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


a_    					=       logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 		_lowercase						(					snake_case_       ):
 lowercase				     =   field(default=snake_case_						,      metadata={'help': 'Whether to use SortishSampler or not.'}       )
 lowercase				     =   field(
     default=snake_case_						,      metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}       )
 lowercase				     =   field(
     default=snake_case_						,      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.'
         )
     }						,      )
 lowercase				     =   field(
     default=snake_case_						,      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.'
         )
     }						,      )
 lowercase				     =   field(
     default=snake_case_						,      metadata={
         'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
     }						,      )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					super().to_dict()
  for k, v in d.items():
   if isinstance(lowerCamelCase_   ,      lowerCamelCase_			):
    UpperCamelCase_					:						List[Any] =					v.to_dict()
  return d








def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					if any(not isinstance(A ,     A       ) or x < 0 for x in sequence       ):
										raise TypeError("Sequence must be list of non-negative integers"       )
					for _ in range(len(A       )       ):
										for i, (rod_upper, rod_lower) in enumerate(zip(A ,     sequence[1:]       )       ):
															if rod_upper > rod_lower:
																				sequence[i] -= rod_upper - rod_lower
																				sequence[i + 1] += rod_upper - rod_lower
					return sequence


if __name__ == "__main__":
				assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
