


"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import OwlViTImageProcessor, OwlViTProcessor






@require_vision
class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	tempfile.mkdtemp()

					# fmt: off
					_UpperCamelCase					:Any					     =	['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
					# fmt: on
					_UpperCamelCase					:str					     =	dict(zip(SCREAMING_SNAKE_CASE__					,				range(len(SCREAMING_SNAKE_CASE__   )   )   )   )
					_UpperCamelCase					:Optional[int]					     =	['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
					_UpperCamelCase					:Tuple					     =	{'''unk_token''': '''<unk>'''}

					_UpperCamelCase					:Any					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']   )
					_UpperCamelCase					:int					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''merges_file''']   )
					with open(self.vocab_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(SCREAMING_SNAKE_CASE__   ) + '''\n'''   )
					with open(self.merges_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								fp.write('''\n'''.join(SCREAMING_SNAKE_CASE__   )   )

					_UpperCamelCase					:List[Any]					     =	{
					    '''do_resize''': True,
					    '''size''': 20,
					    '''do_center_crop''': True,
					    '''crop_size''': 18,
					    '''do_normalize''': True,
					    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					_UpperCamelCase					:List[str]					     =	os.path.join(self.tmpdirname					,				SCREAMING_SNAKE_CASE__   )
					with open(self.image_processor_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								json.dump(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					return CLIPTokenizer.from_pretrained(self.tmpdirname					,				pad_token='''!'''					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,				pad_token='''!'''					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					return OwlViTImageProcessor.from_pretrained(self.tmpdirname					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					shutil.rmtree(self.tmpdirname   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	[np.random.randint(2_55					,				size=(3, 30, 4_00)					,				dtype=np.uinta   )]

					_UpperCamelCase					:int					     =	[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__					,				0					,				-1   )   ) for x in image_inputs]

					return image_inputs
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.get_tokenizer()
					_UpperCamelCase					:List[str]					     =	self.get_rust_tokenizer()
					_UpperCamelCase					:Dict					     =	self.get_image_processor()

					_UpperCamelCase					:List[Any]					     =	OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )
					processor_slow.save_pretrained(self.tmpdirname   )
					_UpperCamelCase					:Union[str, Any]					     =	OwlViTProcessor.from_pretrained(self.tmpdirname					,				use_fast=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )
					processor_fast.save_pretrained(self.tmpdirname   )
					_UpperCamelCase					:List[Any]					     =	OwlViTProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor_slow.tokenizer.get_vocab()					,				tokenizer_slow.get_vocab()   )
					self.assertEqual(processor_fast.tokenizer.get_vocab()					,				tokenizer_fast.get_vocab()   )
					self.assertEqual(tokenizer_slow.get_vocab()					,				tokenizer_fast.get_vocab()   )
					self.assertIsInstance(processor_slow.tokenizer					,				SCREAMING_SNAKE_CASE__   )
					self.assertIsInstance(processor_fast.tokenizer					,				SCREAMING_SNAKE_CASE__   )

					self.assertEqual(processor_slow.image_processor.to_json_string()					,				image_processor.to_json_string()   )
					self.assertEqual(processor_fast.image_processor.to_json_string()					,				image_processor.to_json_string()   )
					self.assertIsInstance(processor_slow.image_processor					,				SCREAMING_SNAKE_CASE__   )
					self.assertIsInstance(processor_fast.image_processor					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	OwlViTProcessor(tokenizer=self.get_tokenizer()					,				image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

					_UpperCamelCase					:Any					     =	self.get_tokenizer(bos_token='''(BOS)'''					,				eos_token='''(EOS)'''   )
					_UpperCamelCase					:Optional[int]					     =	self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:int					     =	OwlViTProcessor.from_pretrained(
					    self.tmpdirname					,				bos_token='''(BOS)'''					,				eos_token='''(EOS)'''					,				do_normalize=SCREAMING_SNAKE_CASE__   )

					self.assertEqual(processor.tokenizer.get_vocab()					,				tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer					,				SCREAMING_SNAKE_CASE__   )

					self.assertEqual(processor.image_processor.to_json_string()					,				image_processor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.image_processor					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.get_image_processor()
					_UpperCamelCase					:Union[str, Any]					     =	self.get_tokenizer()

					_UpperCamelCase					:List[str]					     =	OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	self.prepare_image_inputs()

					_UpperCamelCase					:Union[str, Any]					     =	image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors='''np'''   )
					_UpperCamelCase					:Tuple					     =	processor(images=SCREAMING_SNAKE_CASE__					,				return_tensors='''np'''   )

					for key in input_image_proc.keys():
								self.assertAlmostEqual(input_image_proc[key].sum()					,				input_processor[key].sum()					,				delta=1E-2   )
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.get_image_processor()
					_UpperCamelCase					:Dict					     =	self.get_tokenizer()

					_UpperCamelCase					:Optional[Any]					     =	OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	'''lower newer'''

					_UpperCamelCase					:Union[str, Any]					     =	processor(text=SCREAMING_SNAKE_CASE__					,				return_tensors='''np'''   )

