'''simple docstring'''


import math

import tensorflow as tf
from packaging import version


def  _lowerCamelCase   (				lowerCamelCase_ :							int       ):



					"""simple docstring"""





					UpperCAmelCase_							:       str       =				tf.convert_to_tensor(lowerCamelCase_       )
					UpperCAmelCase_							:       int       =				0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0       )     ,					x.dtype       )       ))

					return x * cdf


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]       ):



					"""simple docstring"""





					UpperCAmelCase_							:       int       =				tf.convert_to_tensor(lowerCamelCase_       )
					UpperCAmelCase_							:       Union[str, Any]       =				tf.cast(math.pi     ,					x.dtype       )
					UpperCAmelCase_							:       Optional[Any]       =				tf.cast(0.04_4715     ,					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  _lowerCamelCase   (				lowerCamelCase_ :							List[str]       ):



					"""simple docstring"""





					UpperCAmelCase_							:       Optional[int]       =				tf.convert_to_tensor(lowerCamelCase_       )

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


def  _lowerCamelCase   (				lowerCamelCase_ :							List[str]       ):



					"""simple docstring"""





					UpperCAmelCase_							:       Dict       =				tf.convert_to_tensor(lowerCamelCase_       )
					UpperCAmelCase_							:       Optional[int]       =				tf.cast(0.04_4715     ,					x.dtype       )
					UpperCAmelCase_							:       Dict       =				tf.cast(0.79_7884_5608     ,					x.dtype       )

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


def  _lowerCamelCase   (				lowerCamelCase_ :							Dict       ):



					"""simple docstring"""





					UpperCAmelCase_							:       str       =				tf.convert_to_tensor(lowerCamelCase_       )
					UpperCAmelCase_							:       int       =				tf.cast(1.702     ,					x.dtype       )
					return x * tf.math.sigmoid(coeff * x       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]       ):



					"""simple docstring"""





					return tf.clip_by_value(_gelu(lowerCamelCase_       )     ,					-10     ,					10       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]     ,					lowerCamelCase_ :							Union[str, Any]=-1       ):



					"""simple docstring"""





					UpperCAmelCase_							:       Union[str, Any]       =				tf.split(lowerCamelCase_     ,					2     ,					axis=lowerCamelCase_       )
					return a * tf.math.sigmoid(lowerCamelCase_       )


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


	def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]       ):



						"""simple docstring"""





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

	snake_case__					:      Dict				      =  tf.keras.activations.gelu





	snake_case__					:      Optional[Any]				      =  approximate_gelu_wrap
else:
	snake_case__					:      List[str]				      =  _gelu
	snake_case__					:      List[str]				      =  _gelu_new


snake_case__					:      Optional[int]				      =  {
    '''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  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



					"""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 json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
 from sagemaker import Session, TrainingJobAnalytics
 from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''						,     '''False'''	)	) is not True						,     reason='''Skipping test because should only be run when releasing minor transformers version'''						,     )
@pytest.mark.usefixtures('''sm_env'''	)
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]	)
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       if self.framework == "pytorch":
            subprocess.run(
                F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()				,		encoding='utf-8'				,		check=snake_case_				,		)
       assert hasattr(self				,		'env'						)


  def _UpperCamelCase					(			self				,		snake_case_=1						):




       '''simple docstring'''

       return HuggingFace(
           entry_point=self.script				,		source_dir=self.env.test_path				,		role=self.env.role				,		image_uri=self.env.image_uri				,		base_job_name=F'''{self.env.base_job_name}-single'''				,		instance_count=snake_case_				,		instance_type=self.instance_type				,		debugger_hook_config=snake_case_				,		hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}				,		metric_definitions=self.env.metric_definitions				,		py_version='py36'				,		)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       TrainingJobAnalytics(snake_case_						).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.create_estimator()

       # run training
       estimator.fit()

       # result dataframe
       UpperCAmelCase_							:       int       =				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

       # extract kpis
       UpperCAmelCase_							:       Dict       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']						)
       UpperCAmelCase_							:       List[str]       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']						)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCAmelCase_							:       int       =				(
           Session().describe_training_job(estimator.latest_training_job.name						).get('TrainingTimeInSeconds'				,		9_9_9_9_9_9						)
       )

       # assert kpis
       assert train_runtime <= self.results["train_runtime"]
       assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy						)
       assert all(t <= self.results['eval_loss'] for t in eval_loss						)

       # dump tests result into json file to share in PR
       with open(F'''{estimator.latest_training_job.name}.json'''				,		'w'						) as outfile:
            json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}				,		snake_case_						)






