


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


_snake_case  :	List[Any]			      =       logging.get_logger(__name__)

_snake_case  :	int			      =       {
    "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	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[Any]  						=						"owlvit_text_model"

							def __init__(							self :						Union[str, Any]					,			lowerCamelCase :						List[Any]=49408					,			lowerCamelCase :						Union[str, Any]=512					,			lowerCamelCase :						Any=2048					,			lowerCamelCase :						Optional[int]=12					,			lowerCamelCase :						Any=8					,			lowerCamelCase :						List[Any]=16					,			lowerCamelCase :						List[str]="quick_gelu"					,			lowerCamelCase :						List[Any]=1E-5					,			lowerCamelCase :						Union[str, Any]=0.0					,			lowerCamelCase :						Union[str, Any]=0.02					,			lowerCamelCase :						Any=1.0					,			lowerCamelCase :						Optional[Any]=0					,			lowerCamelCase :						str=49406					,			lowerCamelCase :						Any=49407					,			**lowerCamelCase :						List[Any]					,			)			->					List[Any]:
														super().__init__(pad_token_id=lowerCamelCase					,			bos_token_id=lowerCamelCase					,			eos_token_id=lowerCamelCase					,			**lowerCamelCase       )

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






							@classmethod
							def 					__snake_case      (							cls :						List[str]					,			lowerCamelCase :						Union[str, os.PathLike]					,			**lowerCamelCase :						str       )			->					"PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase       )

														__snake_case				,    __snake_case	:    Any											=     cls.get_config_dict(lowerCamelCase					,			**lowerCamelCase       )

														# get the text config dict if we are loading from OwlViTConfig
														if config_dict.get("model_type"       ) == "owlvit":
																					__snake_case	:    List[str]											=     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(lowerCamelCase					,			**lowerCamelCase       )

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Any  						=						"owlvit_vision_model"

							def __init__(							self :						str					,			lowerCamelCase :						Dict=768					,			lowerCamelCase :						Dict=3072					,			lowerCamelCase :						Any=12					,			lowerCamelCase :						Tuple=12					,			lowerCamelCase :						Optional[Any]=3					,			lowerCamelCase :						List[str]=768					,			lowerCamelCase :						List[Any]=32					,			lowerCamelCase :						str="quick_gelu"					,			lowerCamelCase :						Optional[Any]=1E-5					,			lowerCamelCase :						Union[str, Any]=0.0					,			lowerCamelCase :						Dict=0.02					,			lowerCamelCase :						List[str]=1.0					,			**lowerCamelCase :						Dict					,			)			->					Tuple:
														super().__init__(**lowerCamelCase       )

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






							@classmethod
							def 					__snake_case      (							cls :						int					,			lowerCamelCase :						Union[str, os.PathLike]					,			**lowerCamelCase :						Dict       )			->					"PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase       )

														__snake_case				,    __snake_case	:    Optional[int]											=     cls.get_config_dict(lowerCamelCase					,			**lowerCamelCase       )

														# get the vision config dict if we are loading from OwlViTConfig
														if config_dict.get("model_type"       ) == "owlvit":
																					__snake_case	:    str											=     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(lowerCamelCase					,			**lowerCamelCase       )

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Union[str, Any]  						=						"owlvit"
							__UpperCAmelCase					:							List[Any]  						=						True

							def __init__(							self :						int					,			lowerCamelCase :						Dict=None					,			lowerCamelCase :						int=None					,			lowerCamelCase :						Optional[int]=512					,			lowerCamelCase :						List[str]=2.65_92					,			lowerCamelCase :						Optional[int]=True					,			**lowerCamelCase :						Union[str, Any]					,			)			->					List[Any]:
														super().__init__(**lowerCamelCase       )

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

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

														__snake_case	:    Tuple											=     OwlViTTextConfig(**lowerCamelCase       )
														__snake_case	:    Dict											=     OwlViTVisionConfig(**lowerCamelCase       )

														__snake_case	:    List[str]											=     projection_dim
														__snake_case	:    Union[str, Any]											=     logit_scale_init_value
														__snake_case	:    Union[str, Any]											=     return_dict
														__snake_case	:    Optional[int]											=     1.0

							@classmethod
							def 					__snake_case      (							cls :						Optional[int]					,			lowerCamelCase :						Union[str, os.PathLike]					,			**lowerCamelCase :						List[Any]       )			->					"PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase       )

														__snake_case				,    __snake_case	:    Tuple											=     cls.get_config_dict(lowerCamelCase					,			**lowerCamelCase       )

														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(lowerCamelCase					,			**lowerCamelCase       )

							@classmethod
							def 					__snake_case      (							cls :						Union[str, Any]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			**lowerCamelCase :						int       )			->					Dict:
														__snake_case	:    str											=     {}
														__snake_case	:    Tuple											=     text_config
														__snake_case	:    Any											=     vision_config

														return cls.from_dict(lowerCamelCase					,			**lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]       )			->					Dict:
														__snake_case	:    Any											=     copy.deepcopy(self.__dict__       )
														__snake_case	:    Any											=     self.text_config.to_dict()
														__snake_case	:    Tuple											=     self.vision_config.to_dict()
														__snake_case	:    Optional[Any]											=     self.__class__.model_type
														return output

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							@property
							def 					__snake_case      (							self :						int       )			->					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 					__snake_case      (							self :						Union[str, Any]       )			->					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 					__snake_case      (							self :						Optional[int]       )			->					float:
														return 1E-4

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






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





import unittest

import numpy as np







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			):
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)

							if shape_a[0] != shape_b[0]:
														__snake_case	:    Any											=     (
														    "Expected the same number of rows for A and B. "
														    F'Instead found A of size {shape_a} and B of size {shape_b}'
														)
														raise ValueError(__lowerCamelCase			)

							if shape_b[1] != shape_c[1]:
														__snake_case	:    int											=     (
														    "Expected the same number of columns for B and C. "
														    F'Instead found B of size {shape_b} and C of size {shape_c}'
														)
														raise ValueError(__lowerCamelCase			)

							__snake_case	:    str											=     pseudo_inv
							if a_inv is None:
														try:
																					__snake_case	:    Optional[Any]											=     np.linalg.inv(__lowerCamelCase			)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    "Input matrix A is not invertible. Cannot compute Schur complement."			)

							return mat_c - mat_b.T @ a_inv @ mat_b

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Tuple       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    str											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Dict											=     np.array([[2, 1], [6, 3]]       )

														__snake_case	:    Dict											=     schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    int											=     np.block([[a, b], [b.T, c]]       )

														__snake_case	:    Optional[int]											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Any											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Tuple											=     np.linalg.det(lowerCamelCase       )

														self.assertAlmostEqual(lowerCamelCase					,			det_a * det_s       )

							def 					__snake_case      (							self :						int       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Dict											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Tuple											=     np.array([[2, 1], [6, 3]]       )

														with self.assertRaises(lowerCamelCase       ):
																					schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )






							def 					__snake_case      (							self :						List[Any]       )			->					None:
														__snake_case	:    List[Any]											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Tuple											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    List[Any]											=     np.array([[2, 1, 3], [6, 3, 5]]       )

														with self.assertRaises(lowerCamelCase       ):
																					schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()

