




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=1024 ):

     '''simple docstring'''


     UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     [], []

     UpperCAmelCase_     :				Optional[Any]  =     list(zip(_lowercase					,		_lowercase ) )
     UpperCAmelCase_,       UpperCAmelCase_     :				int  =     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_     :				int  =     new_src + ''' ''' + src
          UpperCAmelCase_     :				int  =     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_     :				Optional[int]  =     src, tgt
          else:  # can fit, keep adding
               UpperCAmelCase_,       UpperCAmelCase_     :				str  =     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 ):

     '''simple docstring'''


     UpperCAmelCase_     :				Tuple  =     Path(_lowercase )
     save_path.mkdir(exist_ok=_lowercase )
     for split in ["train"]:
          UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
          UpperCAmelCase_     :				List[str]  =     [x.rstrip() for x in Path(_lowercase ).open().readlines()]
          UpperCAmelCase_     :				List[str]  =     [x.rstrip() for x in Path(_lowercase ).open().readlines()]
          UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     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_     :				Optional[Any]  =     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__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				int  =     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_     :				Dict  =     parser.parse_args()
     UpperCAmelCase_     :				Optional[Any]  =     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()





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=0.9  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,)					->						Optional[int]:
         UpperCAmelCase_     :				int  =     size if size is not None else {'''shortest_edge''': 30}
         UpperCAmelCase_     :				List[str]  =     crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         UpperCAmelCase_     :				Dict  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     min_resolution
         UpperCAmelCase_     :				Tuple  =     max_resolution
         UpperCAmelCase_     :				Optional[int]  =     do_resize_and_center_crop
         UpperCAmelCase_     :				Tuple  =     size
         UpperCAmelCase_     :				List[str]  =     crop_pct
         UpperCAmelCase_     :				List[str]  =     crop_size
         UpperCAmelCase_     :				Any  =     do_normalize
         UpperCAmelCase_     :				str  =     image_mean
         UpperCAmelCase_     :				List[Any]  =     image_std





    def 				a__ ( self    )					->						str:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = PoolFormerImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				str  =     PoolFormerImageProcessingTester(self    )





    @property
    def 				a__ ( self    )					->						Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_resize_and_center_crop'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''size'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''crop_pct'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_normalize'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_mean'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_std'''    )    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Any  =     self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 30}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 30, '''width''': 30}    )

         UpperCAmelCase_     :				Optional[Any]  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=42  ,crop_size=84    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 84, '''width''': 84}    )





    def 				a__ ( self    )					->						Optional[int]:
         pass





    def 				a__ ( self    )					->						Dict:
         # Initialize image_processing
         UpperCAmelCase_     :				str  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_     :				int  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,Image.Image    )

         # Test not batched input
         UpperCAmelCase_     :				Optional[int]  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values

         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				Union[str, Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





    def 				a__ ( self    )					->						List[Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				Any  =     self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_     :				List[str]  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE  ,numpify=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,np.ndarray    )

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





    def 				a__ ( self    )					->						Union[str, Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_     :				Optional[Any]  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE  ,torchify=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,torch.Tensor    )

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)