

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_        =      {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      ['PLBartTokenizer']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'PLBartForCausalLM',
					    'PLBartForConditionalGeneration',
					    'PLBartForSequenceClassification',
					    'PLBartModel',
					    'PLBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_plbart import PLBartTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_plbart import (
										    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PLBartForCausalLM,
										    PLBartForConditionalGeneration,
										    PLBartForSequenceClassification,
										    PLBartModel,
										    PLBartPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_        =      _LazyModule(__name__, globals()['__file__'], _import_structure)







from __future__ import annotations

import numpy as np

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[float] )   ->       Dict:
				return np.maximum(0  ,      lowerCAmelCase )


if __name__ == "__main__":
					print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


