
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


SCREAMING_SNAKE_CASE							:Dict							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{
    '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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "owlvit_text_model"






	def __init__(       self					: Dict							,A					: Any=4_94_08							,A					: Optional[Any]=5_12							,A					: Optional[Any]=20_48							,A					: int=12							,A					: List[Any]=8							,A					: str=16							,A					: Dict="quick_gelu"							,A					: str=1E-5							,A					: Dict=0.0							,A					: str=0.02							,A					: List[Any]=1.0							,A					: List[Any]=0							,A					: int=4_94_06							,A					: List[Any]=4_94_07							,**A					: List[str]							,):
					super().__init__(pad_token_id=A							,bos_token_id=A							,eos_token_id=A							,**A )

					__A   			= vocab_size
					__A   			= hidden_size
					__A   			= intermediate_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= max_position_embeddings
					__A   			= hidden_act
					__A   			= layer_norm_eps
					__A   			= attention_dropout
					__A   			= initializer_range
					__A   			= initializer_factor







	@classmethod
	def 				UpperCamelCase_  (       cls					: List[str]							,A					: Union[str, os.PathLike]							,**A					: Optional[Any] ):
					cls._set_token_in_kwargs(A )

					__A			,							__A   			= cls.get_config_dict(A							,**A )

					# get the text config dict if we are loading from OwlViTConfig
					if config_dict.get("model_type" ) == "owlvit":
									__A   			= 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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "owlvit_vision_model"






	def __init__(       self					: List[Any]							,A					: str=7_68							,A					: Tuple=30_72							,A					: List[str]=12							,A					: str=12							,A					: List[str]=3							,A					: List[str]=7_68							,A					: Optional[Any]=32							,A					: Optional[int]="quick_gelu"							,A					: List[str]=1E-5							,A					: Optional[int]=0.0							,A					: Union[str, Any]=0.02							,A					: Optional[Any]=1.0							,**A					: Union[str, Any]							,):
					super().__init__(**A )

					__A   			= hidden_size
					__A   			= intermediate_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= num_channels
					__A   			= image_size
					__A   			= patch_size
					__A   			= hidden_act
					__A   			= layer_norm_eps
					__A   			= attention_dropout
					__A   			= initializer_range
					__A   			= initializer_factor







	@classmethod
	def 				UpperCamelCase_  (       cls					: Union[str, Any]							,A					: Union[str, os.PathLike]							,**A					: List[Any] ):
					cls._set_token_in_kwargs(A )

					__A			,							__A   			= cls.get_config_dict(A							,**A )

					# get the vision config dict if we are loading from OwlViTConfig
					if config_dict.get("model_type" ) == "owlvit":
									__A   			= 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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "owlvit"
	snake_case_					    =     True






	def __init__(       self					: Any							,A					: List[Any]=None							,A					: Tuple=None							,A					: Optional[Any]=5_12							,A					: Tuple=2.65_92							,A					: Dict=True							,**A					: Optional[Any]							,):
					super().__init__(**A )

					if text_config is None:
									__A   			= {}
									logger.info("text_config is None. Initializing the OwlViTTextConfig with default values." )

					if vision_config is None:
									__A   			= {}
									logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values." )

					__A   			= OwlViTTextConfig(**A )
					__A   			= OwlViTVisionConfig(**A )

					__A   			= projection_dim
					__A   			= logit_scale_init_value
					__A   			= return_dict
					__A   			= 1.0






	@classmethod
	def 				UpperCamelCase_  (       cls					: List[Any]							,A					: Union[str, os.PathLike]							,**A					: List[Any] ):
					cls._set_token_in_kwargs(A )

					__A			,							__A   			= 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 				UpperCamelCase_  (       cls					: Dict							,A					: Dict							,A					: Dict							,**A					: Any ):
					__A   			= {}
					__A   			= text_config
					__A   			= vision_config

					return cls.from_dict(A							,**A )







