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


__lowerCamelCase    :				Any							     =	4
__lowerCamelCase    :				Optional[int]							     =	3







class __snake_case       (		_lowercase	):
   pass





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Union[str, Any]:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      )							->      str:







  """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=snake_case__			)
  parser.add_argument("""--local_rank"""				,  type=snake_case__			)
  parser.add_argument("""--num_workers"""				,  type=snake_case__				,  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(snake_case__			)]}
  SCREAMING_SNAKE_CASE__    							=   IterableDataset.from_generator(snake_case__				,  gen_kwargs=snake_case__			)
  if not streaming:
    SCREAMING_SNAKE_CASE__    							=   Dataset.from_list(list(snake_case__			)			)

  SCREAMING_SNAKE_CASE__    							=   split_dataset_by_node(snake_case__				,  rank=snake_case__				,  world_size=snake_case__			)
  SCREAMING_SNAKE_CASE__    							=   torch.utils.data.DataLoader(snake_case__				,  num_workers=snake_case__			)

  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 math import log

from scipy.constants import Boltzmann, physical_constants

__lowerCamelCase    :				int							     =	300  # TEMPERATURE (unit = K)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  )							->      float:







  """simple docstring"""





  if donor_conc <= 0:
    raise ValueError("""Donor concentration should be positive"""			)
  elif acceptor_conc <= 0:
    raise ValueError("""Acceptor concentration should be positive"""			)
  elif intrinsic_conc <= 0:
    raise ValueError("""Intrinsic concentration should be positive"""			)
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        """Donor concentration should be greater than intrinsic concentration"""			)
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        """Acceptor concentration should be greater than intrinsic concentration"""			)
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2			)
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
