
"""simple docstring"""
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 MobileNetVaImageProcessor
class 			UpperCamelCase      (							unittest.TestCase  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__=7  ,     snake_case__=3  ,     snake_case__=18  ,     snake_case__=30  ,     snake_case__=400  ,     snake_case__=True  ,     snake_case__=None  ,     snake_case__=True  ,     snake_case__=None  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = size if size is not None else {"shortest_edge": 20}
       _SCREAMING_SNAKE_CASE   :				Dict							    = crop_size if crop_size is not None else {"height": 18, "width": 18}
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = parent
       _SCREAMING_SNAKE_CASE   :				str							    = batch_size
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = num_channels
       _SCREAMING_SNAKE_CASE   :				int							    = image_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = min_resolution
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = max_resolution
       _SCREAMING_SNAKE_CASE   :				Dict							    = do_resize
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_center_crop
       _SCREAMING_SNAKE_CASE   :				Tuple							    = crop_size




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }






@require_torch
@require_vision
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			MobileNetVaImageProcessor if is_vision_available() else None
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = MobileNetVaImageProcessingTester(self				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(snake_case__  ,     "do_resize"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "size"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "do_center_crop"				)				)
       self.assertTrue(hasattr(snake_case__  ,     "crop_size"				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.image_processing_class.from_dict(self.image_processor_dict				)
       self.assertEqual(image_processor.size  ,     {"shortest_edge": 20}				)
       self.assertEqual(image_processor.crop_size  ,     {"height": 18, "width": 18}				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       pass
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__				)
       for image in image_inputs:
         self.assertIsInstance(snake_case__  ,     Image.Image				)

       # Test not batched input
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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
       _SCREAMING_SNAKE_CASE   :				Tuple							    = image_processing(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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__  ,     numpify=snake_case__				)
       for image in image_inputs:
         self.assertIsInstance(snake_case__  ,     np.ndarray				)

       # Test not batched input
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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
       _SCREAMING_SNAKE_CASE   :				int							    = image_processing(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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE   :				List[str]							    = prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__  ,     torchify=snake_case__				)
       for image in image_inputs:
         self.assertIsInstance(snake_case__  ,     torch.Tensor				)

       # Test not batched input
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 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
       _SCREAMING_SNAKE_CASE   :				List[str]							    = image_processing(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"],
           )  ,     )





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

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

  def is_too_big(lowerCamelCase__							:							List[Any]				):
    return tok(lowerCamelCase__,						return_tensors="pt"				).input_ids.shape[1] > max_tokens

  for src, tgt in tqdm(sorted_examples[1:]				):
    _SCREAMING_SNAKE_CASE   :				int							    = new_src + " " + src
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_tgt + " " + tgt
    if is_too_big(lowerCamelCase__				) or is_too_big(lowerCamelCase__				):  # cant fit, finalize example
      finished_src.append(lowerCamelCase__				)
      finished_tgt.append(lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = src, tgt
    else:  # can fit, keep adding
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = cand_src, cand_tgt

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





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





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



