
'''simple docstring'''
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE				:							Union[str, 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:
							__SCREAMING_SNAKE_CASE				:							str		       =		['''BartTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE				:							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:
							__SCREAMING_SNAKE_CASE				:							Any		       =		[
							    '''TFBartForConditionalGeneration''',
							    '''TFBartForSequenceClassification''',
							    '''TFBartModel''',
							    '''TFBartPretrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE				:							List[Any]		       =		[
							    '''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

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









'''simple docstring'''
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 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		tf.constant([-1_00, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]  )
													A_ 						=		get_tf_activation("gelu"  )
													A_ 						=		get_tf_activation("gelu_10"  )

													A_ 						=		gelu(lowerCAmelCase  )
													A_ 						=		geluaa(lowerCAmelCase  )

													A_ 						=		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 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													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  )





