
"""simple docstring"""






import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
				from ...processing_utils import ProcessorMixin
				from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       =  logging.get_logger(__name__)

__UpperCAmelCase       =  {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Dict				 =						"owlvit_text_model"






			def __init__(					self       ,   __A=4_9408       ,   __A=512       ,   __A=2048       ,   __A=12       ,   __A=8       ,   __A=16       ,   __A="quick_gelu"       ,   __A=1E-5       ,   __A=0.0       ,   __A=0.0_2       ,   __A=1.0       ,   __A=0       ,   __A=4_9406       ,   __A=4_9407       ,   **__A       ,   )			->     Union[str, Any]:
						super().__init__(pad_token_id=__A       ,   bos_token_id=__A       ,   eos_token_id=__A       ,   **__A       )

						lowerCAmelCase_    :List[str]			     =  vocab_size
						lowerCAmelCase_    :Dict			     =  hidden_size
						lowerCAmelCase_    :str			     =  intermediate_size
						lowerCAmelCase_    :str			     =  num_hidden_layers
						lowerCAmelCase_    :int			     =  num_attention_heads
						lowerCAmelCase_    :Any			     =  max_position_embeddings
						lowerCAmelCase_    :List[str]			     =  hidden_act
						lowerCAmelCase_    :str			     =  layer_norm_eps
						lowerCAmelCase_    :Any			     =  attention_dropout
						lowerCAmelCase_    :str			     =  initializer_range
						lowerCAmelCase_    :str			     =  initializer_factor






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       ,   **__A       )			->     "PretrainedConfig":
						cls._set_token_in_kwargs(__A       )

						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[Any]			     =  cls.get_config_dict(__A       ,   **__A       )

						# get the text config dict if we are loading from OwlViTConfig
						if config_dict.get("""model_type"""       ) == "owlvit":
									lowerCAmelCase_    :Optional[int]			     =  config_dict["""text_config"""]

						if "model_type" in config_dict and hasattr(cls       ,   """model_type"""       ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

						return cls.from_dict(__A       ,   **__A       )




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :str				 =						"owlvit_vision_model"






			def __init__(					self       ,   __A=768       ,   __A=3072       ,   __A=12       ,   __A=12       ,   __A=3       ,   __A=768       ,   __A=32       ,   __A="quick_gelu"       ,   __A=1E-5       ,   __A=0.0       ,   __A=0.0_2       ,   __A=1.0       ,   **__A       ,   )			->     Any:
						super().__init__(**__A       )

						lowerCAmelCase_    :int			     =  hidden_size
						lowerCAmelCase_    :Optional[int]			     =  intermediate_size
						lowerCAmelCase_    :Dict			     =  num_hidden_layers
						lowerCAmelCase_    :int			     =  num_attention_heads
						lowerCAmelCase_    :Optional[int]			     =  num_channels
						lowerCAmelCase_    :Any			     =  image_size
						lowerCAmelCase_    :Union[str, Any]			     =  patch_size
						lowerCAmelCase_    :Optional[int]			     =  hidden_act
						lowerCAmelCase_    :int			     =  layer_norm_eps
						lowerCAmelCase_    :Tuple			     =  attention_dropout
						lowerCAmelCase_    :Tuple			     =  initializer_range
						lowerCAmelCase_    :Dict			     =  initializer_factor






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       ,   **__A       )			->     "PretrainedConfig":
						cls._set_token_in_kwargs(__A       )

						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[Any]			     =  cls.get_config_dict(__A       ,   **__A       )

						# get the vision config dict if we are loading from OwlViTConfig
						if config_dict.get("""model_type"""       ) == "owlvit":
									lowerCAmelCase_    :List[Any]			     =  config_dict["""vision_config"""]

						if "model_type" in config_dict and hasattr(cls       ,   """model_type"""       ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

						return cls.from_dict(__A       ,   **__A       )




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :str				 =						"owlvit"
			UpperCAmelCase_  :int				 =						True






			def __init__(					self       ,   __A=None       ,   __A=None       ,   __A=512       ,   __A=2.6_5_9_2       ,   __A=True       ,   **__A       ,   )			->     Tuple:
						super().__init__(**__A       )

						if text_config is None:
									lowerCAmelCase_    :Optional[Any]			     =  {}
									logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values."""       )

						if vision_config is None:
									lowerCAmelCase_    :List[Any]			     =  {}
									logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values."""       )

						lowerCAmelCase_    :Tuple			     =  OwlViTTextConfig(**__A       )
						lowerCAmelCase_    :Dict			     =  OwlViTVisionConfig(**__A       )

						lowerCAmelCase_    :Any			     =  projection_dim
						lowerCAmelCase_    :int			     =  logit_scale_init_value
						lowerCAmelCase_    :List[str]			     =  return_dict
						lowerCAmelCase_    :Optional[int]			     =  1.0






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       ,   **__A       )			->     "PretrainedConfig":
						cls._set_token_in_kwargs(__A       )

						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[int]			     =  cls.get_config_dict(__A       ,   **__A       )

						if "model_type" in config_dict and hasattr(cls       ,   """model_type"""       ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

						return cls.from_dict(__A       ,   **__A       )






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       ,   __A       ,   **__A       )			->     Optional[int]:
						lowerCAmelCase_    :Optional[int]			     =  {}
						lowerCAmelCase_    :int			     =  text_config
						lowerCAmelCase_    :Dict			     =  vision_config

						return cls.from_dict(__A       ,   **__A       )






