





'''simple docstring'''






import math

import tensorflow as tf
from packaging import version





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							List[str]			       =						tf.convert_to_tensor(lowerCamelCase_ )
		snake_case_		:							int			       =						0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 )				,      x.dtype ) ))

		return x * cdf





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							int			       =						tf.convert_to_tensor(lowerCamelCase_ )
		snake_case_		:							str			       =						tf.cast(math.pi				,      x.dtype )
		snake_case_		:							Dict			       =						tf.cast(0.044_715				,      x.dtype )
		snake_case_		:							Any			       =						0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase_				,      3 )) ))

		return x * cdf





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						tf.convert_to_tensor(lowerCamelCase_ )

		return x * tf.tanh(tf.math.softplus(lowerCamelCase_ ) )





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[int] ):







		'''simple docstring'''
		snake_case_		:							int			       =						tf.convert_to_tensor(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						tf.cast(0.044_715				,      x.dtype )
		snake_case_		:							List[str]			       =						tf.cast(0.7_978_845_608				,      x.dtype )

		return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[int] ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						tf.convert_to_tensor(lowerCamelCase_ )
		snake_case_		:							Any			       =						tf.cast(1.702				,      x.dtype )
		return x * tf.math.sigmoid(coeff * x )





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any] ):







		'''simple docstring'''
		return tf.clip_by_value(_gelu(lowerCamelCase_ )				,      -10				,      10 )





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :List[Any]=-1 ):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						tf.split(lowerCamelCase_				,      2				,      axis=lowerCamelCase_ )
		return a * tf.math.sigmoid(lowerCamelCase_ )


if version.parse(tf.version.VERSION) >= version.parse('2.4'):





							def      UpperCAmelCase      (		lowerCamelCase_  :Dict ):







									'''simple docstring'''
									return tf.keras.activations.gelu(lowerCamelCase_				,      approximate=lowerCamelCase_ )

							__A	:	int     =     tf.keras.activations.gelu






							__A	:	Any     =     approximate_gelu_wrap
else:
							__A	:	str     =     _gelu
							__A	:	Dict     =     _gelu_new


__A	:	Tuple     =     {
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}






def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		if activation_string in ACTaFN:
				return ACTaFN[activation_string]
		else:
				raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}''' )



'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)