'''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    :     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			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			str, UpperCamelCase__      :			bool, UpperCamelCase__      :			str = None, UpperCamelCase__      :			list = None     )  ->			Any:
        _A    		=  None
        _A    		=  os.path.abspath(os.path.join('examples', 'by_feature'     )     )
        _A    		=  os.path.abspath('examples'     )
        for item in os.listdir(UpperCamelCase__     ):
             if item not in EXCLUDE_EXAMPLES:
                  _A    		=  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()', ):
                            _A    		=  compare_against_test(
                                os.path.join(UpperCamelCase__, UpperCamelCase__     ), UpperCamelCase__, UpperCamelCase__, UpperCamelCase__     )
                            _A    		=  '\n'.join(UpperCamelCase__     )
                            if special_strings is not None:
                                 for string in special_strings:
                                      _A    		=  diff.replace(UpperCamelCase__, ''     )
                            self.assertEqual(UpperCamelCase__, ''     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        self.one_complete_example('complete_nlp_example.py', UpperCamelCase__     )
        self.one_complete_example('complete_nlp_example.py', UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[str]:
        _A    		=  os.path.abspath(os.path.join('examples', 'cv_example.py'     )     )
        _A    		=  [
            ' ' * 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 lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   False
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			Optional[int]     )  ->			Dict:
        super().setUpClass()
        _A    		=  tempfile.mkdtemp()
        _A    		=  os.path.join(cls._tmpdir, 'default_config.yml'     )

        write_basic_config(save_location=cls.configPath     )
        _A    		=  ['accelerate', 'launch', '--config_file', cls.configPath]
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			List[str]     )  ->			Tuple:
        super().tearDownClass()
        shutil.rmtree(cls._tmpdir     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			str:
        _A    		=  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 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
        _A    		=  run_command(self._launch_args + testargs     )
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir, 'step_2'     )     )     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			str:
        _A    		=  f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir, "epoch_0"     )}\n        '.split()
        _A    		=  run_command(self._launch_args + testargs, return_stdout=UpperCamelCase__     )
        self.assertNotIn('epoch 0:', UpperCamelCase__     )
        self.assertIn('epoch 1:', UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[str]:
        _A    		=  f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir, "step_2"     )}\n        '.split()
        _A    		=  run_command(self._launch_args + testargs, return_stdout=UpperCamelCase__     )
        if torch.cuda.is_available():
             _A    		=  torch.cuda.device_count()
        else:
             _A    		=  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      :			List[Any]     )  ->			Optional[int]:
        _A    		=  '\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
        with mock.patch.dict(os.environ, {'TESTING_MOCKED_DATALOADERS': '0'}     ):
             _A    		=  run_command(self._launch_args + testargs, return_stdout=UpperCamelCase__     )
             _A    		=  re.findall('({.+})', UpperCamelCase__     )
             _A    		=  [r for r in results if 'accuracy' in r][-1]
             _A    		=  ast.literal_eval(UpperCamelCase__     )
             self.assertGreaterEqual(results['accuracy'], 0.75     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Dict:
        _A    		=  ['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      :			Tuple     )  ->			Union[str, Any]:
        with tempfile.TemporaryDirectory() as tmpdir:
             _A    		=  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(UpperCamelCase__, 'tracking'     )     )     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[Any]:
        _A    		=  ['examples/by_feature/gradient_accumulation.py']
        run_command(self._launch_args + testargs     )

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        _A    		=  ['examples/by_feature/local_sgd.py']
        run_command(self._launch_args + testargs     )




'''simple docstring'''






import math





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  []
     _A    		=  2
     _A    		=  int(math.sqrt(__snake_case				)				)  # Size of every segment
     _A    		=  [True] * (end + 1)
     _A    		=  []

     while start <= end:
          if temp[start] is True:
               in_prime.append(__snake_case				)
               for i in range(start * start   ,		end + 1   ,		__snake_case				):
                    _A    		=  False
          start += 1
     prime += in_prime

     _A    		=  end + 1
     _A    		=  min(2 * end   ,		__snake_case				)

     while low <= n:
          _A    		=  [True] * (high - low + 1)
          for each in in_prime:
               _A    		=  math.floor(low / each				) * each
               if t < low:
                    t += each

               for j in range(__snake_case   ,		high + 1   ,		__snake_case				):
                    _A    		=  False

          for j in range(len(__snake_case				)				):
               if temp[j] is True:
                    prime.append(j + low				)

          _A    		=  high + 1
          _A    		=  min(high + end   ,		__snake_case				)

     return prime
print(sieve(10**6))



