





'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   list   )						->list:
    _SCREAMING_SNAKE_CASE     =      sorted(__lowerCamelCase ,	key=lambda __lowerCamelCase   : value[2] ,	reverse=__lowerCamelCase   )

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

    # Finding the maximum profit and the count of jobs
    _SCREAMING_SNAKE_CASE     =      0
    _SCREAMING_SNAKE_CASE     =      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     =      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_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



