

"""simple docstring"""




import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


__A	:       Dict        =						logging.get_logger(__name__)





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       "hybrid-clip"
		SCREAMING_SNAKE_CASE														=       True


		def __init__(self					:						str	,	__SCREAMING_SNAKE_CASE					:						str=5_1_2	,	**__SCREAMING_SNAKE_CASE					:						str):
							super().__init__(**__SCREAMING_SNAKE_CASE)

							if "text_config" not in kwargs:
												raise ValueError("`text_config` can not be `None`.")

							if "vision_config" not in kwargs:
												raise ValueError("`vision_config` can not be `None`.")

							A     =   kwargs.pop("text_config")
							A     =   kwargs.pop("vision_config")

							A     =   text_config.pop("model_type")
							A     =   vision_config.pop("model_type")

							from transformers import AutoConfig

							A     =   AutoConfig.for_model(__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

							if vision_model_type == "clip":
												A     =   AutoConfig.for_model(__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE).vision_config
							elif vision_model_type == "clip_vision_model":
												from transformers import CLIPVisionConfig

												A     =   CLIPVisionConfig(**__SCREAMING_SNAKE_CASE)
							else:
												A     =   AutoConfig.for_model(__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)

							A     =   projection_dim
							A     =   1.0


		@classmethod
		def    SCREAMING_SNAKE_CASE__  (cls					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						PretrainedConfig	,	__SCREAMING_SNAKE_CASE					:						PretrainedConfig	,	**__SCREAMING_SNAKE_CASE					:						Union[str, Any]):
							return cls(text_config=text_config.to_dict()	,	vision_config=vision_config.to_dict()	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[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






"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

     # Create a list of size equal to the maximum deadline
     # and initialize it with -1
     lowercase__								=				max(A     ,					key=lambda A   : value[1]   )[1]
     lowercase__								=				[-1] * max_deadline

     # Finding the maximum profit and the count of jobs
     lowercase__								=				0
     lowercase__								=				0
     for job in jobs:
          # Find a free time slot for this job
          # (Note that we start from the last possible slot)
          for i in range(job[1] - 1     ,					-1     ,					-1   ):
               if time_slots[i] == -1:
                    lowercase__								=				job[0]
                    count += 1
                    max_profit += job[2]
                    break
     return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




