





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


SCREAMING_SNAKE_CASE_						       =	4
SCREAMING_SNAKE_CASE_						       =	3

class 		UpperCamelCase__			(				__SCREAMING_SNAKE_CASE       ):



     '''simple docstring'''

     pass





def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Dict:


      '''simple docstring'''


      for shard in shards:
            for i in range(lowerCAmelCase__     ):
                  yield {"i": i, "shard": shard}





def        __lowercase       (				)			->       List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						int(os.environ["""RANK"""]     )
      SCREAMING_SNAKE_CASE		=						int(os.environ["""WORLD_SIZE"""]     )

      SCREAMING_SNAKE_CASE		=						ArgumentParser()
      parser.add_argument("""--streaming"""			,						type=lowerCAmelCase__     )
      parser.add_argument("""--local_rank"""			,						type=lowerCAmelCase__     )
      parser.add_argument("""--num_workers"""			,						type=lowerCAmelCase__			,						default=0     )
      SCREAMING_SNAKE_CASE		=						parser.parse_args()
      SCREAMING_SNAKE_CASE		=						args.streaming
      SCREAMING_SNAKE_CASE		=						args.num_workers

      SCREAMING_SNAKE_CASE		=						{"""shards""": [F"""shard_{shard_idx}""" for shard_idx in range(lowerCAmelCase__     )]}
      SCREAMING_SNAKE_CASE		=						IterableDataset.from_generator(lowerCAmelCase__			,						gen_kwargs=lowerCAmelCase__     )
      if not streaming:
            SCREAMING_SNAKE_CASE		=						Dataset.from_list(list(lowerCAmelCase__     )     )

      SCREAMING_SNAKE_CASE		=						split_dataset_by_node(lowerCAmelCase__			,						rank=lowerCAmelCase__			,						world_size=lowerCAmelCase__     )
      SCREAMING_SNAKE_CASE		=						torch.utils.data.DataLoader(lowerCAmelCase__			,						num_workers=lowerCAmelCase__     )

      SCREAMING_SNAKE_CASE		=						NUM_SHARDS * NUM_ITEMS_PER_SHARD
      SCREAMING_SNAKE_CASE		=						full_size // world_size
      expected_local_size += int(rank < (full_size % world_size)     )

      SCREAMING_SNAKE_CASE		=						sum(1 for _ in dataloader     )
      if local_size != expected_local_size:
            raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
      main()









from PIL import Image



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Image:


      '''simple docstring'''


      def brightness(_SCREAMING_SNAKE_CASE     ) -> float:
            return 1_28 + level + (c - 1_28)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
      return img.point(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            SCREAMING_SNAKE_CASE_						       =	change_brightness(img, 1_0_0)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


