



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase       :						Any						={
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						List[str]						=["BartTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						Union[str, Any]						=[
				    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BartForCausalLM",
				    "BartForConditionalGeneration",
				    "BartForQuestionAnswering",
				    "BartForSequenceClassification",
				    "BartModel",
				    "BartPretrainedModel",
				    "PretrainedBartModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						Tuple						=[
				    "TFBartForConditionalGeneration",
				    "TFBartForSequenceClassification",
				    "TFBartModel",
				    "TFBartPretrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						str						=[
				    "FlaxBartDecoderPreTrainedModel",
				    "FlaxBartForCausalLM",
				    "FlaxBartForConditionalGeneration",
				    "FlaxBartForQuestionAnswering",
				    "FlaxBartForSequenceClassification",
				    "FlaxBartModel",
				    "FlaxBartPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

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









'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:list			)		->						list:
				snake_case__      :   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
				snake_case__      :   Any      =    max(UpperCamelCase__   ,				key=lambda UpperCamelCase__			: value[1]			)[1]
				snake_case__      :   str      =    [-1] * max_deadline

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


if __name__ == "__main__":
				import doctest

				doctest.testmod()




