


'''simple docstring'''
from copy import deepcopy
class   lowercase      :

		"""simple docstring"""

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

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					len(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					deepcopy(UpperCamelCase_				)
							for i in range(1       ,  self.size				):
												UpperCamelCase__  :List[str]				   =					self.next_(UpperCamelCase_				)
												if j < self.size:
																	self.tree[j] += self.tree[i]




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		@staticmethod
		def        lowerCAmelCase__							(						UpperCamelCase_				):

							'''simple docstring'''


							return index + (index & (-index))




		@staticmethod
		def        lowerCAmelCase__							(						UpperCamelCase_				):

							'''simple docstring'''


							return index - (index & (-index))




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							self.add(UpperCamelCase_       ,  value - self.get(UpperCamelCase_				)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							return self.prefix(UpperCamelCase_				) - self.prefix(UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							return self.query(UpperCamelCase_       ,  index + 1				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							value -= self.tree[0]
							if value < 0:
												return -1

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

							UpperCamelCase__  :str				   =					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'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__snake_case				 =	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoForCausalLM''',
		    '''GPTNeoForQuestionAnswering''',
		    '''GPTNeoForSequenceClassification''',
		    '''GPTNeoForTokenClassification''',
		    '''GPTNeoModel''',
		    '''GPTNeoPreTrainedModel''',
		    '''load_tf_weights_in_gpt_neo''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxGPTNeoForCausalLM''',
		    '''FlaxGPTNeoModel''',
		    '''FlaxGPTNeoPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neo import (
				    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoForCausalLM,
				    GPTNeoForQuestionAnswering,
				    GPTNeoForSequenceClassification,
				    GPTNeoForTokenClassification,
				    GPTNeoModel,
				    GPTNeoPreTrainedModel,
				    load_tf_weights_in_gpt_neo,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
		import sys

		__snake_case				 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)