


"""simple docstring"""






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)
_A   					=    logging.getLogger()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				"\n".join(__snake_case       )
				Path(__snake_case       ).open("w"       ).writelines(__snake_case       )


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

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

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				Dict		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
									SCREAMING_SNAKE_CASE__		  =				input_file_name.parent / "utest_output.txt"
									assert not output_file_name.exists()
									SCREAMING_SNAKE_CASE__		  =				[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
									_dump_articles(__a   , __a		)

									SCREAMING_SNAKE_CASE__		  =				str(Path(self.get_auto_remove_tmp_dir()		) / "scores.json"		)
									SCREAMING_SNAKE_CASE__		  =				"translation_en_to_de" if model == T5_TINY else "summarization"
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									self.run_eval_tester(__a		)



					@parameterized.expand([BART_TINY, MBART_TINY]		)
					@slow
					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Tuple		)							->       Optional[int]:
									self.run_eval_tester(__a		)



					@parameterized.expand([T5_TINY, MBART_TINY]		)
					@slow
					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				List[Any]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
									SCREAMING_SNAKE_CASE__		  =				input_file_name.parent / "utest_output.txt"
									assert not output_file_name.exists()

									SCREAMING_SNAKE_CASE__		  =				{
									    "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!",
									    ],
									}

									SCREAMING_SNAKE_CASE__		  =				Path(self.get_auto_remove_tmp_dir()		)
									SCREAMING_SNAKE_CASE__		  =				str(tmp_dir / "scores.json"		)
									SCREAMING_SNAKE_CASE__		  =				str(tmp_dir / "val.target"		)
									_dump_articles(__a   , text["en"]		)
									_dump_articles(__a   , text["de"]		)
									SCREAMING_SNAKE_CASE__		  =				"translation_en_to_de" if model == T5_TINY else "summarization"
									SCREAMING_SNAKE_CASE__		  =				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()
													SCREAMING_SNAKE_CASE__		  =				[" num_beams | length_penalty", model, "Best score args"]
													SCREAMING_SNAKE_CASE__		  =				["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		)		)





"""simple docstring"""






# 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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				return 1 / (1 + np.exp(-z       ))




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				return (-y * np.log(__UpperCAmelCase       ) - (1 - y) * np.log(1 - h       )).mean()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
				return np.sum(y * scores - np.log(1 + np.exp(__UpperCAmelCase       )       )       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=70_000       )							->   Optional[Any]:
				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 % 100 == 0:
												print(F"""loss: {j} \t"""       )  # printing the loss after every 100 iterations
				return theta


# In[68]:

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

						_A   					=    0.1
						_A   					=    logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
						print('theta: ', theta)  # printing the theta i.e our weights vector




						def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
										return sigmoid_function(
										    np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )       )  # predicting the value of probability from the logistic regression algorithm

						plt.figure(figsize=(1_0, 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))   					=    (x[:, 0].min(), x[:, 0].max())
						((_A)					,			(_A))   					=    (x[:, 1].min(), x[:, 1].max())
						((_A)					,			(_A))   					=    np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
						_A   					=    np.c_[xxa.ravel(), xxa.ravel()]
						_A   					=    predict_prob(grid).reshape(xxa.shape)
						plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

						plt.legend()

						plt.show()




