




from copy import deepcopy


class    lowerCamelCase    :






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   list[int] | None = None       ,						lowerCamelCase_			:   int | None = None  )						->   None:

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

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   list[int]  )						->   None:
								__magic_name__							:	str    							=							len(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							deepcopy(lowerCamelCase_  )
								for i in range(1       ,						self.size  ):
													__magic_name__							:	Optional[Any]    							=							self.next_(lowerCamelCase_  )
													if j < self.size:
																		self.tree[j] += self.tree[i]

			def 							UpperCAmelCase__       ( self			:   Any  )						->   list[int]:
								__magic_name__							:	Dict    							=							self.tree[:]
								for i in range(self.size - 1       ,						0       ,						-1  ):
													__magic_name__							:	Dict    							=							self.next_(lowerCamelCase_  )
													if j < self.size:
																		arr[j] -= arr[i]
								return arr

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   int  )						->   int:
								return index + (index & (-index))

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   int  )						->   int:
								return index - (index & (-index))

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int  )						->   None:
								if index == 0:
													self.tree[0] += value
													return
								while index < self.size:
													self.tree[index] += value
													__magic_name__							:	Tuple    							=							self.next_(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int  )						->   None:
								self.add(lowerCamelCase_       ,						value - self.get(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   int  )						->   int:
								if right == 0:
													return 0
								__magic_name__							:	Tuple    							=							self.tree[0]
								right -= 1  # make right inclusive
								while right > 0:
													result += self.tree[right]
													__magic_name__							:	Tuple    							=							self.prev(lowerCamelCase_  )
								return result

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int  )						->   int:
								return self.prefix(lowerCamelCase_  ) - self.prefix(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int  )						->   int:
								return self.query(lowerCamelCase_       ,						index + 1  )


			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   int  )						->   int:
								value -= self.tree[0]
								if value < 0:
													return -1

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

								__magic_name__							:	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_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[Any]				=			{
    '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
    '''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			['''Speech2TextTokenizer''']

try:
	if not is_speech_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			['''Speech2TextFeatureExtractor''']

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSpeech2TextForConditionalGeneration''',
	    '''TFSpeech2TextModel''',
	    '''TFSpeech2TextPreTrainedModel''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''Speech2TextForConditionalGeneration''',
	    '''Speech2TextModel''',
	    '''Speech2TextPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
	from .processing_speech_to_text import SpeechaTextProcessor

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_speech_to_text import SpeechaTextTokenizer

	try:
		if not is_speech_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_speech_to_text import (
		    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSpeechaTextForConditionalGeneration,
		    TFSpeechaTextModel,
		    TFSpeechaTextPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_to_text import (
		    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SpeechaTextForConditionalGeneration,
		    SpeechaTextModel,
		    SpeechaTextPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						List[str]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



