
"""simple docstring"""





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402






class     _UpperCAmelCase(    unittest.TestCase	):



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  0



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
							self.assertIsInstance(__a				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdirname:
												_UpperCamelCase							 =  Path(__a) / '''preprocessor_config.json'''
												_UpperCamelCase							 =  Path(__a) / '''config.json'''
												json.dump(
												    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}				,		open(__a				,		'''w''')				,		)
												json.dump({'''model_type''': '''clip'''}				,		open(__a				,		'''w'''))

												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a)
												self.assertIsInstance(__a				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
												_UpperCamelCase							 =  Path(__a) / '''preprocessor_config.json'''
												_UpperCamelCase							 =  Path(__a) / '''config.json'''
												json.dump(
												    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}				,		open(__a				,		'''w''')				,		)
												json.dump({'''model_type''': '''clip'''}				,		open(__a				,		'''w'''))

												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a)
												self.assertIsInstance(__a				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdirname:
												_UpperCamelCase							 =  CLIPConfig()

												# Create a dummy config file with image_proceesor_type
												_UpperCamelCase							 =  Path(__a) / '''preprocessor_config.json'''
												_UpperCamelCase							 =  Path(__a) / '''config.json'''
												json.dump(
												    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}				,		open(__a				,		'''w''')				,		)
												json.dump({'''model_type''': '''clip'''}				,		open(__a				,		'''w'''))

												# remove image_processor_type to make sure config.json alone is enough to load image processor locally
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a).to_dict()

												config_dict.pop('''image_processor_type''')
												_UpperCamelCase							 =  CLIPImageProcessor(**__a)

												# save in new folder
												model_config.save_pretrained(__a)
												config.save_pretrained(__a)

												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a)

												# make sure private variable is not incorrectly saved
												_UpperCamelCase							 =  json.loads(config.to_json_string())
												self.assertTrue('''_processor_class''' not in dict_as_saved)

							self.assertIsInstance(__a				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdirname:
												_UpperCamelCase							 =  Path(__a) / '''preprocessor_config.json'''
												json.dump(
												    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}				,		open(__a				,		'''w''')				,		)

												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a)
												self.assertIsInstance(__a				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							with self.assertRaisesRegex(
							    __a				,		'''clip-base is not a local folder and is not a valid model identifier'''):
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained('''clip-base''')



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							with self.assertRaisesRegex(
							    __a				,		R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a				,		revision='''aaaaaa''')



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							with self.assertRaisesRegex(
							    __a				,		'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''				,		):
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(__a):
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(__a):
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(
												    '''hf-internal-testing/test_dynamic_image_processor'''				,		trust_remote_code=__a)

							_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(
							    '''hf-internal-testing/test_dynamic_image_processor'''				,		trust_remote_code=__a)
							self.assertEqual(image_processor.__class__.__name__				,		'''NewImageProcessor''')

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
												image_processor.save_pretrained(__a)
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a				,		trust_remote_code=__a)
							self.assertEqual(reloaded_image_processor.__class__.__name__				,		'''NewImageProcessor''')



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							try:
												AutoConfig.register('''custom'''				,		__a)
												AutoImageProcessor.register(__a				,		__a)
												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__a):
																	AutoImageProcessor.register(__a				,		__a)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	_UpperCamelCase							 =  Path(__a) / '''preprocessor_config.json'''
																	_UpperCamelCase							 =  Path(__a) / '''config.json'''
																	json.dump(
																	    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}				,		open(__a				,		'''w''')				,		)
																	json.dump({'''model_type''': '''clip'''}				,		open(__a				,		'''w'''))

																	_UpperCamelCase							 =  CustomImageProcessor.from_pretrained(__a)

												# Now that the config is registered, it can be used as any other config with the auto-API
												with tempfile.TemporaryDirectory() as tmp_dir:
																	image_processor.save_pretrained(__a)
																	_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(__a)
																	self.assertIsInstance(__a				,		__a)

							finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																	del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																	del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




		def 						UpperCAmelCase							(					self)       ->       Union[str, Any]:






							'''simple docstring'''






							class     _UpperCAmelCase(    lowerCamelCase	):
									lowercase__						=      True

							try:
												AutoConfig.register('''custom'''				,		__a)
												AutoImageProcessor.register(__a				,		__a)
												# If remote code is not set, the default is to use local
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
												self.assertEqual(image_processor.__class__.__name__				,		'''NewImageProcessor''')
												self.assertTrue(image_processor.is_local)

												# If remote code is disabled, we load the local one.
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(
												    '''hf-internal-testing/test_dynamic_image_processor'''				,		trust_remote_code=__a)
												self.assertEqual(image_processor.__class__.__name__				,		'''NewImageProcessor''')
												self.assertTrue(image_processor.is_local)

												# If remote is enabled, we load from the Hub
												_UpperCamelCase							 =  AutoImageProcessor.from_pretrained(
												    '''hf-internal-testing/test_dynamic_image_processor'''				,		trust_remote_code=__a)
												self.assertEqual(image_processor.__class__.__name__				,		'''NewImageProcessor''')
												self.assertTrue(not hasattr(__a				,		'''is_local'''))

							finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																	del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																	del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]








"""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()






