






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE 							=						logging.getLogger()

def      _lowerCamelCase	(						__A    :							Path		,		__A    :							list )      ->					Any:
    _UpperCAmelCase				:      Union[str, Any]			  =				'''\n'''.join(__A )
    Path(__A ).open('''w''' ).writelines(__A )


SCREAMING_SNAKE_CASE 							=						'patrickvonplaten/t5-tiny-random'
SCREAMING_SNAKE_CASE 							=						'sshleifer/bart-tiny-random'
SCREAMING_SNAKE_CASE 							=						'sshleifer/tiny-mbart'

SCREAMING_SNAKE_CASE 							=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class 							A_    (						__lowercase			):






 '''simple docstring'''




 def  snake_case__				(	self							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
     _UpperCAmelCase				:      Dict			  =				input_file_name.parent / '''utest_output.txt'''
     assert not output_file_name.exists()
     _UpperCAmelCase				:      Union[str, Any]			  =				[''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
     _dump_articles(_A							,  _A)

     _UpperCAmelCase				:      Dict			  =				str(Path(self.get_auto_remove_tmp_dir()) / '''scores.json''')
     _UpperCAmelCase				:      Dict			  =				'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
     _UpperCAmelCase				:      Dict			  =				f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

     with patch.object(_A							,  '''argv'''							,  _A):
         run_generate()
         assert Path(_A).exists()
         # os.remove(Path(output_file_name))


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     self.run_eval_tester(_A)


 @parameterized.expand([BART_TINY, MBART_TINY])
 @slow
 def  snake_case__				(	self							,  _A) ->		int:

     """simple docstring"""



     self.run_eval_tester(_A)


 @parameterized.expand([T5_TINY, MBART_TINY])
 @slow
 def  snake_case__				(	self							,  _A) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
     _UpperCAmelCase				:      Tuple			  =				input_file_name.parent / '''utest_output.txt'''
     assert not output_file_name.exists()

     _UpperCAmelCase				:      int			  =				{
         '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
         '''de''': [
             '''Maschinelles Lernen ist großartig, oder?''',
             '''Ich esse gerne Bananen''',
             '''Morgen ist wieder ein toller Tag!''',
         ],
     }

     _UpperCAmelCase				:      Optional[int]			  =				Path(self.get_auto_remove_tmp_dir())
     _UpperCAmelCase				:      List[Any]			  =				str(tmp_dir / '''scores.json''')
     _UpperCAmelCase				:      int			  =				str(tmp_dir / '''val.target''')
     _dump_articles(_A							,  text['''en'''])
     _dump_articles(_A							,  text['''de'''])
     _UpperCAmelCase				:      List[str]			  =				'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
     _UpperCAmelCase				:      List[Any]			  =				f'''
            run_eval_search.py
            {model}
            {str(_A)}
            {str(_A)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
     testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''])

     with patch.object(_A							,  '''argv'''							,  _A):
         with CaptureStdout() as cs:
             run_search()
         _UpperCAmelCase				:      Optional[int]			  =				[''' num_beams | length_penalty''', model, '''Best score args''']
         _UpperCAmelCase				:      Tuple			  =				['''Info''']
         if "translation" in task:
             expected_strings.append('''bleu''')
         else:
             expected_strings.extend(_A)
         for w in expected_strings:
             assert w in cs.out
         for w in un_expected_strings:
             assert w not in cs.out
         assert Path(_A).exists()
         os.remove(Path(_A))












# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

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

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					str:
    return 1 / (1 + np.exp(-z ))

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any] )      ->					Optional[int]:
    return (-y * np.log(__A ) - (1 - y) * np.log(1 - h )).mean()

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      int			  =				np.dot(__A		,		__A )
    return np.sum(y * scores - np.log(1 + np.exp(__A ) ) )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							str		,		__A    :							Dict=70_000 )      ->					Optional[Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				np.zeros(x.shape[1] )

    for iterations in range(__A ):
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Optional[Any]			  =				np.dot(x.T		,		h - y ) / y.size
        _UpperCAmelCase				:      Optional[Any]			  =				theta - alpha * gradient  # updating the weights
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Any			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				cost_function(__A		,		__A )
        if iterations % 100 == 0:
            print(f'''loss: {j} \t''' )  # printing the loss after every 100 iterations
    return theta


# In[68]:

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

  SCREAMING_SNAKE_CASE 							=						0.1
  SCREAMING_SNAKE_CASE 							=						logistic_reg(alpha, x, y, max_iterations=70000)
  print('theta: ', theta)  # printing the theta i.e our weights vector

  def      _lowerCamelCase	(						__A    :							Any )      ->					int:
      return sigmoid_function(
          np.dot(__A		,		__A ) )  # 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')
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 0].min(), x[:, 0].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 1].min(), x[:, 1].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  SCREAMING_SNAKE_CASE 							=						np.c_[xxa.ravel(), xxa.ravel()]
  SCREAMING_SNAKE_CASE 							=						predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()


  plt.show()




