

"""simple docstring"""






from pathlib import Path

import fire
from tqdm import tqdm





def 	lowercase      (   __UpperCamelCase="ro"      ,   __UpperCamelCase="en"      ,   __UpperCamelCase="wmt16"      ,   __UpperCamelCase=None						) ->      None:
 try:
  import datasets
 except (ModuleNotFoundError, ImportError):
  raise ImportError('''run pip install datasets'''						)
 __magic_name__     =      f'''{src_lang}-{tgt_lang}'''
 print(f'''Converting {dataset}-{pair}'''						)
 __magic_name__     =      datasets.load_dataset(__UpperCamelCase      ,   __UpperCamelCase						)
 if save_dir is None:
  __magic_name__     =      f'''{dataset}-{pair}'''
 __magic_name__     =      Path(__UpperCamelCase						)
 save_dir.mkdir(exist_ok=__UpperCamelCase						)

 for split in ds.keys():
  print(f'''Splitting {split} with {ds[split].num_rows} records'''						)

  # to save to val.source, val.target like summary datasets
  __magic_name__     =      '''val''' if split == '''validation''' else split
  __magic_name__     =      save_dir.joinpath(f'''{fn}.source'''						)
  __magic_name__     =      save_dir.joinpath(f'''{fn}.target'''						)
  __magic_name__     =      src_path.open('''w+'''						)
  __magic_name__     =      tgt_path.open('''w+'''						)

  # reader is the bottleneck so writing one record at a time doesn't slow things down
  for x in tqdm(ds[split]						):
   __magic_name__     =      x['''translation''']
   src_fp.write(ex[src_lang] + '''\n'''						)
   tgt_fp.write(ex[tgt_lang] + '''\n'''						)

 print(f'''Saved {dataset} dataset to {save_dir}'''						)


if __name__ == "__main__":
 fire.Fire(download_wmt_dataset)




"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''pixel_values''']







     def __init__(      self ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 32 ,						UpperCamelCase_=PILImageResampling.BILINEAR ,						UpperCamelCase_ = True ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize
      __magic_name__     =      do_rescale
      __magic_name__     =      size_divisor
      __magic_name__     =      resample
      super().__init__(**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      __magic_name__   ,						__magic_name__     =      get_image_size(UpperCamelCase_       )
      # Rounds the height and width down to the closest multiple of size_divisor
      __magic_name__     =      height // size_divisor * size_divisor
      __magic_name__     =      width // size_divisor * size_divisor
      __magic_name__     =      resize(UpperCamelCase_ ,						(new_h, new_w) ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      return rescale(image=UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_=None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = ChannelDimension.FIRST ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize if do_resize is not None else self.do_resize
      __magic_name__     =      do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__     =      size_divisor if size_divisor is not None else self.size_divisor
      __magic_name__     =      resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
       raise ValueError('''size_divisor is required for resizing'''       )

      __magic_name__     =      make_list_of_images(UpperCamelCase_       )

      if not valid_images(UpperCamelCase_       ):
       raise ValueError('''Invalid image(s)'''       )

      # All transformations expect numpy arrays.
      __magic_name__     =      [to_numpy_array(UpperCamelCase_       ) for img in images]

      if do_resize:
       __magic_name__     =      [self.resize(UpperCamelCase_ ,						size_divisor=UpperCamelCase_ ,						resample=UpperCamelCase_       ) for image in images]

      if do_rescale:
       __magic_name__     =      [self.rescale(UpperCamelCase_ ,						scale=1 / 255       ) for image in images]

      __magic_name__     =      [to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_       ) for image in images]

      __magic_name__     =      {'''pixel_values''': images}
      return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )

