

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
					import numpy as np
					import torch

if is_vision_available():
					from PIL import Image


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False ):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						snake_case_     =			[]
						for i in range(image_processor_tester.batch_size ):
												if equal_resolution:
																		snake_case_     =			snake_case_     =			image_processor_tester.max_resolution
												else:
																		# To avoid getting image width/height 0
																		snake_case_     =			image_processor_tester.min_resolution
																		if getattr(SCREAMING_SNAKE_CASE__						,	'''size_divisor'''						,	SCREAMING_SNAKE_CASE__ ):
																								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																								snake_case_     =			max(image_processor_tester.size_divisor						,	SCREAMING_SNAKE_CASE__ )
																		snake_case_,					snake_case_     =			np.random.choice(np.arange(SCREAMING_SNAKE_CASE__						,	image_processor_tester.max_resolution )						,	2 )
												image_inputs.append(
												    np.random.randint(255						,	size=(image_processor_tester.num_channels, width, height)						,	dtype=np.uinta ) )

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												snake_case_     =			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1 ) ) for image in image_inputs]

						if torchify:
												snake_case_     =			[torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for image in image_inputs]

						return image_inputs


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False ):
						snake_case_     =			[]
						for i in range(image_processor_tester.num_frames ):
												video.append(np.random.randint(255						,	size=(image_processor_tester.num_channels, width, height)						,	dtype=np.uinta ) )

						if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												snake_case_     =			[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1 ) ) for frame in video]

						if torchify:
												snake_case_     =			[torch.from_numpy(SCREAMING_SNAKE_CASE__ ) for frame in video]

						return video


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False ):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						snake_case_     =			[]
						for i in range(image_processor_tester.batch_size ):
												if equal_resolution:
																		snake_case_     =			snake_case_     =			image_processor_tester.max_resolution
												else:
																		snake_case_,					snake_case_     =			np.random.choice(
																		    np.arange(image_processor_tester.min_resolution						,	image_processor_tester.max_resolution )						,	2 )
																		snake_case_     =			prepare_video(
																		    image_processor_tester=SCREAMING_SNAKE_CASE__						,	width=SCREAMING_SNAKE_CASE__						,	height=SCREAMING_SNAKE_CASE__						,	numpify=SCREAMING_SNAKE_CASE__						,	torchify=SCREAMING_SNAKE_CASE__						,	)
												video_inputs.append(SCREAMING_SNAKE_CASE__ )

						return video_inputs





class 			snake_case_     :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Union[str, Any]		    =       None







			def        snake_case__(  self				:       List[str]							)   ->str:
									snake_case_     =			self.image_processing_class(**self.image_processor_dict							)
									snake_case_     =			json.loads(image_processor.to_json_string()							)
									for key, value in self.image_processor_dict.items():
															self.assertEqual(obj[key]							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Optional[Any]:
									snake_case_     =			self.image_processing_class(**self.image_processor_dict							)

									with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case_     =			os.path.join(_UpperCamelCase							,     '''image_processor.json'''							)
															image_processor_first.to_json_file(_UpperCamelCase							)
															snake_case_     =			self.image_processing_class.from_json_file(_UpperCamelCase							)

									self.assertEqual(image_processor_second.to_dict()							,     image_processor_first.to_dict()							)







			def        snake_case__(  self				:       List[Any]							)   ->List[Any]:
									snake_case_     =			self.image_processing_class(**self.image_processor_dict							)

									with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case_     =			image_processor_first.save_pretrained(_UpperCamelCase							)[0]
															check_json_file_has_correct_format(_UpperCamelCase							)
															snake_case_     =			self.image_processing_class.from_pretrained(_UpperCamelCase							)

									self.assertEqual(image_processor_second.to_dict()							,     image_processor_first.to_dict()							)







			def        snake_case__(  self				:       Tuple							)   ->str:
									snake_case_     =			self.image_processing_class()
									self.assertIsNotNone(_UpperCamelCase							)







			@require_torch
			@require_vision
			def        snake_case__(  self				:       Any							)   ->Any:
									if self.test_cast_dtype is not None:
															# Initialize image_processor
															snake_case_     =			self.image_processing_class(**self.image_processor_dict							)

