






"""simple docstring"""



from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

A__	:    str			 = TypeVar('T')







class        lowercase__      (							Generic[T]       ):


			def __init__(				self					:  Tuple       ,	snake_case__					:  list[T]       ,	snake_case__					:  Callable[[T, T], T] ):
										lowerCamelCase_	:							Any | T									=None

										lowerCamelCase_	:							int									=len(snake_case__ )
										lowerCamelCase_	:							list[T]									=[any_type for _ in range(self.N )] + arr
										lowerCamelCase_	:							Tuple									=fnc
										self.build()


			def      UpperCAmelCase__						(				self					:  Tuple ):
										for p in range(self.N - 1       ,	0       ,	-1 ):
																	lowerCamelCase_	:							Dict									=self.fn(self.st[p * 2]       ,	self.st[p * 2 + 1] )


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  int       ,	snake_case__					:  T ):
										p += self.N
										lowerCamelCase_	:							str									=v
										while p > 1:
																	lowerCamelCase_	:							Optional[Any]									=p // 2
																	lowerCamelCase_	:							Any									=self.fn(self.st[p * 2]       ,	self.st[p * 2 + 1] )


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  int       ,	snake_case__					:  int ):  # noqa: E741
										lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=l + self.N, r + self.N

										lowerCamelCase_	:							T | None									=None
										while l <= r:
																	if l % 2 == 1:
																								lowerCamelCase_	:							Union[str, Any]									=self.st[l] if res is None else self.fn(snake_case__       ,	self.st[l] )
																	if r % 2 == 0:
																								lowerCamelCase_	:							Union[str, Any]									=self.st[r] if res is None else self.fn(snake_case__       ,	self.st[r] )
																	lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=(l + 1) // 2, (r - 1) // 2
										return res


if __name__ == "__main__":
						from functools import reduce

						A__	:    Any			 = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

						A__	:    int			 = {
						    0: 7,
						    1: 2,
						    2: 6,
						    3: -14,
						    4: 5,
						    5: 4,
						    6: 7,
						    7: -10,
						    8: 9,
						    9: 10,
						    10: 12,
						    11: 1,
						}

						A__	:    Any			 = SegmentTree(test_array, min)
						A__	:    Tuple			 = SegmentTree(test_array, max)
						A__	:    Any			 = SegmentTree(test_array, lambda a, b: a + b)

						def 			_snake_case    (					)    ->     None:
													for i in range(len(lowerCamelCase__  )  ):
																				for j in range(lowerCamelCase__    ,						len(lowerCamelCase__  )  ):
																											lowerCamelCase_	:							int									=reduce(lowerCamelCase__    ,						test_array[i : j + 1]  )
																											lowerCamelCase_	:							Optional[Any]									=reduce(lowerCamelCase__    ,						test_array[i : j + 1]  )
																											lowerCamelCase_	:							List[str]									=reduce(lambda lowerCamelCase__    ,						lowerCamelCase__  : a + b    ,						test_array[i : j + 1]  )
																											assert min_range == min_segment_tree.query(lowerCamelCase__    ,						lowerCamelCase__  )
																											assert max_range == max_segment_tree.query(lowerCamelCase__    ,						lowerCamelCase__  )
																											assert sum_range == sum_segment_tree.query(lowerCamelCase__    ,						lowerCamelCase__  )

						test_all_segments()

						for index, value in test_updates.items():
												A__	:    List[str]			 = value
												min_segment_tree.update(index, value)
												max_segment_tree.update(index, value)
												sum_segment_tree.update(index, value)
												test_all_segments()











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


A__	:    Optional[int]			 = {
    '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:
						A__	:    Dict			 = ['Speech2TextTokenizer']

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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[int]			 = [
						    '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

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



