import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets


def    a__       (						__UpperCamelCase ):
      return 1 / (1 + np.exp(-z ))


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return (-y * np.log(__UpperCamelCase ) - (1 - y) * np.log(1 - h )).mean()


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     np.dot(__UpperCamelCase   ,					__UpperCamelCase )
      return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase ) ) )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=7_0_0_0_0 ):
      SCREAMING_SNAKE_CASE_      =     np.zeros(x.shape[1] )

      for iterations in range(__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     np.dot(__UpperCamelCase   ,					__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     sigmoid_function(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     np.dot(x.T   ,					h - y ) / y.size
            SCREAMING_SNAKE_CASE_      =     theta - alpha * gradient  # updating the weights
            SCREAMING_SNAKE_CASE_      =     np.dot(__UpperCamelCase   ,					__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     sigmoid_function(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     cost_function(__UpperCamelCase   ,					__UpperCamelCase )
            if iterations % 1_0_0 == 0:
                  print(F'''loss: {j} \t''' )  # printing the loss after every 100 iterations
      return theta


# In[68]:

if __name__ == "__main__":
      A   :     Optional[int]       =				datasets.load_iris()
      A   :     Optional[Any]       =				iris.data[:, :2]
      A   :     Any       =				(iris.target != 0) * 1

      A   :     List[str]       =				0.1
      A   :     List[Any]       =				logistic_reg(alpha, x, y, max_iterations=7_00_00)
      print("theta: ", theta)  # printing the theta i.e our weights vector


      def    a__       (						__UpperCamelCase ):
            return sigmoid_function(
                np.dot(__UpperCamelCase   ,					__UpperCamelCase ) )  # predicting the value of probability from the logistic regression algorithm

      plt.figure(figsize=(10, 6))
      plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
      plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
      ((A)   ,				(A))   :     List[Any]       =				(x[:, 0].min(), x[:, 0].max())
      ((A)   ,				(A))   :     Optional[int]       =				(x[:, 1].min(), x[:, 1].max())
      ((A)   ,				(A))   :     Optional[Any]       =				np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
      A   :     str       =				np.c_[xxa.ravel(), xxa.ravel()]
      A   :     int       =				predict_prob(grid).reshape(xxa.shape)
      plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

      plt.legend()





      plt.show()







'''simple docstring'''





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

import pytest
from parameterized import parameterized, 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.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      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=__UpperCAmelCase  ,)
      assert hasattr(self  ,"""env"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      lowerCAmelCase__ :  Any							      =					{"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

      # creates estimator
      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=__UpperCAmelCase  ,instance_count=__UpperCAmelCase  ,instance_type=self.instance_type  ,debugger_hook_config=__UpperCAmelCase  ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=__UpperCAmelCase  ,py_version="""py36"""  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      TrainingJobAnalytics(__UpperCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






  @parameterized.expand([(2,)]      )
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # create estimator
      lowerCAmelCase__ :  List[Any]							      =					self.create_estimator(__UpperCAmelCase      )

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase__ :  Any							      =					TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      lowerCAmelCase__ :  int							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
      lowerCAmelCase__ :  List[Any]							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      lowerCAmelCase__ :  List[str]							      =					(
          Session().describe_training_job(estimator.latest_training_job.name      ).get("""TrainingTimeInSeconds"""  ,99_9999      )
      )

      # 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}  ,__UpperCAmelCase      )


