"""simple docstring"""
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=1024       )     ->List[str]:
		a__							,   a__:					Optional[int]      							=     [], []

		a__:					str      							=     list(zip(__A		,					__A       )       )
		a__							,   a__:					Any      							=     sorted_examples[0]

		def is_too_big(_SCREAMING_SNAKE_CASE       ):
				return tok(__A		,					return_tensors='pt'       ).input_ids.shape[1] > max_tokens

		for src, tgt in tqdm(sorted_examples[1:]       ):
				a__:					List[str]      							=     new_src + ' ' + src
				a__:					Any      							=     new_tgt + ' ' + tgt
				if is_too_big(__A       ) or is_too_big(__A       ):  # cant fit, finalize example
						finished_src.append(__A       )
						finished_tgt.append(__A       )
						a__							,   a__:					List[Any]      							=     src, tgt
				else:  # can fit, keep adding
						a__							,   a__:					str      							=     cand_src, cand_tgt

    # cleanup
		if new_src:
				assert new_tgt
				finished_src.append(__A       )
				finished_tgt.append(__A       )
		return finished_src, finished_tgt







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
		a__:					int      							=     Path(__A       )
		save_path.mkdir(exist_ok=__A       )
		for split in ["train"]:
				a__							,   a__:					Tuple      							=     data_dir / F'{split}.source', data_dir / F'{split}.target'
				a__:					Union[str, Any]      							=     [x.rstrip() for x in Path(__A       ).open().readlines()]
				a__:					List[str]      							=     [x.rstrip() for x in Path(__A       ).open().readlines()]
				a__							,   a__:					Union[str, Any]      							=     pack_examples(__A		,					__A		,					__A		,					__A       )
				print(F'packed {split} split from {len(__A       )} examples -> {len(__A       )}.'       )
				Path(save_path / F'{split}.source'       ).open('w'       ).write('\n'.join(__A       )       )
				Path(save_path / F'{split}.target'       ).open('w'       ).write('\n'.join(__A       )       )
		for split in ["val", "test"]:
				a__							,   a__:					List[Any]      							=     data_dir / F'{split}.source', data_dir / F'{split}.target'
				shutil.copyfile(__A		,					save_path / F'{split}.source'       )
				shutil.copyfile(__A		,					save_path / F'{split}.target'       )







def 						__a       (     )     ->Any:
		a__:					Any      							=     argparse.ArgumentParser()
		parser.add_argument('--tok_name'		,					type=__A		,					help='like facebook/bart-large-cnn,t5-base, etc.'       )
		parser.add_argument('--max_seq_len'		,					type=__A		,					default=128       )
		parser.add_argument('--data_dir'		,					type=__A       )
		parser.add_argument('--save_path'		,					type=__A       )
		a__:					Tuple      							=     parser.parse_args()
		a__:					Optional[Any]      							=     AutoTokenizer.from_pretrained(args.tok_name       )
		return pack_data_dir(__A		,					Path(args.data_dir       )		,					args.max_seq_len		,					args.save_path       )


if __name__ == "__main__":
	packer_cli()



"""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)

lowercase__ 	=     [
    '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   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      lowercase = None)     ->      Union[str, Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Dict      							=     os.path.abspath(os.path.join('examples'   ,      'cv_example.py'))
        a__:					Tuple      							=     [
            ' ' * 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'   ,      lowercase   ,      lowercase   ,      lowercase)
        self.one_complete_example('complete_cv_example.py'   ,      lowercase   ,      lowercase   ,      lowercase)







@mock.patch.dict(os.environ	,				{"""TESTING_MOCKED_DATALOADERS""": """1"""}    )
class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		False



      @classmethod
      def   lowerCamelCase_       (						cls)     ->      List[str]:





        '''simple docstring'''





        super().setUpClass()
        a__:					Dict      							=     tempfile.mkdtemp()
        a__:					Optional[int]      							=     os.path.join(cls._tmpdir   ,      'default_config.yml')

        write_basic_config(save_location=cls.configPath)
        a__:					List[str]      							=     ['accelerate', 'launch', '--config_file', cls.configPath]



      @classmethod
      def   lowerCamelCase_       (						cls)     ->      List[str]:





        '''simple docstring'''





        super().tearDownClass()
        shutil.rmtree(cls._tmpdir)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     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   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Any      							=     f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
        a__:					Tuple      							=     run_command(self._launch_args + testargs)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir   ,      'step_2')))



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Optional[Any]      							=     f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir   ,      "epoch_0")}\n        '.split()
        a__:					Optional[int]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
        self.assertNotIn('epoch 0:'   ,      lowercase)
        self.assertIn('epoch 1:'   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir   ,      "step_2")}\n        '.split()
        a__:					List[str]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
        if torch.cuda.is_available():
          a__:					Union[str, Any]      							=     torch.cuda.device_count()
        else:
          a__:					Optional[int]      							=     1
        if num_processes > 1:
          self.assertNotIn('epoch 0:'   ,      lowercase)
          self.assertIn('epoch 1:'   ,      lowercase)
        else:
          self.assertIn('epoch 0:'   ,      lowercase)
          self.assertIn('epoch 1:'   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					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   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        with tempfile.TemporaryDirectory() as tmpdir:
          a__:					List[str]      							=     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(lowercase   ,      'tracking')))



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					int      							=     ['examples/by_feature/gradient_accumulation.py']
        run_command(self._launch_args + testargs)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     ['examples/by_feature/local_sgd.py']
        run_command(self._launch_args + testargs)


