






"""simple docstring"""
from typing import TYPE_CHECKING

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


A_			: Tuple				=			{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Any				=			["DeformableDetrFeatureExtractor"]
      A_			: Tuple				=			["DeformableDetrImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Optional[int]				=			[
          "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DeformableDetrForObjectDetection",
          "DeformableDetrModel",
          "DeformableDetrPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
            from .image_processing_deformable_detr import DeformableDetrImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deformable_detr import (
                DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeformableDetrForObjectDetection,
                DeformableDetrModel,
                DeformableDetrPreTrainedModel,
            )

else:
      import sys

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







import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation





@require_tf
class 		A__				(   unittest.TestCase ):






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]						)
						__magic_name__					:							List[str]		   =							get_tf_activation('''gelu'''						)
						__magic_name__					:							str		   =							get_tf_activation('''gelu_10'''						)

						__magic_name__					:							List[str]		   =							gelu(lowerCamelCase						)
						__magic_name__					:							Optional[Any]		   =							geluaa(lowerCamelCase						)

						__magic_name__					:							Optional[int]		   =							tf.where(y_gelu_aa < 1_0.0				,     1.0				,     0.0						)

						self.assertEqual(tf.math.reduce_max(lowerCamelCase						).numpy().item()				,     1_0.0						)
						self.assertTrue(np.allclose(y_gelu * clipped_mask				,     y_gelu_aa * clipped_mask						)						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						get_tf_activation('''gelu'''						)
						get_tf_activation('''gelu_10'''						)
						get_tf_activation('''gelu_fast'''						)
						get_tf_activation('''gelu_new'''						)
						get_tf_activation('''glu'''						)
						get_tf_activation('''mish'''						)
						get_tf_activation('''quick_gelu'''						)
						get_tf_activation('''relu'''						)
						get_tf_activation('''sigmoid'''						)
						get_tf_activation('''silu'''						)
						get_tf_activation('''swish'''						)
						get_tf_activation('''tanh'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation('''bogus'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation(lowerCamelCase						)



