

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		:   Union[str, Any]										=       [
    """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  lowercase				(       unittest.TestCase			):




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




				def     a							(					self       ):
											self.one_complete_example('complete_nlp_example.py'      ,   snake_case       )
											self.one_complete_example('complete_nlp_example.py'      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    os.path.abspath(os.path.join('examples'      ,   'cv_example.py'       )       )
											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'      ,   snake_case      ,   snake_case      ,   snake_case       )
											self.one_complete_example('complete_cv_example.py'      ,   snake_case      ,   snake_case      ,   snake_case       )







@mock.patch.dict(os.environ	,  {'''TESTING_MOCKED_DATALOADERS''': '''1'''}			)
class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			False




				@classmethod
				def     a							(					cls       ):
											super().setUpClass()
											snake_case_							=    tempfile.mkdtemp()
											snake_case_							=    os.path.join(cls._tmpdir      ,   'default_config.yml'       )

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




				@classmethod
				def     a							(					cls       ):
											super().tearDownClass()
											shutil.rmtree(cls._tmpdir       )




				def     a							(					self       ):
											snake_case_							=    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     a							(					self       ):
											snake_case_							=    F'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        '''.split()
											snake_case_							=    run_command(self._launch_args + testargs       )
											self.assertTrue(os.path.exists(os.path.join(self.tmpdir      ,   'step_2'       )       )       )




				def     a							(					self       ):
											snake_case_							=    F'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir      ,   "epoch_0"       )}
        '''.split()
											snake_case_							=    run_command(self._launch_args + testargs      ,   return_stdout=snake_case       )
											self.assertNotIn('epoch 0:'      ,   snake_case       )
											self.assertIn('epoch 1:'      ,   snake_case       )




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




				@slow
				def     a							(					self       ):
											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'}       ):
																		snake_case_							=    run_command(self._launch_args + testargs      ,   return_stdout=snake_case       )
																		snake_case_							=    re.findall('({.+})'      ,   snake_case       )
																		snake_case_							=    [r for r in results if 'accuracy' in r][-1]
																		snake_case_							=    ast.literal_eval(snake_case       )
																		self.assertGreaterEqual(results['accuracy']      ,   0.75       )




				def     a							(					self       ):
											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       ):
											with tempfile.TemporaryDirectory() as tmpdir:
																		snake_case_							=    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(snake_case      ,   'tracking'       )       )       )




				def     a							(					self       ):
											snake_case_							=    ['examples/by_feature/gradient_accumulation.py']
											run_command(self._launch_args + testargs       )




				def     a							(					self       ):
											snake_case_							=    ['examples/by_feature/local_sgd.py']
											run_command(self._launch_args + testargs       )







from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




