





# Algorithm for the pigeonhole sorting













def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]   )   ->			int:
    UpperCamelCase__							:       Dict      =   min(__UpperCAmelCase   )  # min() finds the minimum value
    UpperCamelCase__							:       str      =   max(__UpperCAmelCase   )  # max() finds the maximum value

    UpperCamelCase__							:       str      =   max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    UpperCamelCase__							:       List[Any]      =   [0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(__UpperCAmelCase	,     __UpperCAmelCase   ), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    UpperCamelCase__							:       List[Any]      =   0
    for count in range(__UpperCAmelCase   ):
        while holes[count] > 0:
            holes[count] -= 1
            UpperCamelCase__							:       Any      =   count + min_val
            i += 1




def 	lowerCAmelCase_					(						)   ->			Dict:
    UpperCamelCase__							:       Tuple      =   [8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(__UpperCAmelCase   )
    print('''Sorted order is:'''	,     ''' '''.join(__UpperCAmelCase   )   )


if __name__ == "__main__":
  main()








import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[Union[str, Path, GenerationConfig]]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