			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :List[Any]			     =  copy.deepcopy(self.__dict__       )
						lowerCAmelCase_    :str			     =  self.text_config.to_dict()
						lowerCAmelCase_    :Tuple			     =  self.vision_config.to_dict()
						lowerCAmelCase_    :Optional[int]			     =  self.__class__.model_type
						return output




class 			_SCREAMING_SNAKE_CASE					(			A__							):






			@property
			def  __lowerCAmelCase (					self       )			->     Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
						    ]       )






			@property
			def  __lowerCAmelCase (					self       )			->     Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ("""logits_per_image""", {0: """batch"""}),
						        ("""logits_per_text""", {0: """batch"""}),
						        ("""text_embeds""", {0: """batch"""}),
						        ("""image_embeds""", {0: """batch"""}),
						    ]       )






			@property
			def  __lowerCAmelCase (					self       )			->     float:
						return 1E-4






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = -1       ,   __A = -1       ,   __A = None       ,   )			->     Mapping[str, Any]:
						lowerCAmelCase_    :Tuple			     =  super().generate_dummy_inputs(
						    processor.tokenizer       ,   batch_size=__A       ,   seq_length=__A       ,   framework=__A       )
						lowerCAmelCase_    :Tuple			     =  super().generate_dummy_inputs(
						    processor.image_processor       ,   batch_size=__A       ,   framework=__A       )
						return {**text_input_dict, **image_input_dict}






			@property
			def  __lowerCAmelCase (					self       )			->     int:
						return 14










from __future__ import annotations

import math







class  snake_case__:







						'''simple docstring'''




						def __init__( self      ,  __lowercase							)    ->			None:
										lowerCAmelCase_       :					str			 = size
										# approximate the overall size of segment tree with given value
										lowerCAmelCase_       :					Dict			 = [0 for i in range(0      ,  4 * size							)]
										# create array to store lazy update
										lowerCAmelCase_       :					Dict			 = [0 for i in range(0      ,  4 * size							)]
										lowerCAmelCase_       :					Optional[int]			 = [0 for i in range(0      ,  4 * size							)]  # flag for lazy update




						def   lowercase_       ( self      ,  __lowercase							)    ->			int:
										return idx * 2




						def   lowercase_       ( self      ,  __lowercase							)    ->			int:
										return idx * 2 + 1




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			None:
										if left_element == right_element:
														lowerCAmelCase_       :					Tuple			 = a[left_element - 1]
										else:
														lowerCAmelCase_       :					int			 = (left_element + right_element) // 2
														self.build(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
														self.build(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase							)
														lowerCAmelCase_       :					Any			 = max(
														    self.segment_tree[self.left(__lowercase							)]      ,  self.segment_tree[self.right(__lowercase							)]							)




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			bool:
										if self.flag[idx] is True:
														lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
														lowerCAmelCase_       :					Union[str, Any]			 = False
														if left_element != right_element:
																		lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
																		lowerCAmelCase_       :					Any			 = self.lazy[idx]
																		lowerCAmelCase_       :					List[str]			 = True
																		lowerCAmelCase_       :					Optional[Any]			 = True

										if right_element < a or left_element > b:
														return True
										if left_element >= a and right_element <= b:
														lowerCAmelCase_       :					Dict			 = val
														if left_element != right_element:
																		lowerCAmelCase_       :					Union[str, Any]			 = val
																		lowerCAmelCase_       :					List[Any]			 = val
																		lowerCAmelCase_       :					Optional[Any]			 = True
																		lowerCAmelCase_       :					List[str]			 = True
														return True
										lowerCAmelCase_       :					Optional[Any]			 = (left_element + right_element) // 2
										self.update(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										self.update(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										lowerCAmelCase_       :					int			 = max(
										    self.segment_tree[self.left(__lowercase							)]      ,  self.segment_tree[self.right(__lowercase							)]							)
										return True




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			int | float:
										if self.flag[idx] is True:
														lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
														lowerCAmelCase_       :					Optional[Any]			 = False
														if left_element != right_element:
																		lowerCAmelCase_       :					List[Any]			 = self.lazy[idx]
																		lowerCAmelCase_       :					Dict			 = self.lazy[idx]
																		lowerCAmelCase_       :					Optional[int]			 = True
																		lowerCAmelCase_       :					Optional[int]			 = True
										if right_element < a or left_element > b:
														return -math.inf
										if left_element >= a and right_element <= b:
														return self.segment_tree[idx]
										lowerCAmelCase_       :					List[Any]			 = (left_element + right_element) // 2
										lowerCAmelCase_       :					Tuple			 = self.query(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										lowerCAmelCase_       :					List[Any]			 = self.query(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										return max(__lowercase      ,  __lowercase							)




						def __str__( self							)    ->			str:
										return str([self.query(1      ,  1      ,  self.size      ,  __lowercase      ,  __lowercase							) for i in range(1      ,  self.size + 1							)]							)


if __name__ == "__main__":
						_UpperCAmelCase	:     str   =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
						_UpperCAmelCase	:     List[str]   =15
						_UpperCAmelCase	:     Any   =SegmentTree(size)
						segt.build(1, 1, size, A)
						print(segt.query(1, 1, size, 4, 6))
						print(segt.query(1, 1, size, 7, 11))
						print(segt.query(1, 1, size, 7, 12))
						segt.update(1, 1, size, 1, 3, 111)
						print(segt.query(1, 1, size, 1, 15))
						segt.update(1, 1, size, 7, 8, 235)
						print(segt)