





"""simple docstring"""





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase__        =      logging.get_logger(__name__)

lowerCAmelCase__        =      {
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}





class 							_lowerCamelCase   (							_lowercase			):
						UpperCAmelCase_			=      "vit-hybrid"


						def __init__(self ,				__a=None ,				__a=7_68 ,				__a=12 ,				__a=12 ,				__a=30_72 ,				__a="gelu" ,				__a=0.0 ,				__a=0.0 ,				__a=0.02 ,				__a=1e-1_2 ,				__a=2_24 ,				__a=1 ,				__a=3 ,				__a=[1, 10_24, 24, 24] ,				__a=True ,				**__a ,				)			-> Optional[Any]:
								super().__init__(**__a		)

								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."		)
										UpperCamelCase =						{
										    "global_padding": "same",
										    "layer_type": "bottleneck",
										    "depths": [3, 4, 9],
										    "out_features": ["stage3"],
										    "embedding_dynamic_padding": True,
										}

								if isinstance(__a ,				__a		):
										if "model_type" in backbone_config:
												UpperCamelCase =						CONFIG_MAPPING[backbone_config["model_type"]]
										else:
												logger.info(
												    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."		)
												UpperCamelCase =						BitConfig
										UpperCamelCase =						backbone_config_class(**__a		)

								UpperCamelCase =						backbone_featmap_shape
								UpperCamelCase =						backbone_config
								UpperCamelCase =						hidden_size
								UpperCamelCase =						num_hidden_layers
								UpperCamelCase =						num_attention_heads
								UpperCamelCase =						intermediate_size
								UpperCamelCase =						hidden_act
								UpperCamelCase =						hidden_dropout_prob
								UpperCamelCase =						attention_probs_dropout_prob
								UpperCamelCase =						initializer_range
								UpperCamelCase =						layer_norm_eps
								UpperCamelCase =						image_size
								UpperCamelCase =						patch_size
								UpperCamelCase =						num_channels
								UpperCamelCase =						qkv_bias




						def      snake_case_ (self		)			-> Dict[str, any]:
								UpperCamelCase =						copy.deepcopy(self.__dict__		)
								UpperCamelCase =						self.backbone_config.to_dict()
								UpperCamelCase =						self.__class__.model_type
								return output













"""simple docstring"""





def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						sorted(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[2]	,						reverse=_SCREAMING_SNAKE_CASE     )

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

		# Finding the maximum profit and the count of jobs
		UpperCamelCase =						0
		UpperCamelCase =						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:
								UpperCamelCase =						job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






