





from __future__ import annotations

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

_lowerCAmelCase 				=     TypeVar("""T""")

class    _UpperCAmelCase							(		Generic[T]     ):
						def __init__(		self      ,       a__      ,       a__       ):
									A_     :				Any | T			      =	None

									A_     :				int			      =	len(a__       )
									A_     :				list[T]			      =	[any_type for _ in range(self.N       )] + arr
									A_     :				Optional[Any]			      =	fnc
									self.build()
						def 		_lowerCamelCase      (		self       ):
									for p in range(self.N - 1      ,       0      ,       -1       ):
												A_     :				Optional[Any]			      =	self.fn(self.st[p * 2]      ,       self.st[p * 2 + 1]       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):
									p += self.N
									A_     :				Tuple			      =	v
									while p > 1:
												A_     :				str			      =	p // 2
												A_     :				List[Any]			      =	self.fn(self.st[p * 2]      ,       self.st[p * 2 + 1]       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):  # noqa: E741
									A_     :				Optional[int]			      =	l + self.N, r + self.N

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


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

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

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

						_lowerCAmelCase 				=     SegmentTree(test_array, min)
						_lowerCAmelCase 				=     SegmentTree(test_array, max)
						_lowerCAmelCase 				=     SegmentTree(test_array, lambda a, b: a + b)



						def     _lowerCAmelCase		(  ):
									'''simple docstring'''


									for i in range(len(_lowerCAmelCase     )     ):
												for j in range(_lowerCAmelCase  ,len(_lowerCAmelCase     )     ):
															A_     :				Any			      =	reduce(_lowerCAmelCase  ,test_array[i : j + 1]     )
															A_     :				List[str]			      =	reduce(_lowerCAmelCase  ,test_array[i : j + 1]     )
															A_     :				List[Any]			      =	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():
												_lowerCAmelCase 				=     value
												min_segment_tree.update(index, value)
												max_segment_tree.update(index, value)
												sum_segment_tree.update(index, value)
												test_all_segments()













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






