


"""simple docstring"""
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


SCREAMING_SNAKE_CASE :    List[str]									=				logging.getLogger(__name__)



@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=field(default=__snake_case,				metadata={'help': 'Whether to use SortishSampler or not.'}				)
		lowerCamelCase__				=field(
		    default=__snake_case,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}				)
		lowerCamelCase__				=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.'
		        )
		    },				)
		lowerCamelCase__				=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.'
		        )
		    },				)
		lowerCamelCase__				=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					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				super().to_dict()
			for k, v in d.items():
				if isinstance(a_				,       a_					):
					__snake_case	:						Tuple						=				v.to_dict()
			return d













"""simple docstring"""



from __future__ import annotations

__A		:       List[Any]  				=		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A		:       str  				=		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		lowercase_ : List[Any]							       =   len(__snake_case	)

		for i in range(__snake_case	):
				lowercase_ : float							       =   -1
				for j in range(i + 1		,					__snake_case	):
						if arr[i] < arr[j]:
								lowercase_ : List[str]							       =   arr[j]
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		for i, outer in enumerate(__snake_case	):
				lowercase_ : float							       =   -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowercase_ : List[Any]							       =   inner
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   len(__snake_case	)
		lowercase_ : list[float]							       =   []
		lowercase_ : list[float]							       =   [-1] * arr_size

		for index in reversed(range(__snake_case	)	):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						lowercase_ : Optional[Any]							       =   stack[-1]
				stack.append(arr[index]	)
		return result


if __name__ == "__main__":
						from doctest import testmod
						from timeit import timeit

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						__A		:       int  				=		(
						    '''from __main__ import arr, next_greatest_element_slow, '''
						    '''next_greatest_element_fast, next_greatest_element'''
						)
						print(
						    '''next_greatest_element_slow():''',
						    timeit('''next_greatest_element_slow(arr)''', setup=setup),
						)
						print(
						    '''next_greatest_element_fast():''',
						    timeit('''next_greatest_element_fast(arr)''', setup=setup),
						)
						print(
						    '''     next_greatest_element():''',
						    timeit('''next_greatest_element(arr)''', setup=setup),
						)



