


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase__    	=     {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLMForMultipleChoice",
						    "XLMForQuestionAnswering",
						    "XLMForQuestionAnsweringSimple",
						    "XLMForSequenceClassification",
						    "XLMForTokenClassification",
						    "XLMModel",
						    "XLMPreTrainedModel",
						    "XLMWithLMHeadModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFXLMForMultipleChoice",
						    "TFXLMForQuestionAnsweringSimple",
						    "TFXLMForSequenceClassification",
						    "TFXLMForTokenClassification",
						    "TFXLMMainLayer",
						    "TFXLMModel",
						    "TFXLMPreTrainedModel",
						    "TFXLMWithLMHeadModel",
						]


if TYPE_CHECKING:
						from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
						from .tokenization_xlm import XLMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm import (
												    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMForMultipleChoice,
												    XLMForQuestionAnswering,
												    XLMForQuestionAnsweringSimple,
												    XLMForSequenceClassification,
												    XLMForTokenClassification,
												    XLMModel,
												    XLMPreTrainedModel,
												    XLMWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm import (
												    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMForMultipleChoice,
												    TFXLMForQuestionAnsweringSimple,
												    TFXLMForSequenceClassification,
												    TFXLMForTokenClassification,
												    TFXLMMainLayer,
												    TFXLMModel,
												    TFXLMPreTrainedModel,
												    TFXLMWithLMHeadModel,
												)

else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''


from __future__ import annotations

import numpy as np






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return np.maximum(0      , __lowerCAmelCase   )


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



