
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
   from ... import PreTrainedTokenizerBase, TensorType

__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		int						     =     "vision-encoder-decoder"
   A__					:		Tuple						     =     True



   def __init__(self		:					Optional[Any]    ,						**snake_case__		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


          super().__init__(**snake_case__ )
          if "encoder" not in kwargs or "decoder" not in kwargs:
                 raise ValueError(
                     f"""A configuraton of type {self.model_type} cannot be instantiated because """
                     f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )

          snake_case       :   int							=      kwargs.pop("encoder" )
          snake_case       :   Tuple							=      encoder_config.pop("model_type" )
          snake_case       :   int							=      kwargs.pop("decoder" )
          snake_case       :   Optional[Any]							=      decoder_config.pop("model_type" )

          snake_case       :   Tuple							=      AutoConfig.for_model(snake_case__    ,						**snake_case__ )
          snake_case       :   List[Any]							=      AutoConfig.for_model(snake_case__    ,						**snake_case__ )
          snake_case       :   List[Any]							=      True



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any]    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					PretrainedConfig    ,						**snake_case__		:					int )		->	PretrainedConfig:


          '''simple docstring'''


          logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
          snake_case       :   List[str]							=      True
          snake_case       :   Optional[Any]							=      True

          return cls(encoder=encoder_config.to_dict()    ,						decoder=decoder_config.to_dict()    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   Any							=      copy.deepcopy(self.__dict__ )
          snake_case       :   List[Any]							=      self.encoder.to_dict()
          snake_case       :   Tuple							=      self.decoder.to_dict()
          snake_case       :   Union[str, Any]							=      self.__class__.model_type
          return output






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     version.parse("1.11" )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ] )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	float:


          '''simple docstring'''


          return 1e-4



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}} )






class   UpperCAmelCase		(						A_ ):



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          snake_case       :   Dict							=      OrderedDict()
          snake_case       :   Optional[Any]							=      {0: "batch", 1: "past_decoder_sequence + sequence"}
          snake_case       :   Tuple							=      {0: "batch", 1: "past_decoder_sequence + sequence"}
          snake_case       :   List[Any]							=      {0: "batch", 1: "encoder_sequence"}

          return common_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					"PreTrainedTokenizerBase"    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional["TensorType"] = None    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          import torch

          snake_case       :   str							=      OrderedDict()

          snake_case       :   Dict							=      super().generate_dummy_inputs(
              snake_case__    ,						batch_size=snake_case__    ,						seq_length=snake_case__    ,						is_pair=snake_case__    ,						framework=snake_case__ )

          snake_case				,					snake_case       :   Dict							=      dummy_input["input_ids"].shape
          snake_case       :   Dict							=      (batch, encoder_sequence, self._config.encoder_hidden_size)
          snake_case       :   List[str]							=      dummy_input.pop("input_ids" )
          snake_case       :   Optional[int]							=      dummy_input.pop("attention_mask" )
          snake_case       :   Union[str, Any]							=      torch.zeros(snake_case__ )

          return common_inputs






class   UpperCAmelCase		(						A_ ):



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	None:


          '''simple docstring'''


          pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					PretrainedConfig )		->	OnnxConfig:


          '''simple docstring'''


          return VisionEncoderDecoderEncoderOnnxConfig(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					PretrainedConfig    ,						snake_case__		:					str = "default" )		->	OnnxConfig:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      encoder_config.hidden_size
          return VisionEncoderDecoderDecoderOnnxConfig(snake_case__    ,						snake_case__ )






from collections.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							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,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    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)
				__lowerCamelCase					:	int       =  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()
