


'''simple docstring'''






def A_					(				__SCREAMING_SNAKE_CASE :					int	,   __SCREAMING_SNAKE_CASE :					list       )     ->  list:
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = 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
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = max(__SCREAMING_SNAKE_CASE	,   key=lambda __SCREAMING_SNAKE_CASE       : value[1]       )[1]
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = [-1] * max_deadline

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      =     {
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A      =     [
						    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DecisionTransformerGPT2Model""",
						    """DecisionTransformerGPT2PreTrainedModel""",
						    """DecisionTransformerModel""",
						    """DecisionTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_decision_transformer import (
						    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DecisionTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_decision_transformer import (
												    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DecisionTransformerGPTaModel,
												    DecisionTransformerGPTaPreTrainedModel,
												    DecisionTransformerModel,
												    DecisionTransformerPreTrainedModel,
												)


else:
						import sys

						_A      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


