
"""simple docstring"""




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
    from .testing_utils import (
        floats_tensor,
        load_hf_numpy,
        load_image,
        load_numpy,
        load_pt,
        nightly,
        parse_flag_from_env,
        print_tensor_test,
        require_torch_a,
        require_torch_gpu,
        skip_mps,
        slow,
        torch_all_close,
        torch_device,
    )
    from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


a		:					List[Any]    	=					get_logger(__name__)



def 						__magic_name__		(				UpperCamelCase    :		str    )    ->	int:
    if version.parse(UpperCamelCase    ) < version.parse(UpperCamelCase    ):
        if "dev" in min_version:
            a__    = (
                'This example requires a source install from HuggingFace diffusers (see '
                '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
            )
        else:
            a__    = f'This example requires a minimum version of {min_version},'
        error_message += f' but the version found is {__version__}.\n'
        raise ImportError(UpperCamelCase    )




"""simple docstring"""




import math







class 						lowercase:



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         a__    = len(__SCREAMING_SNAKE_CASE      )
         a__    = [0] * (
             4 * self.N
         )  # approximate the overall size of segment tree with array N
         self.build(1    ,							0    ,							self.N - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[Any]:







         """simple docstring"""



         return idx * 2



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:







         """simple docstring"""



         return idx * 2 + 1



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:  # noqa: E741







         """simple docstring"""



         if l == r:
             a__    = A[l]
         else:
             a__    = (l + r) // 2
             self.build(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
             self.build(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE      )
             a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    int:







         """simple docstring"""



         return self.update_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1    ,							__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[int]:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return True
         if l == r:
             a__    = val
             return True
         a__    = (l + r) // 2
         self.update_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.update_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = max(self.st[self.left(__SCREAMING_SNAKE_CASE      )]    ,							self.st[self.right(__SCREAMING_SNAKE_CASE      )]      )
         return True



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    List[str]:







         """simple docstring"""



         return self.query_recursive(1    ,							0    ,							self.N - 1    ,							a - 1    ,							b - 1      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Any:  # noqa: E741







         """simple docstring"""



         if r < a or l > b:
             return -math.inf
         if l >= a and r <= b:
             return self.st[idx]
         a__    = (l + r) // 2
         a__    = self.query_recursive(self.left(__SCREAMING_SNAKE_CASE      )    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self.query_recursive(self.right(__SCREAMING_SNAKE_CASE      )    ,							mid + 1    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         return max(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )




     def  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



         a__    = []
         for i in range(1    ,							N + 1      ):
             show_list += [self.query(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )]
         print(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
    a		:					Union[str, Any]    	=					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    a		:					Dict    	=					15
    a		:					Tuple    	=					SegmentTree(A)
    print(segt.query(4, 6))
    print(segt.query(7, 11))
    print(segt.query(7, 12))
    segt.update(1, 3, 1_11)
    print(segt.query(1, 15))
    segt.update(7, 8, 2_35)
    segt.show_data()


