
import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def  __lowerCamelCase					( )       ->      Optional[Any]:
     raise RuntimeError('CUDA out of memory.'			)
class       _lowerCAmelCase  (		nn.Module							):

 """simple docstring"""

 def __init__(						self			:			Dict   ):






      """simple docstring"""



      super().__init__()
      UpperCamelCase						    =					nn.Linear(3					, 4   )
      UpperCamelCase						    =					nn.BatchNormad(4   )
      UpperCamelCase						    =					nn.Linear(4					, 5   )






 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			str   ):






      """simple docstring"""



      return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE__   )   )   )




class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					[]

      @find_executable_batch_size(starting_batch_size=1_28   )
      def mock_training_loop_function(SCREAMING_SNAKE_CASE__			:			int   ):
           nonlocal batch_sizes
           batch_sizes.append(SCREAMING_SNAKE_CASE__   )
           if batch_size != 8:
                raise_fake_out_of_memory()

      mock_training_loop_function()
      self.assertListEqual(SCREAMING_SNAKE_CASE__					, [1_28, 64, 32, 16, 8]   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					[]

      @find_executable_batch_size(starting_batch_size=1_28   )
      def mock_training_loop_function(SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			List[str]   ):
           nonlocal batch_sizes
           batch_sizes.append(SCREAMING_SNAKE_CASE__   )
           if batch_size != 8:
                raise_fake_out_of_memory()
           return batch_size, arga

      UpperCamelCase    ,		UpperCamelCase						    =					mock_training_loop_function('hello'   )
      self.assertListEqual(SCREAMING_SNAKE_CASE__					, [1_28, 64, 32, 16, 8]   )
      self.assertListEqual([bs, arga]					, [8, 'hello']   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      @find_executable_batch_size(starting_batch_size=0   )
      def mock_training_loop_function(SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):
           pass

      with self.assertRaises(SCREAMING_SNAKE_CASE__   ) as cm:
           mock_training_loop_function()
           self.assertIn('No executable batch size found, reached zero.'					, cm.exception.args[0]   )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      @find_executable_batch_size(starting_batch_size=16   )
      def mock_training_loop_function(SCREAMING_SNAKE_CASE__			:			str   ):
           if batch_size > 0:
                raise_fake_out_of_memory()
           pass

      with self.assertRaises(SCREAMING_SNAKE_CASE__   ) as cm:
           mock_training_loop_function()
           self.assertIn('No executable batch size found, reached zero.'					, cm.exception.args[0]   )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      @find_executable_batch_size(starting_batch_size=1_28   )
      def mock_training_loop_function(SCREAMING_SNAKE_CASE__			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[str]   ):
           if batch_size != 8:
                raise raise_fake_out_of_memory()

      with self.assertRaises(SCREAMING_SNAKE_CASE__   ) as cm:
           mock_training_loop_function(1_28					, 'hello'					, 'world'   )
           self.assertIn('Batch size was passed into `f`'					, cm.exception.args[0]   )
           self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'					, cm.exception.args[0]   )

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      @find_executable_batch_size(starting_batch_size=16   )
      def mock_training_loop_function(SCREAMING_SNAKE_CASE__			:			int   ):
           raise ValueError('Oops, we had an error!'   )

      with self.assertRaises(SCREAMING_SNAKE_CASE__   ) as cm:
           mock_training_loop_function()
           self.assertIn('Oops, we had an error!'					, cm.exception.args[0]   )






 @require_cuda
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					torch.cuda.memory_allocated()
      UpperCamelCase						    =					ModelForTest()
      model.cuda()
      self.assertGreater(torch.cuda.memory_allocated()					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					release_memory(SCREAMING_SNAKE_CASE__   )
      self.assertEqual(torch.cuda.memory_allocated()					, SCREAMING_SNAKE_CASE__   )




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=1024			)       ->      Union[str, Any]:
     UpperCamelCase    ,		UpperCamelCase						    =					[], []

     UpperCamelCase						    =					list(zip(_lowercase						,     _lowercase			)			)
     UpperCamelCase    ,		UpperCamelCase						    =					sorted_examples[0]

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

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

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

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

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


if __name__ == "__main__":
 packer_cli()