	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= copy.deepcopy(self.__dict__ )
					__A   			= self.text_config.to_dict()
					__A   			= self.vision_config.to_dict()
					__A   			= self.__class__.model_type
					return output





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					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 				UpperCamelCase_  (       self					: List[str] ):
					return OrderedDict(
					    [
					        ("logits_per_image", {0: "batch"}),
					        ("logits_per_text", {0: "batch"}),
					        ("text_embeds", {0: "batch"}),
					        ("image_embeds", {0: "batch"}),
					    ] )






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					return 1E-4






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: "ProcessorMixin"							,A					: int = -1							,A					: int = -1							,A					: Optional["TensorType"] = None							,):
					__A   			= super().generate_dummy_inputs(
					    processor.tokenizer							,batch_size=A							,seq_length=A							,framework=A )
					__A   			= super().generate_dummy_inputs(
					    processor.image_processor							,batch_size=A							,framework=A )
					return {**text_input_dict, **image_input_dict}







	@property
	def 				UpperCamelCase_  (       self					: Any ):
					return 14


import math


def 						UpperCAmelCase			(	a_		,   a_ = 0		,   a_ = 0						)		->	list:







				"""simple docstring"""



				__A   			= end or len(a_						)
				for i in range(a_		,   a_						):
								__A   			= i
								__A   			= array[i]
								while temp_index != start and temp_index_value < array[temp_index - 1]:
												__A   			= array[temp_index - 1]
												temp_index -= 1
								__A   			= temp_index_value
				return array


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	None:  # Max Heap







				"""simple docstring"""



				__A   			= index
				__A   			= 2 * index + 1  # Left Node
				__A   			= 2 * index + 2  # Right Node

				if left_index < heap_size and array[largest] < array[left_index]:
								__A   			= left_index

				if right_index < heap_size and array[largest] < array[right_index]:
								__A   			= right_index

				if largest != index:
								__A			,							__A   			= array[largest], array[index]
								heapify(a_		,   a_		,   a_						)


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				__A   			= len(a_						)

				for i in range(n // 2		,   -1		,   -1						):
								heapify(a_		,   a_		,   a_						)

				for i in range(n - 1		,   0		,   -1						):
								__A			,							__A   			= array[0], array[i]
								heapify(a_		,   0		,   a_						)

				return array


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_						)		->	int:







				"""simple docstring"""



				if (array[first_index] > array[middle_index]) != (
				    array[first_index] > array[last_index]
				):
								return array[first_index]
				elif (array[middle_index] > array[first_index]) != (
				    array[middle_index] > array[last_index]
				):
								return array[middle_index]
				else:
								return array[last_index]


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_						)		->	int:







				"""simple docstring"""



				__A   			= low
				__A   			= high
				while True:
								while array[i] < pivot:
												i += 1
								j -= 1
								while pivot < array[j]:
												j -= 1
								if i >= j:
												return i
								__A			,							__A   			= array[j], array[i]
								i += 1


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) == 0:
								return array
				__A   			= 2 * math.ceil(math.loga(len(a_						)						)						)
				__A   			= 1_6
				return intro_sort(a_		,   0		,   len(a_						)		,   a_		,   a_						)


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_						)		->	list:







				"""simple docstring"""



				while end - start > size_threshold:
								if max_depth == 0:
												return heap_sort(a_						)
								max_depth -= 1
								__A   			= median_of_a(a_		,   a_		,   start + ((end - start) // 2) + 1		,   end - 1						)
								__A   			= partition(a_		,   a_		,   a_		,   a_						)
								intro_sort(a_		,   a_		,   a_		,   a_		,   a_						)
								__A   			= p
				return insertion_sort(a_		,   a_		,   a_						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE							:List[Any]							 =				input('Enter numbers separated by a comma : ').strip()
					SCREAMING_SNAKE_CASE							:str							 =				[float(item) for item in user_input.split(',')]
					print(sort(unsorted))
