



'''simple docstring'''






import os

import pytest
from attr import dataclass


UpperCamelCase__:     Union[str, Any]										=    "us-east-1"  # defaults region

@dataclass
class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= 42
	lowerCamelCase__    				= """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
	lowerCamelCase__    				= {
	    """task_name""": """mnli""",
	    """per_device_train_batch_size""": 16,
	    """per_device_eval_batch_size""": 16,
	    """do_train""": True,
	    """do_eval""": True,
	    """do_predict""": True,
	    """output_dir""": """/opt/ml/model""",
	    """overwrite_output_dir""": True,
	    """max_steps""": 500,
	    """save_steps""": 5_500,
	}
	lowerCamelCase__    				= {**hyperparameters, """max_steps""": 1_000}







	@property
	def   A			(		self    :  List[str]	)    ->	str:
								if self.framework == "pytorch":
															return [
															    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
															    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
															    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
															]
								else:
															return [
															    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
															    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
															    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
															]







	@property
	def   A			(		self    :  Optional[int]	)    ->	str:
								return F"""{self.framework}-transfromers-test"""







	@property
	def   A			(		self    :  Any	)    ->	str:
								return F"""./tests/sagemaker/scripts/{self.framework}"""







	@property
	def   A			(		self    :  Optional[int]	)    ->	str:
								if self.framework == "pytorch":
															return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
								else:
															return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"


@pytest.fixture(scope='''class'''						)
def 				snake_case_  (      _lowerCAmelCase						:		Tuple						)   ->     Tuple:
							UpperCAmelCase				: Optional[Any]											=	SageMakerTestEnvironment(framework=request.cls.framework						)










'''simple docstring'''






import math

import tensorflow as tf
from packaging import version


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Dict:
							UpperCAmelCase				: Tuple											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)     ,   x.dtype						)						))

							return x * cdf


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     List[Any]:
							UpperCAmelCase				: str											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Union[str, Any]											=	tf.cast(math.pi     ,   x.dtype						)
							UpperCAmelCase				: Tuple											=	tf.cast(0.0_4_4_7_1_5     ,   x.dtype						)
							UpperCAmelCase				: List[Any]											=	0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(_lowerCAmelCase     ,   3						))						))

							return x * cdf


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Any:
							UpperCAmelCase				: List[Any]											=	tf.convert_to_tensor(_lowerCAmelCase						)

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


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Tuple:
							UpperCAmelCase				: Union[str, Any]											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Union[str, Any]											=	tf.cast(0.0_4_4_7_1_5     ,   x.dtype						)
							UpperCAmelCase				: Tuple											=	tf.cast(0.7_9_7_8_8_4_5_6_0_8     ,   x.dtype						)

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


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Optional[int]:
							UpperCAmelCase				: str											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	tf.cast(1.7_0_2     ,   x.dtype						)
							return x * tf.math.sigmoid(coeff * x						)


def 				snake_case_  (      _lowerCAmelCase						:		Any						)   ->     Any:
							return tf.clip_by_value(_gelu(_lowerCAmelCase						)     ,   -10     ,   10						)


def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Optional[Any]=-1						)   ->     Union[str, Any]:
							UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	tf.split(_lowerCAmelCase     ,   2     ,   axis=_lowerCAmelCase						)
							return a * tf.math.sigmoid(_lowerCAmelCase						)


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


						def 				snake_case_  (      _lowerCAmelCase						:		Tuple						)   ->     Any:
													return tf.keras.activations.gelu(_lowerCAmelCase     ,   approximate=_lowerCAmelCase						)

						UpperCamelCase__:     List[Any]										=    tf.keras.activations.gelu




						UpperCamelCase__:     List[Any]										=    approximate_gelu_wrap
else:
						UpperCamelCase__:     List[str]										=    _gelu
						UpperCamelCase__:     Tuple										=    _gelu_new


UpperCamelCase__:     List[Any]										=    {
    "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 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     List[Any]:
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)





