docstring"""


									super().tearDownClass()
									shutil.rmtree(cls._tmpdir   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Union[str, Any]          =			F'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        '''.split()
									run_command(self._launch_args + testargs   )
									self.assertTrue(os.path.exists(os.path.join(self.tmpdir,     'epoch_0'   )   )   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase       :      List[Any]          =			F'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir,     'epoch_0'   )}
        '''.split()
									lowerCamelCase       :      List[str]          =			run_command(self._launch_args + testargs,     return_stdout=_UpperCAmelCase   )
									self.assertNotIn('epoch 0:',     _UpperCAmelCase   )
									self.assertIn('epoch 1:',     _UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[Any]          =			F'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir,     'step_2'   )}
        '''.split()
									lowerCamelCase       :      Optional[int]          =			run_command(self._launch_args + testargs,     return_stdout=_UpperCAmelCase   )
									if torch.cuda.is_available():
													lowerCamelCase       :      Union[str, Any]          =			torch.cuda.device_count()
									else:
													lowerCamelCase       :      int          =			1
									if num_processes > 1:
													self.assertNotIn('epoch 0:',     _UpperCAmelCase   )
													self.assertIn('epoch 1:',     _UpperCAmelCase   )
									else:
													self.assertIn('epoch 0:',     _UpperCAmelCase   )
													self.assertIn('epoch 1:',     _UpperCAmelCase   )
					@slow
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Union[str, Any]          =			'\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
									with mock.patch.dict(os.environ,     {'TESTING_MOCKED_DATALOADERS': '0'}   ):
													lowerCamelCase       :      Tuple          =			run_command(self._launch_args + testargs,     return_stdout=_UpperCAmelCase   )
													lowerCamelCase       :      str          =			re.findall('({.+})',     _UpperCAmelCase   )
													lowerCamelCase       :      Dict          =			[r for r in results if 'accuracy' in r][-1]
													lowerCamelCase       :      Any          =			ast.literal_eval(_UpperCAmelCase   )
													self.assertGreaterEqual(results['accuracy'],     0.75   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      str          =			['examples/by_feature/multi_process_metrics.py']
									run_command(self._launch_args + testargs   )
					@require_trackers
					@mock.patch.dict(os.environ,     {'WANDB_MODE': 'offline'}   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									with tempfile.TemporaryDirectory() as tmpdir:
													lowerCamelCase       :      Optional[int]          =			F'''
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            '''.split()
													run_command(self._launch_args + testargs   )
													self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase,     'tracking'   )   )   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      List[Any]          =			['examples/by_feature/gradient_accumulation.py']
									run_command(self._launch_args + testargs   )




					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Any          =			['examples/by_feature/local_sgd.py']
									run_command(self._launch_args + testargs   )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

