






"""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


lowercase    			=       logging.get_logger(__name__)

lowercase    			=       {
    '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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''owlvit_text_model'''







				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						int=4_9_4_0_8						,    __lowerCamelCase							:						int=5_1_2						,    __lowerCamelCase							:						int=2_0_4_8						,    __lowerCamelCase							:						str=1_2						,    __lowerCamelCase							:						List[str]=8						,    __lowerCamelCase							:						Optional[Any]=1_6						,    __lowerCamelCase							:						Dict="quick_gelu"						,    __lowerCamelCase							:						Tuple=1e-5						,    __lowerCamelCase							:						Optional[Any]=0.0						,    __lowerCamelCase							:						Any=0.02						,    __lowerCamelCase							:						Any=1.0						,    __lowerCamelCase							:						str=0						,    __lowerCamelCase							:						Dict=4_9_4_0_6						,    __lowerCamelCase							:						str=4_9_4_0_7						,    **__lowerCamelCase							:						Dict						,    )				->					str:
										super().__init__(pad_token_id=__A						,    bos_token_id=__A						,    eos_token_id=__A						,    **__A	)

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







				@classmethod
				def     _snake_case					(							cls							:						int						,    __lowerCamelCase							:						Union[str, os.PathLike]						,    **__lowerCamelCase							:						Tuple	)				->					int:
										cls._set_token_in_kwargs(__A	)

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

										# get the text config dict if we are loading from OwlViTConfig
										if config_dict.get("model_type"	) == "owlvit":
																__magic_name__            =     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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''owlvit_vision_model'''







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						Optional[Any]=7_6_8						,    __lowerCamelCase							:						Any=3_0_7_2						,    __lowerCamelCase							:						int=1_2						,    __lowerCamelCase							:						Optional[int]=1_2						,    __lowerCamelCase							:						Optional[int]=3						,    __lowerCamelCase							:						List[Any]=7_6_8						,    __lowerCamelCase							:						List[str]=3_2						,    __lowerCamelCase							:						int="quick_gelu"						,    __lowerCamelCase							:						Dict=1e-5						,    __lowerCamelCase							:						Optional[Any]=0.0						,    __lowerCamelCase							:						str=0.02						,    __lowerCamelCase							:						Union[str, Any]=1.0						,    **__lowerCamelCase							:						int						,    )				->					Optional[Any]:
										super().__init__(**__A	)

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







				@classmethod
				def     _snake_case					(							cls							:						Dict						,    __lowerCamelCase							:						Union[str, os.PathLike]						,    **__lowerCamelCase							:						Tuple	)				->					List[str]:
										cls._set_token_in_kwargs(__A	)

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

										# get the vision config dict if we are loading from OwlViTConfig
										if config_dict.get("model_type"	) == "owlvit":
																__magic_name__            =     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 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				'''owlvit'''
				UpperCAmelCase__        =				True







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str=None						,    __lowerCamelCase							:						Tuple=None						,    __lowerCamelCase							:						str=5_1_2						,    __lowerCamelCase							:						Union[str, Any]=2.6592						,    __lowerCamelCase							:						List[Any]=True						,    **__lowerCamelCase							:						str						,    )				->					Optional[Any]:
										super().__init__(**__A	)

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

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

										__magic_name__            =     OwlViTTextConfig(**__A	)
										__magic_name__            =     OwlViTVisionConfig(**__A	)

										__magic_name__            =     projection_dim
										__magic_name__            =     logit_scale_init_value
										__magic_name__            =     return_dict
										__magic_name__            =     1.0







				@classmethod
				def     _snake_case					(							cls							:						Union[str, Any]						,    __lowerCamelCase							:						Union[str, os.PathLike]						,    **__lowerCamelCase							:						int	)				->					Dict:
										cls._set_token_in_kwargs(__A	)

										__magic_name__            =     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     _snake_case					(							cls							:						str						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Dict						,    **__lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     {}
										__magic_name__            =     text_config
										__magic_name__            =     vision_config

										return cls.from_dict(__A						,    **__A	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     copy.deepcopy(self.__dict__	)
										__magic_name__            =     self.text_config.to_dict()
										__magic_name__            =     self.vision_config.to_dict()
										__magic_name__            =     self.__class__.model_type
										return output







class 						A_							(      snake_case_  ):







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







				@property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										return 1e-4







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						"ProcessorMixin"						,    __lowerCamelCase							:						int = -1						,    __lowerCamelCase							:						int = -1						,    __lowerCamelCase							:						Optional["TensorType"] = None						,    )				->					List[str]:
										__magic_name__            =     super().generate_dummy_inputs(
										    processor.tokenizer						,    batch_size=__A						,    seq_length=__A						,    framework=__A	)
										__magic_name__            =     super().generate_dummy_inputs(
										    processor.image_processor						,    batch_size=__A						,    framework=__A	)
										return {**text_input_dict, **image_input_dict}







				@property
				def     _snake_case					(							self							:						Optional[Any]	)				->					Union[str, Any]:
										return 1_4











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



