'''simple docstring'''

from collections.abc import Sequence
from queue import Queue
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A=None		,						__A=None				) ->     List[Any]:
								_lowerCAmelCase										=start
								_lowerCAmelCase										=end
								_lowerCAmelCase										=val
								_lowerCAmelCase										=(start + end) // 2
								_lowerCAmelCase										=left
								_lowerCAmelCase										=right






		def __repr__(   self				) ->     Optional[Any]:
								return F'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''

class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Dict:
								_lowerCAmelCase										=collection
								_lowerCAmelCase										=function
								if self.collection:
														_lowerCAmelCase										=self._build_tree(0		,						len(lowercase__				) - 1				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     str:
								self._update_tree(self.root		,						lowercase__		,						lowercase__				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:
								return self._query_range(self.root		,						lowercase__		,						lowercase__				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:





								if start == end:
														return SegmentTreeNode(lowercase__		,						lowercase__		,						self.collection[start]				)
								_lowerCAmelCase										=(start + end) // 2
								_lowerCAmelCase										=self._build_tree(lowercase__		,						lowercase__				)
								_lowerCAmelCase										=self._build_tree(mid + 1		,						lowercase__				)
								return SegmentTreeNode(lowercase__		,						lowercase__		,						self.fn(left.val		,						right.val				)		,						lowercase__		,						lowercase__				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Any:





								if node.start == i and node.end == i:
														_lowerCAmelCase										=val
														return
								if i <= node.mid:
														self._update_tree(node.left		,						lowercase__		,						lowercase__				)
								else:
														self._update_tree(node.right		,						lowercase__		,						lowercase__				)
								_lowerCAmelCase										=self.fn(node.left.val		,						node.right.val				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     List[str]:





								if node.start == i and node.end == j:
														return node.val

								if i <= node.mid:
														if j <= node.mid:
																				# range in left child tree
																				return self._query_range(node.left		,						lowercase__		,						lowercase__				)
														else:
																				# range in left child tree and right child tree
																				return self.fn(
																				    self._query_range(node.left		,						lowercase__		,						node.mid				)		,						self._query_range(node.right		,						node.mid + 1		,						lowercase__				)		,						)
								else:
														# range in right child tree
														return self._query_range(node.right		,						lowercase__		,						lowercase__				)






		def 		UpperCamelCase__       (   self				) ->     str:





								if self.root is not None:
														_lowerCAmelCase										=Queue()
														queue.put(self.root				)
														while not queue.empty():
																				_lowerCAmelCase										=queue.get()
																				yield node

																				if node.left is not None:
																										queue.put(node.left				)

																				if node.right is not None:
																										queue.put(node.right				)


if __name__ == "__main__":
							import operator

							for fn in [operator.add, max, min]:
														print('''*''' * 50)
														lowercase_     		= SegmentTree([2, 1, 5, 3, 4], fn)
														for node in arr.traverse():
																					print(node)
														print()

														arr.update(1, 5)
														for node in arr.traverse():
																					print(node)
														print()

														print(arr.query_range(3, 4))  # 7
														print(arr.query_range(2, 2))  # 5
														print(arr.query_range(1, 3))  # 13
														print()






'''simple docstring'''

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,
)