					_UpperCamelCase					:Optional[int]					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors='''np'''   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key][0].tolist()					,				encoded_processor[key][0].tolist()   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.get_image_processor()
					_UpperCamelCase					:Tuple					     =	self.get_tokenizer()

					_UpperCamelCase					:Dict					     =	OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	'''lower newer'''
					_UpperCamelCase					:str					     =	self.prepare_image_inputs()

					_UpperCamelCase					:str					     =	processor(text=SCREAMING_SNAKE_CASE__					,				images=SCREAMING_SNAKE_CASE__   )

					self.assertListEqual(list(inputs.keys()   )					,				['''input_ids''', '''attention_mask''', '''pixel_values''']   )

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE__   ):
								processor()
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	'''google/owlvit-base-patch32'''
					_UpperCamelCase					:str					     =	OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	['''cat''', '''nasa badge''']
					_UpperCamelCase					:Union[str, Any]					     =	processor(text=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	16
					self.assertListEqual(list(inputs.keys()   )					,				['''input_ids''', '''attention_mask''']   )
					self.assertEqual(inputs['''input_ids'''].shape					,				(2, seq_length)   )

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE__   ):
								processor()
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/owlvit-base-patch32'''
					_UpperCamelCase					:Union[str, Any]					     =	OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	[['''cat''', '''nasa badge'''], ['''person''']]
					_UpperCamelCase					:Union[str, Any]					     =	processor(text=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[Any]					     =	16
					_UpperCamelCase					:Dict					     =	len(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	max([len(SCREAMING_SNAKE_CASE__   ) for texts in input_texts]   )

					self.assertListEqual(list(inputs.keys()   )					,				['''input_ids''', '''attention_mask''']   )
					self.assertEqual(inputs['''input_ids'''].shape					,				(batch_size * num_max_text_queries, seq_length)   )

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE__   ):
								processor()
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	'''google/owlvit-base-patch32'''
					_UpperCamelCase					:Union[str, Any]					     =	OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	['''cat''', '''nasa badge''']
					_UpperCamelCase					:Optional[int]					     =	processor(text=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	16
					_UpperCamelCase					:Optional[Any]					     =	inputs['''input_ids''']
					_UpperCamelCase					:Optional[Any]					     =	[
					    [4_94_06, 23_68, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
					    [4_94_06, 68_41, 1_13_01, 4_94_07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
					]

					self.assertListEqual(list(inputs.keys()   )					,				['''input_ids''', '''attention_mask''']   )
					self.assertEqual(inputs['''input_ids'''].shape					,				(2, seq_length)   )
					self.assertListEqual(list(input_ids[0]   )					,				predicted_ids[0]   )
					self.assertListEqual(list(input_ids[1]   )					,				predicted_ids[1]   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.get_image_processor()
					_UpperCamelCase					:List[str]					     =	self.get_tokenizer()

					_UpperCamelCase					:Optional[Any]					     =	OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[Any]					     =	self.prepare_image_inputs()
					_UpperCamelCase					:Optional[int]					     =	self.prepare_image_inputs()

					_UpperCamelCase					:Optional[Any]					     =	processor(images=SCREAMING_SNAKE_CASE__					,				query_images=SCREAMING_SNAKE_CASE__   )

					self.assertListEqual(list(inputs.keys()   )					,				['''query_pixel_values''', '''pixel_values''']   )

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE__   ):
								processor()





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.get_image_processor()
					_UpperCamelCase					:Optional[Any]					     =	self.get_tokenizer()

					_UpperCamelCase					:str					     =	OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					_UpperCamelCase					:str					     =	processor.batch_decode(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE__   )

					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )





"""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 				A_       ( snake_case__		)					->  str:
			return 1 / (1 + np.exp(-z		))





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			return (-y * np.log(snake_case__		) - (1 - y) * np.log(1 - h		)).mean()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Tuple					     =	np.dot(snake_case__       ,					snake_case__		)
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__		)		)		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=7_00_00		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros(x.shape[1]		)

			for iterations in range(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Tuple					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	np.dot(x.T       ,					h - y		) / y.size
						_UpperCamelCase					:int					     =	theta - alpha * gradient  # updating the weights
						_UpperCamelCase					:Union[str, Any]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Dict					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	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__":
					UpperCamelCase__				:Union[str, Any]			   =	datasets.load_iris()
					UpperCamelCase__				:Dict			   =	iris.data[:, :2]
					UpperCamelCase__				:Any			   =	(iris.target != 0) * 1

					UpperCamelCase__				:Tuple			   =	0.1
					UpperCamelCase__				:List[str]			   =	logistic_reg(alpha, x, y, max_iterations=70_000)
					print("""theta: """, theta)  # printing the theta i.e our weights vector





					def 				A_       ( snake_case__		)					->  Optional[Any]:
								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""")
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Union[str, Any]			   =	(x[:, 0].min(), x[:, 0].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	(x[:, 1].min(), x[:, 1].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					UpperCamelCase__				:List[Any]			   =	np.c_[xxa.ravel(), xxa.ravel()]
					UpperCamelCase__				:List[Any]			   =	predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()




					plt.show()

