

"""simple docstring"""



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE_					:	Tuple							  =						logging.getLogger(__name__)



@dataclass
class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					field(
        default=0.0,	metadata={"help": "The label smoothing epsilon to apply (if not zero)."}				)
    UpperCAmelCase						 =					field(default=_lowerCamelCase,	metadata={"help": "Whether to SortishSamler or not."}				)
    UpperCAmelCase						 =					field(
        default=_lowerCamelCase,	metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}				)
    UpperCAmelCase						 =					field(default=_lowerCamelCase,	metadata={"help": "whether to use adafactor"}				)
    UpperCAmelCase						 =					field(
        default=_lowerCamelCase,	metadata={"help": "Encoder layer dropout probability. Goes into model.config."}				)
    UpperCAmelCase						 =					field(
        default=_lowerCamelCase,	metadata={"help": "Decoder layer dropout probability. Goes into model.config."}				)
    UpperCAmelCase						 =					field(default=_lowerCamelCase,	metadata={"help": "Dropout probability. Goes into model.config."}				)
    UpperCAmelCase						 =					field(
        default=_lowerCamelCase,	metadata={"help": "Attention dropout probability. Goes into model.config."}				)
    UpperCAmelCase						 =					field(
        default="linear",	metadata={"help": F'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'},	)









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Optional[Any]			):
     A__										=  [0 for i in range(r + 1			)]
     # nc0 = 1
     A__										=  1
     for i in range(1					,   n + 1			):
          # to compute current row from previous row.
          A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          while j > 0:
               c[j] += c[j - 1]
               j -= 1
     return c[r]

print(binomial_coefficient(n=1_0, r=5))






