
"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase_       :						List[str]     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			["""pixel_values"""]
     def __init__(     self  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = 0.9  ,     snake_case__ = PILImageResampling.BICUBIC  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = 1 / 255  ,     snake_case__ = True  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(**snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = size if size is not None else {"shortest_edge": 224}
       _SCREAMING_SNAKE_CASE   :				str							    = get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = crop_size if crop_size is not None else {"height": 224, "width": 224}
       _SCREAMING_SNAKE_CASE   :				Tuple							    = get_size_dict(snake_case__  ,     param_name="crop_size"				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = do_resize
       _SCREAMING_SNAKE_CASE   :				int							    = size
       _SCREAMING_SNAKE_CASE   :				Tuple							    = crop_pct
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = resample
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = do_center_crop
       _SCREAMING_SNAKE_CASE   :				Dict							    = crop_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_rescale
       _SCREAMING_SNAKE_CASE   :				Any							    = rescale_factor
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = do_normalize
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       _SCREAMING_SNAKE_CASE   :				Any							    = image_std if image_std is not None else IMAGENET_DEFAULT_STD
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = PILImageResampling.BICUBIC  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
       if crop_pct is not None:
         if "shortest_edge" in size:
           _SCREAMING_SNAKE_CASE   :				int							    = int(size["shortest_edge"] / crop_pct				)
         elif "height" in size and "width" in size:
           if size["height"] == size["width"]:
             _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(size["height"] / crop_pct				)
           else:
             _SCREAMING_SNAKE_CASE   :				List[str]							    = (int(size["height"] / crop_pct				), int(size["width"] / crop_pct				))
         else:
           raise ValueError("Invalid size for resize: {}".format(snake_case__				)				)

         _SCREAMING_SNAKE_CASE   :				int							    = get_resize_output_image_size(snake_case__  ,     size=snake_case__  ,     default_to_square=snake_case__				)
       else:
         if "shortest_edge" in size:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = get_resize_output_image_size(snake_case__  ,     size=size["shortest_edge"]  ,     default_to_square=snake_case__				)
         elif "height" in size and "width" in size:
           _SCREAMING_SNAKE_CASE   :				str							    = (size["height"], size["width"])
         else:
           raise ValueError("Invalid size for resize: {}".format(snake_case__				)				)

       return resize(snake_case__  ,     size=snake_case__  ,     resample=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_size_dict(snake_case__				)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''				)
       return center_crop(snake_case__  ,     size=(size["height"], size["width"])  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       return rescale(snake_case__  ,     scale=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       return normalize(snake_case__  ,     mean=snake_case__  ,     std=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = ChannelDimension.FIRST  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = do_resize if do_resize is not None else self.do_resize
       _SCREAMING_SNAKE_CASE   :				Dict							    = crop_pct if crop_pct is not None else self.crop_pct
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = resample if resample is not None else self.resample
       _SCREAMING_SNAKE_CASE   :				Tuple							    = do_center_crop if do_center_crop is not None else self.do_center_crop
       _SCREAMING_SNAKE_CASE   :				str							    = do_rescale if do_rescale is not None else self.do_rescale
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = rescale_factor if rescale_factor is not None else self.rescale_factor
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_normalize if do_normalize is not None else self.do_normalize
       _SCREAMING_SNAKE_CASE   :				int							    = image_mean if image_mean is not None else self.image_mean
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = image_std if image_std is not None else self.image_std

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = size if size is not None else self.size
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = get_size_dict(snake_case__  ,     default_to_square=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = crop_size if crop_size is not None else self.crop_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = get_size_dict(snake_case__  ,     param_name="crop_size"				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = make_list_of_images(snake_case__				)

       if not valid_images(snake_case__				):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."				)

       if do_resize and size is None or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."				)

       if do_center_crop and crop_pct is None:
         raise ValueError("Crop_pct must be specified if do_center_crop is True."				)

       if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."				)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."				)

       # All transformations expect numpy arrays.
       _SCREAMING_SNAKE_CASE   :				List[str]							    = [to_numpy_array(snake_case__				) for image in images]

       if do_resize:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [self.resize(image=snake_case__  ,     size=snake_case__  ,     crop_pct=snake_case__  ,     resample=snake_case__				) for image in images]

       if do_center_crop:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [self.center_crop(image=snake_case__  ,     size=snake_case__				) for image in images]

       if do_rescale:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = [self.rescale(image=snake_case__  ,     scale=snake_case__				) for image in images]

       if do_normalize:
         _SCREAMING_SNAKE_CASE   :				str							    = [self.normalize(image=snake_case__  ,     mean=snake_case__  ,     std=snake_case__				) for image in images]

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [to_channel_dimension_format(snake_case__  ,     snake_case__				) for image in images]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {"pixel_values": images}
       return BatchFeature(data=snake_case__  ,     tensor_type=snake_case__				)





"""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()



