






"""simple docstring"""


def 					a			(  UpperCamelCase_				:					Any  ,						UpperCamelCase_				:					Any		)	->		list:
 __A						:							List[str]      =						sorted(_UpperCAmelCase  ,						key=lambda UpperCamelCase_		: value[2]  ,						reverse=_UpperCAmelCase		)

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

 # Finding the maximum profit and the count of jobs
 __A						:							Optional[Any]      =						0
 __A						:							Optional[Any]      =						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:
    __A						:							Any      =						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_tokenizers_available, is_torch_available


_UpperCamelCase:					Optional[Any]     				=	{
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					Union[str, Any]     				=	['CodeGenTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:					str     				=	[
         'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CodeGenForCausalLM',
         'CodeGenModel',
         'CodeGenPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
     from .tokenization_codegen import CodeGenTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_codegen_fast import CodeGenTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_codegen import (
              CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
              CodeGenForCausalLM,
              CodeGenModel,
              CodeGenPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase:					int     				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
