


def   lowerCamelCase_					( _a			,     _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Dict					  =      sorted(_a			,     key=lambda _a : value[2]			,     reverse=_a )

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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase		=	{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	['''SegformerFeatureExtractor''']
		lowerCamelCase		=	['''SegformerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SegformerDecodeHead''',
		    '''SegformerForImageClassification''',
		    '''SegformerForSemanticSegmentation''',
		    '''SegformerLayer''',
		    '''SegformerModel''',
		    '''SegformerPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFSegformerDecodeHead''',
		    '''TFSegformerForImageClassification''',
		    '''TFSegformerForSemanticSegmentation''',
		    '''TFSegformerModel''',
		    '''TFSegformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_segformer import SegformerFeatureExtractor
				from .image_processing_segformer import SegformerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_segformer import (
				    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SegformerDecodeHead,
				    SegformerForImageClassification,
				    SegformerForSemanticSegmentation,
				    SegformerLayer,
				    SegformerModel,
				    SegformerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_segformer import (
				    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSegformerDecodeHead,
				    TFSegformerForImageClassification,
				    TFSegformerForSemanticSegmentation,
				    TFSegformerModel,
				    TFSegformerPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase		=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




