

from copy import deepcopy



class    _a						:


					'''simple docstring'''






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

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






					def   __A      (			self   ,	A__      ):
						A__							:   Dict					       =		len(A__      )
						A__							:   int					       =		deepcopy(A__      )
						for i in range(1   ,	self.size      ):
							A__							:   List[Any]					       =		self.next_(A__      )
							if j < self.size:
								self.tree[j] += self.tree[i]






					def   __A      (			self      ):
						A__							:   int					       =		self.tree[:]
						for i in range(self.size - 1   ,	0   ,	-1      ):
							A__							:   List[Any]					       =		self.next_(A__      )
							if j < self.size:
								arr[j] -= arr[i]
						return arr






					@staticmethod
					def   __A      (			A__      ):
						return index + (index & (-index))






					@staticmethod
					def   __A      (			A__      ):
						return index - (index & (-index))






					def   __A      (			self   ,	A__   ,	A__      ):
						if index == 0:
							self.tree[0] += value
							return
						while index < self.size:
							self.tree[index] += value
							A__							:   List[str]					       =		self.next_(A__      )






					def   __A      (			self   ,	A__   ,	A__      ):
						self.add(A__   ,	value - self.get(A__      )      )






					def   __A      (			self   ,	A__      ):
						if right == 0:
							return 0
						A__							:   Union[str, Any]					       =		self.tree[0]
						right -= 1  # make right inclusive
						while right > 0:
							result += self.tree[right]
							A__							:   int					       =		self.prev(A__      )
						return result






					def   __A      (			self   ,	A__   ,	A__      ):
						return self.prefix(A__      ) - self.prefix(A__      )






					def   __A      (			self   ,	A__      ):
						return self.query(A__   ,	index + 1      )






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

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

						A__							:   Union[str, Any]					       =		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()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_	:			Optional[int]  	=      {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['FNetTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Tuple  	=      ['FNetTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[Any]  	=      [
	    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FNetForMaskedLM',
	    'FNetForMultipleChoice',
	    'FNetForNextSentencePrediction',
	    'FNetForPreTraining',
	    'FNetForQuestionAnswering',
	    'FNetForSequenceClassification',
	    'FNetForTokenClassification',
	    'FNetLayer',
	    'FNetModel',
	    'FNetPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet import FNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_fnet_fast import FNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fnet import (
		    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FNetForMaskedLM,
		    FNetForMultipleChoice,
		    FNetForNextSentencePrediction,
		    FNetForPreTraining,
		    FNetForQuestionAnswering,
		    FNetForSequenceClassification,
		    FNetForTokenClassification,
		    FNetLayer,
		    FNetModel,
		    FNetPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



