
"""simple docstring"""





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_a														=		50_0000

_a							,							_a														=		os.path.split(__file__)
_a														=		os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



@get_duration
def       lowerCamelCase__		(			__snake_case,     **__snake_case	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  dataset.map(**__snake_case	)



@get_duration
def       lowerCamelCase__		(			__snake_case,     **__snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  dataset.filter(**__snake_case	)



def       lowerCamelCase__		(			)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
					with tempfile.TemporaryDirectory() as tmp_dir:
										_UpperCamelCase							 =  datasets.Features({'''text''': datasets.Value('''string'''	), '''numbers''': datasets.Value('''float32'''	)}	)
										_UpperCamelCase							 =  generate_example_dataset(
										    os.path.join(__snake_case,     '''dataset.arrow'''	),     __snake_case,     num_examples=__snake_case	)

										_UpperCamelCase							 =  transformers.AutoTokenizer.from_pretrained('''bert-base-cased''',     use_fast=__snake_case	)

										def tokenize(__snake_case	):
															return tokenizer(examples['''text''']	)

										_UpperCamelCase							 =  map(__snake_case	)

										_UpperCamelCase							 =  map(__snake_case,     batched=__snake_case	)

										_UpperCamelCase							 =  map(__snake_case,     function=lambda __snake_case	: None,     batched=__snake_case	)

										with dataset.formatted_as(type='''numpy'''	):
															_UpperCamelCase							 =  map(__snake_case,     function=lambda __snake_case	: None,     batched=__snake_case	)

										with dataset.formatted_as(type='''pandas'''	):
															_UpperCamelCase							 =  map(__snake_case,     function=lambda __snake_case	: None,     batched=__snake_case	)

										with dataset.formatted_as(type='''torch''',     columns='''numbers'''	):
															_UpperCamelCase							 =  map(__snake_case,     function=lambda __snake_case	: None,     batched=__snake_case	)

										with dataset.formatted_as(type='''tensorflow''',     columns='''numbers'''	):
															_UpperCamelCase							 =  map(__snake_case,     function=lambda __snake_case	: None,     batched=__snake_case	)

										_UpperCamelCase							 =  map(__snake_case,     function=__snake_case,     batched=__snake_case	)

										_UpperCamelCase							 =  filter(__snake_case	)

										# Activate later when tokenizer support batched inputs
										# with dataset.formatted_as(type='numpy'):
										#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

					with open(__snake_case,     '''wb'''	) as f:
										f.write(json.dumps(__snake_case	).encode('''utf-8'''	)	)


if __name__ == "__main__":  # useful to run the profiler
				benchmark_map_filter()








"""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       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					return 1 / (1 + np.exp(-z	))



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					return (-y * np.log(__snake_case	) - (1 - y) * np.log(1 - h	)).mean()



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
					return np.sum(y * scores - np.log(1 + np.exp(__snake_case	)	)	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.zeros(x.shape[1]	)

					for iterations in range(__snake_case	):
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  np.dot(x.T,     h - y	) / y.size
										_UpperCamelCase							 =  theta - alpha * gradient  # updating the weights
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  cost_function(__snake_case,     __snake_case	)
										if iterations % 1_00 == 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_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""simple docstring"""





									return sigmoid_function(
									    np.dot(__snake_case,     __snake_case	)	)  # 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))														=		(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()