															# create random PyTorch tensors
															snake_case_     =			prepare_image_inputs(self.image_processor_tester							,     equal_resolution=_UpperCamelCase							,     torchify=_UpperCamelCase							)

															snake_case_     =			image_processor(_UpperCamelCase							,     return_tensors='''pt'''							)
															# for layoutLM compatiblity
															self.assertEqual(encoding.pixel_values.device							,     torch.device('''cpu'''							)							)
															self.assertEqual(encoding.pixel_values.dtype							,     torch.floataa							)

															snake_case_     =			image_processor(_UpperCamelCase							,     return_tensors='''pt'''							).to(torch.floataa							)
															self.assertEqual(encoding.pixel_values.device							,     torch.device('''cpu'''							)							)
															self.assertEqual(encoding.pixel_values.dtype							,     torch.floataa							)

															snake_case_     =			image_processor(_UpperCamelCase							,     return_tensors='''pt'''							).to('''cpu'''							,     torch.bfloataa							)
															self.assertEqual(encoding.pixel_values.device							,     torch.device('''cpu'''							)							)
															self.assertEqual(encoding.pixel_values.dtype							,     torch.bfloataa							)

															with self.assertRaises(_UpperCamelCase							):
																					snake_case_     =			image_processor(_UpperCamelCase							,     return_tensors='''pt'''							).to(torch.bfloataa							,     '''cpu'''							)

															# Try with text + image feature
															snake_case_     =			image_processor(_UpperCamelCase							,     return_tensors='''pt'''							)
															encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]							)}							)
															snake_case_     =			encoding.to(torch.floataa							)

															self.assertEqual(encoding.pixel_values.device							,     torch.device('''cpu'''							)							)
															self.assertEqual(encoding.pixel_values.dtype							,     torch.floataa							)
															self.assertEqual(encoding.input_ids.dtype							,     torch.long							)


import random





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       List[str]							,     _UpperCamelCase				:       list[list[float]]							,     _UpperCamelCase				:       list[int]							,     _UpperCamelCase				:       float = 0.01							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = -1							,     )   ->None:
									snake_case_     =			sample
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									snake_case_     =			target
									if len(self.target							) == 0:
															raise ValueError('''Target data can not be empty'''							)
									if len(self.sample							) != len(self.target							):
															raise ValueError('''Sample data and Target data do not have matching lengths'''							)
									snake_case_     =			learning_rate
									snake_case_     =			epoch_number
									snake_case_     =			bias
									snake_case_     =			len(_UpperCamelCase							)
									snake_case_     =			len(sample[0]							)  # number of columns in dataset
									snake_case_     =			[]







			def        snake_case__(  self				:       List[str]							)   ->None:
									for sample in self.sample:
															sample.insert(0							,     self.bias							)

									for _ in range(self.col_sample							):
															self.weight.append(random.random()							)

									self.weight.insert(0							,     self.bias							)

									snake_case_     =			0

									while True:
															snake_case_     =			False
															for i in range(self.number_sample							):
																					snake_case_     =			0
																					for j in range(self.col_sample + 1							):
																											snake_case_     =			u + self.weight[j] * self.sample[i][j]
																					snake_case_     =			self.sign(_UpperCamelCase							)
																					if y != self.target[i]:
																											for j in range(self.col_sample + 1							):
																																	snake_case_     =			(
																																	    self.weight[j]
																																	    + self.learning_rate
																																	    * (self.target[i] - y)
																																	    * self.sample[i][j]
																																	)
																											snake_case_     =			True
            # print('Epoch: \n',epoch_count)
															snake_case_     =			epoch_count + 1
															# if you want control the epoch or just by error
															if not has_misclassified:
																					print(('''\nEpoch:\n''', epoch_count)							)
																					print('''------------------------\n'''							)
																					# if epoch_count > self.epoch_number or not error:
																					break







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       list[float]							)   ->None:
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									sample.insert(0							,     self.bias							)
									snake_case_     =			0
									for i in range(self.col_sample + 1							):
															snake_case_     =			u + self.weight[i] * sample[i]

									snake_case_     =			self.sign(_UpperCamelCase							)

									if y == -1:
															print(('''Sample: ''', sample)							)
															print('''classification: P1'''							)
									else:
															print(('''Sample: ''', sample)							)
															print('''classification: P2'''							)







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       float							)   ->int:
									return 1 if u >= 0 else -1






lowerCAmelCase_ 			=			[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

lowerCAmelCase_ 			=			[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCAmelCase_ 			=			Perceptron(
					    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
					)
					network.training()
					print('''Finished training perceptron''')
					print('''Enter values to predict or q to exit''')
					while True:
										lowerCAmelCase_ 			=			[]
										for i in range(len(samples[0])):
															lowerCAmelCase_ 			=			input('''value: ''').strip()
															if user_input == "q":
																				break
															lowerCAmelCase_ 			=			float(user_input)
															sample.insert(i, observation)
										network.sort(sample)