import unittest

from knapsack import greedy_knapsack as kp



class 			SCREAMING_SNAKE_CASE_			(       unittest.TestCase      ):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(					self     :       Dict  )     ->					Dict:
         A    :  Optional[Any]								=[10, 20, 30, 40, 50, 60]
         A    :  Optional[int]								=[2, 4, 6, 8, 10, 12]
         A    :  Optional[int]								=1_00
         self.assertEqual(kp.calc_profit(SCREAMING_SNAKE_CASE__   ,						SCREAMING_SNAKE_CASE__   ,						SCREAMING_SNAKE_CASE__  )   ,						2_10  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Tuple  )     ->					List[Any]:
         self.assertRaisesRegex(SCREAMING_SNAKE_CASE__   ,						'max_weight must greater than zero.'  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Dict  )     ->					Any:
         self.assertRaisesRegex(SCREAMING_SNAKE_CASE__   ,						'Weight can not be negative.'  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       List[Any]  )     ->					List[str]:
         self.assertRaisesRegex(SCREAMING_SNAKE_CASE__   ,						'Profit can not be negative.'  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Union[str, Any]  )     ->					List[str]:
         self.assertRaisesRegex(SCREAMING_SNAKE_CASE__   ,						'max_weight must greater than zero.'  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       int  )     ->					List[str]:
         self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE__   ,						'The length of profit and weight must be same.'  )


if __name__ == "__main__":
    unittest.main()










from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			: str				   =					{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: int				   =					[
     "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTBigCodeForSequenceClassification",
     "GPTBigCodeForTokenClassification",
     "GPTBigCodeForCausalLM",
     "GPTBigCodeModel",
     "GPTBigCodePreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_bigcode import (
      GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTBigCodeForCausalLM,
      GPTBigCodeForSequenceClassification,
      GPTBigCodeForTokenClassification,
      GPTBigCodeModel,
      GPTBigCodePreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE			: List[Any]				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


