

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__	   = logging.get_logger(__name__)


if is_vision_available():
						import PIL

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       ['pixel_values']


			def __init__(    self      ,      lowercase = True      ,      lowercase = None      ,      lowercase = PILImageResampling.BICUBIC      ,      lowercase = True      ,      lowercase = None      ,      lowercase = True      ,      lowercase = 1 / 255      ,      lowercase = True      ,      lowercase = None      ,      lowercase = None      ,      lowercase = True      ,      **lowercase      ,      )  ->						None:
							'''simple docstring'''






							super().__init__(**lowercase       )
							A__        =			size if size is not None else {"shortest_edge": 224}
							A__        =			get_size_dict(lowercase      ,      default_to_square=lowercase       )
							A__        =			crop_size if crop_size is not None else {"height": 224, "width": 224}
							A__        =			get_size_dict(lowercase      ,      default_to_square=lowercase      ,      param_name="crop_size"       )

							A__        =			do_resize
							A__        =			size
							A__        =			resample
							A__        =			do_center_crop
							A__        =			crop_size
							A__        =			do_rescale
							A__        =			rescale_factor
							A__        =			do_normalize
							A__        =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							A__        =			image_std if image_std is not None else OPENAI_CLIP_STD
							A__        =			do_convert_rgb


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = PILImageResampling.BICUBIC      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							A__        =			get_size_dict(lowercase      ,      default_to_square=lowercase       )
							if "shortest_edge" not in size:
											raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'       )
							A__        =			get_resize_output_image_size(lowercase      ,      size=size["shortest_edge"]      ,      default_to_square=lowercase       )
							return resize(lowercase      ,      size=lowercase      ,      resample=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							A__        =			get_size_dict(lowercase       )
							if "height" not in size or "width" not in size:
											raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'       )
							return center_crop(lowercase      ,      size=(size["height"], size["width"])      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						List[str]:
							'''simple docstring'''






							return rescale(lowercase      ,      scale=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = ChannelDimension.FIRST      ,      **lowercase      ,      )  ->						PIL.Image.Image:
							'''simple docstring'''






							A__        =			do_resize if do_resize is not None else self.do_resize
							A__        =			size if size is not None else self.size
							A__        =			get_size_dict(lowercase      ,      param_name="size"      ,      default_to_square=lowercase       )
							A__        =			resample if resample is not None else self.resample
							A__        =			do_center_crop if do_center_crop is not None else self.do_center_crop
							A__        =			crop_size if crop_size is not None else self.crop_size
							A__        =			get_size_dict(lowercase      ,      param_name="crop_size"      ,      default_to_square=lowercase       )
							A__        =			do_rescale if do_rescale is not None else self.do_rescale
							A__        =			rescale_factor if rescale_factor is not None else self.rescale_factor
							A__        =			do_normalize if do_normalize is not None else self.do_normalize
							A__        =			image_mean if image_mean is not None else self.image_mean
							A__        =			image_std if image_std is not None else self.image_std
							A__        =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							A__        =			make_list_of_images(lowercase       )

							if not valid_images(lowercase       ):
											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:
											raise ValueError("Size must be specified if do_resize is True."       )

							if do_center_crop and crop_size is None:
											raise ValueError("Crop size 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."       )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
											A__        =			[convert_to_rgb(lowercase       ) for image in images]

							# All transformations expect numpy arrays.
							A__        =			[to_numpy_array(lowercase       ) for image in images]

							if do_resize:
											A__        =			[self.resize(image=lowercase      ,      size=lowercase      ,      resample=lowercase       ) for image in images]

							if do_center_crop:
											A__        =			[self.center_crop(image=lowercase      ,      size=lowercase       ) for image in images]

							if do_rescale:
											A__        =			[self.rescale(image=lowercase      ,      scale=lowercase       ) for image in images]

							if do_normalize:
											A__        =			[self.normalize(image=lowercase      ,      mean=lowercase      ,      std=lowercase       ) for image in images]

							A__        =			[to_channel_dimension_format(lowercase      ,      lowercase       ) for image in images]

							A__        =			{"pixel_values": images}
							return BatchFeature(data=lowercase      ,      tensor_type=lowercase       )




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

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

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

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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Union[str, Any]:




				'''simple docstring'''





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



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

