





from copy import deepcopy

class 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	):


														if arr is None and size is not None:
																					UpperCamelCase__					  =      size
																					UpperCamelCase__					  =      [0] * size
														elif arr is not None:
																					self.init(UpperCamelCase_	)
														else:
																					raise ValueError("""Either arr or size must be specified"""	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      len(UpperCamelCase_	)
														UpperCamelCase__					  =      deepcopy(UpperCamelCase_	)
														for i in range(1	,			self.size	):
																					UpperCamelCase__					  =      self.next_(UpperCamelCase_	)
																					if j < self.size:
																												self.tree[j] += self.tree[i]






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.tree[:]
														for i in range(self.size - 1	,			0	,			-1	):
																					UpperCamelCase__					  =      self.next_(UpperCamelCase_	)
																					if j < self.size:
																												arr[j] -= arr[i]
														return arr






							@staticmethod
							def 					_lowerCamelCase		(    __lowerCAmelCase	):
														return index + (index & (-index))






							@staticmethod
							def 					_lowerCamelCase		(    __lowerCAmelCase	):
														return index - (index & (-index))






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):


														if index == 0:
																					self.tree[0] += value
																					return
														while index < self.size:
																					self.tree[index] += value
																					UpperCamelCase__					  =      self.next_(UpperCamelCase_	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														self.add(UpperCamelCase_	,			value - self.get(UpperCamelCase_	)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):


														if right == 0:
																					return 0
														UpperCamelCase__					  =      self.tree[0]
														right -= 1  # make right inclusive
														while right > 0:
																					result += self.tree[right]
																					UpperCamelCase__					  =      self.prev(UpperCamelCase_	)
														return result






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														return self.prefix(UpperCamelCase_	) - self.prefix(UpperCamelCase_	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														return self.query(UpperCamelCase_	,			index + 1	)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														value -= self.tree[0]
														if value < 0:
																					return -1

														UpperCamelCase__					  =      1  # Largest power of 2 <= size
														while j * 2 < self.size:
																					j *= 2

														UpperCamelCase__					  =      0

														while j > 0:
																					if i + j < self.size and self.tree[i + j] <= value:
																												value -= self.tree[i + j]
																												i += j
																					j //= 2
														return i


if __name__ == "__main__":
					import doctest

					doctest.testmod()








'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

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