





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 ViTImageProcessor



class 					A__   (       unittest.TestCase			):





      def __init__( self       ,							A_       ,							A_=13       ,							A_=3       ,							A_=224       ,							A_=30       ,							A_=400       ,							A_=True       ,							A_=None       ,							A_=True       ,							A_=[0.5, 0.5, 0.5]       ,							A_=[0.5, 0.5, 0.5]       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase	:					List[str]       	=   parent
       UpperCamelCase	:					int       	=   batch_size
       UpperCamelCase	:					Optional[int]       	=   num_channels
       UpperCamelCase	:					Union[str, Any]       	=   image_size
       UpperCamelCase	:					Tuple       	=   min_resolution
       UpperCamelCase	:					Union[str, Any]       	=   max_resolution
       UpperCamelCase	:					str       	=   do_resize
       UpperCamelCase	:					str       	=   size
       UpperCamelCase	:					List[Any]       	=   do_normalize
       UpperCamelCase	:					Optional[int]       	=   image_mean
       UpperCamelCase	:					Optional[Any]       	=   image_std





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:int						    =   ViTImageProcessor if is_vision_available() else None





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   EfficientFormerImageProcessorTester(self   )





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.image_proc_tester.prepare_image_processor_dict()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(A_       ,							"image_mean"   )   )
       self.assertTrue(hasattr(A_       ,							"image_std"   )   )
       self.assertTrue(hasattr(A_       ,							"do_normalize"   )   )
       self.assertTrue(hasattr(A_       ,							"do_resize"   )   )
       self.assertTrue(hasattr(A_       ,							"size"   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       pass





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       UpperCamelCase	:					Optional[int]       	=   prepare_image_inputs(self.image_proc_tester       ,							equal_resolution=A_   )
       for image in image_inputs:
        self.assertIsInstance(A_       ,							Image.Image   )

       # Test not batched input
       UpperCamelCase	:					Tuple       	=   image_processor(image_inputs[0]       ,							return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,							)

       # Test batched
       UpperCamelCase	:					int       	=   image_processor(A_       ,							return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       UpperCamelCase	:					List[str]       	=   prepare_image_inputs(self.image_proc_tester       ,							equal_resolution=A_       ,							numpify=A_   )
       for image in image_inputs:
        self.assertIsInstance(A_       ,							np.ndarray   )

       # Test not batched input
       UpperCamelCase	:					str       	=   image_processor(image_inputs[0]       ,							return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,							)

       # Test batched
       UpperCamelCase	:					str       	=   image_processor(A_       ,							return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       UpperCamelCase	:					Optional[Any]       	=   prepare_image_inputs(self.image_proc_tester       ,							equal_resolution=A_       ,							torchify=A_   )
       for image in image_inputs:
        self.assertIsInstance(A_       ,							torch.Tensor   )

       # Test not batched input
       UpperCamelCase	:					List[str]       	=   image_processor(image_inputs[0]       ,							return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,							)

       # Test batched
       UpperCamelCase	:					Dict       	=   image_processor(A_       ,							return_tensors="pt"   ).pixel_values
       self.assertEqual(
           encoded_images.shape       ,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )       ,							)






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

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

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

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


