



'''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__:     Optional[Any]										=    logging.get_logger(__name__)

UpperCamelCase__:     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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """owlvit_text_model"""







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

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







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

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

								# get the text config dict if we are loading from OwlViTConfig
								if config_dict.get('''model_type'''	) == "owlvit":
															UpperCAmelCase				: 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(__snake_case	,    **__snake_case	)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """owlvit_vision_model"""







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

								UpperCAmelCase				: Any											=	hidden_size
								UpperCAmelCase				: List[Any]											=	intermediate_size
								UpperCAmelCase				: Optional[Any]											=	num_hidden_layers
								UpperCAmelCase				: Dict											=	num_attention_heads
								UpperCAmelCase				: Any											=	num_channels
								UpperCAmelCase				: int											=	image_size
								UpperCAmelCase				: Any											=	patch_size
								UpperCAmelCase				: int											=	hidden_act
								UpperCAmelCase				: Optional[int]											=	layer_norm_eps
								UpperCAmelCase				: List[Any]											=	attention_dropout
								UpperCAmelCase				: Optional[Any]											=	initializer_range
								UpperCAmelCase				: Dict											=	initializer_factor







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

								UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	cls.get_config_dict(__snake_case	,    **__snake_case	)

								# get the vision config dict if we are loading from OwlViTConfig
								if config_dict.get('''model_type'''	) == "owlvit":
															UpperCAmelCase				: Optional[int]											=	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(__snake_case	,    **__snake_case	)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """owlvit"""
	lowerCamelCase__    				= True







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

								if text_config is None:
															UpperCAmelCase				: int											=	{}
															logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''	)

								if vision_config is None:
															UpperCAmelCase				: str											=	{}
															logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''	)

								UpperCAmelCase				: Optional[int]											=	OwlViTTextConfig(**__snake_case	)
								UpperCAmelCase				: Tuple											=	OwlViTVisionConfig(**__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	projection_dim
								UpperCAmelCase				: Tuple											=	logit_scale_init_value
								UpperCAmelCase				: Optional[Any]											=	return_dict
								UpperCAmelCase				: Optional[Any]											=	1.0







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

								UpperCAmelCase							,	UpperCAmelCase				: int											=	cls.get_config_dict(__snake_case	,    **__snake_case	)

								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(__snake_case	,    **__snake_case	)







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

								return cls.from_dict(__snake_case	,    **__snake_case	)







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

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






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







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







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










'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

UpperCamelCase__:     Tuple										=    [8, 5, 9, 7]
UpperCamelCase__:     int										=    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCamelCase__:     List[str]										=    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  list[int]	,    __snake_case    :  list[list[int]]	,    __snake_case    :  list[list[int]]	,    )    ->	None:
								UpperCAmelCase				: Union[str, Any]											=	claim_vector
								UpperCAmelCase				: Tuple											=	allocated_resources_table
								UpperCAmelCase				: List[str]											=	maximum_claim_table







	def   A			(		self    :  Optional[Any]	)    ->	list[int]:
								return [
								    sum(p_item[i] for p_item in self.__allocated_resources_table	)
								    for i in range(len(self.__allocated_resources_table[0]	)	)
								]







	def   A			(		self    :  Union[str, Any]	)    ->	list[int]:
								return np.array(self.__claim_vector	) - np.array(
								    self.__processes_resource_summation()	)







	def   A			(		self    :  str	)    ->	list[list[int]]:
								return [
								    list(np.array(self.__maximum_claim_table[i]	) - np.array(__snake_case	)	)
								    for i, allocated_resource in enumerate(self.__allocated_resources_table	)
								]







	def   A			(		self    :  List[Any]	)    ->	dict[int, list[int]]:
								return {self.__need().index(__snake_case	): i for i in self.__need()}







	def   A			(		self    :  Tuple	,    **__snake_case    :  Union[str, Any]	)    ->	None:
								UpperCAmelCase				: Tuple											=	self.__need()
								UpperCAmelCase				: Union[str, Any]											=	self.__allocated_resources_table
								UpperCAmelCase				: int											=	self.__available_resources()
								UpperCAmelCase				: Tuple											=	self.__need_index_manager()
								for kw, val in kwargs.items():
															if kw and val is True:
																						self.__pretty_data()
								print('''_''' * 50 + '''\n'''	)
								while need_list:
															UpperCAmelCase				: Dict											=	False
															for each_need in need_list:
																						UpperCAmelCase				: Optional[int]											=	True
																						for index, need in enumerate(__snake_case	):
																													if need > available_resources[index]:
																																				UpperCAmelCase				: List[str]											=	False
																																				break
																						if execution:
																													UpperCAmelCase				: str											=	True
																													# get the original index of the process from ind_ctrl db
																													for original_need_index, need_clone in need_index_manager.items():
																																				if each_need == need_clone:
																																											UpperCAmelCase				: Union[str, Any]											=	original_need_index
																													print(F"""Process {process_number + 1} is executing."""	)
																													# remove the process run from stack
																													need_list.remove(__snake_case	)
																													# update available/freed resources stack
																													UpperCAmelCase				: Optional[int]											=	np.array(__snake_case	) + np.array(
																													    alloc_resources_table[process_number]	)
																													print(
																													    '''Updated available resource stack for processes: '''
																													    + ''' '''.join([str(__snake_case	) for x in available_resources]	)	)
																													break
															if safe:
																						print('''The process is in a safe state.\n'''	)
															else:
																						print('''System in unsafe state. Aborting...\n'''	)
																						break







	def   A			(		self    :  Dict	)    ->	Optional[Any]:
								print(''' ''' * 9 + '''Allocated Resource Table'''	)
								for item in self.__allocated_resources_table:
															print(
															    F"""P{self.__allocated_resources_table.index(__snake_case	) + 1}"""
															    + ''' '''.join(F"""{it:>8}""" for it in item	)
															    + '''\n'''	)
								print(''' ''' * 9 + '''System Resource Table'''	)
								for item in self.__maximum_claim_table:
															print(
															    F"""P{self.__maximum_claim_table.index(__snake_case	) + 1}"""
															    + ''' '''.join(F"""{it:>8}""" for it in item	)
															    + '''\n'''	)
								print(
								    '''Current Usage by Active Processes: '''
								    + ''' '''.join(str(__snake_case	) for x in self.__claim_vector	)	)
								print(
								    '''Initial Available Resources:       '''
								    + ''' '''.join(str(__snake_case	) for x in self.__available_resources()	)	)
								time.sleep(1	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





