



"""simple docstring"""






import random

class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a    ,       a = 0.01    ,       a = 10_00    ,       a = -1    ,       ):




										"""simple docstring"""





										snake_case_						:List[str]								=						sample
										if len(self.sample   ) == 0:
															raise ValueError("Sample data can not be empty"   )
										snake_case_						:Union[str, Any]								=						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_						:List[Any]								=						learning_rate
										snake_case_						:Optional[Any]								=						epoch_number
										snake_case_						:Union[str, Any]								=						bias
										snake_case_						:Union[str, Any]								=						len(a   )
										snake_case_						:str								=						len(sample[0]   )  # number of columns in dataset
										snake_case_						:list								=						[]







					def 		_a (      self   ):




										"""simple docstring"""





										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_						:List[Any]								=						0

										while True:
															snake_case_						:Tuple								=						False
															for i in range(self.number_sample   ):
																				snake_case_						:Optional[int]								=						0
																				for j in range(self.col_sample + 1   ):
																									snake_case_						:int								=						u + self.weight[j] * self.sample[i][j]
																				snake_case_						:List[Any]								=						self.sign(a   )
																				if y != self.target[i]:
																									for j in range(self.col_sample + 1   ):
																														snake_case_						:List[Any]								=						(
																														    self.weight[j]
																														    + self.learning_rate
																														    * (self.target[i] - y)
																														    * self.sample[i][j]
																														)
																									snake_case_						:str								=						True
            # print('Epoch: \n',epoch_count)
															snake_case_						:Union[str, Any]								=						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 		_a (      self    ,       a   ):




										"""simple docstring"""





										if len(self.sample   ) == 0:
															raise ValueError("Sample data can not be empty"   )
										sample.insert(0    ,       self.bias   )
										snake_case_						:Optional[Any]								=						0
										for i in range(self.col_sample + 1   ):
															snake_case_						:Optional[int]								=						u + self.weight[i] * sample[i]

										snake_case_						:Dict								=						self.sign(a   )

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







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										return 1 if u >= 0 else -1



__UpperCAmelCase      :      List[Any]   					=					[
    [-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],
]

__UpperCAmelCase      :      List[Any]   					=					[
    -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()

				__UpperCAmelCase      :      List[Any]   					=					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:
								__UpperCAmelCase      :      list   					=					[]
								for i in range(len(samples[0])):
												__UpperCAmelCase      :      Any   					=					input('value: ').strip()
												if user_input == "q":
																break
												__UpperCAmelCase      :      Tuple   					=					float(user_input)
												sample.insert(i, observation)
								network.sort(sample)






"""simple docstring"""






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     CONFIG_NAME







					@register_to_config
					def __init__(      self    ,       a = True    ,       a = 8    ,       a = "lanczos"    ,       a = True    ,       a = False    ,       ):




										"""simple docstring"""





										super().__init__()







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:Union[str, Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:List[str]								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:str								=						[Image.fromarray(a   ) for image in images]

										return pil_images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if not isinstance(a    ,       a   ):
															snake_case_						:Dict								=						[images]
										snake_case_						:Optional[int]								=						[np.array(a   ).astype(np.floataa   ) / 255.0 for image in images]
										snake_case_						:Optional[int]								=						np.stack(a    ,       axis=0   )

										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:int								=						images[..., None]

										snake_case_						:str								=						torch.from_numpy(images.transpose(0    ,       3    ,       1    ,       2   )   )
										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										snake_case_						:str								=						images.cpu().permute(0    ,       2    ,       3    ,       1   ).float().numpy()
										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return 2.0 * images - 1.0







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return (images / 2 + 0.5).clamp(0    ,       1   )







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										snake_case_						:List[str]								=						image.convert("RGB"   )
										return image







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										if height is None:
															snake_case_						:Optional[Any]								=						image.height
										if width is None:
															snake_case_						:Dict								=						image.width

										snake_case_	,  snake_case_						:Optional[Any]								=						(
										    x - x % self.config.vae_scale_factor for x in (width, height)
										)  # resize to integer multiple of vae_scale_factor
										snake_case_						:Any								=						image.resize((width, height)    ,       resample=PIL_INTERPOLATION[self.config.resample]   )
										return image







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						(PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(a    ,       a   ):
															snake_case_						:str								=						[image]
										elif not (isinstance(a    ,       a   ) and all(isinstance(a    ,       a   ) for i in image   )):
															raise ValueError(
															    F'''Input is in incorrect format: {[type(a   ) for i in image]}. Currently, we only support {', '.join(a   )}'''   )

										if isinstance(image[0]    ,       PIL.Image.Image   ):
															if self.config.do_convert_rgb:
																				snake_case_						:int								=						[self.convert_to_rgb(a   ) for i in image]
															if self.config.do_resize:
																				snake_case_						:Tuple								=						[self.resize(a    ,       a    ,       a   ) for i in image]
															snake_case_						:Dict								=						self.pil_to_numpy(a   )  # to np
															snake_case_						:int								=						self.numpy_to_pt(a   )  # to pt

										elif isinstance(image[0]    ,       np.ndarray   ):
															snake_case_						:Optional[Any]								=						np.concatenate(a    ,       axis=0   ) if image[0].ndim == 4 else np.stack(a    ,       axis=0   )
															snake_case_						:List[Any]								=						self.numpy_to_pt(a   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[int]								=						image.shape
															if self.config.do_resize and (
															    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
															):
																				raise ValueError(
																				    F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
																				    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''   )

										elif isinstance(image[0]    ,       torch.Tensor   ):
															snake_case_						:Optional[Any]								=						torch.cat(a    ,       axis=0   ) if image[0].ndim == 4 else torch.stack(a    ,       axis=0   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:int								=						image.shape

															# don't need any preprocess if the image is latents
															if channel == 4:
																				return image

															if self.config.do_resize and (
															    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
															):
																				raise ValueError(
																				    F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
																				    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''   )

        # expected range [0,1], normalize to [-1,1]
										snake_case_						:Union[str, Any]								=						self.config.do_normalize
										if image.min() < 0:
															warnings.warn(
															    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
															    F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''    ,       a    ,       )
															snake_case_						:Optional[int]								=						False

										if do_normalize:
															snake_case_						:Tuple								=						self.normalize(a   )

										return image







					def 		_a (      self    ,       a    ,       a = "pil"    ,       a = None    ,       ):




										"""simple docstring"""





										if not isinstance(a    ,       torch.Tensor   ):
															raise ValueError(
															    F'''Input for postprocessing is in incorrect format: {type(a   )}. We only support pytorch tensor'''   )
										if output_type not in ["latent", "pt", "np", "pil"]:
															snake_case_						:List[Any]								=						(
															    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
															    "`pil`, `np`, `pt`, `latent`"
															)
															deprecate("Unsupported output_type"    ,       "1.0.0"    ,       a    ,       standard_warn=a   )
															snake_case_						:Dict								=						"np"

										if output_type == "latent":
															return image

										if do_denormalize is None:
															snake_case_						:List[Any]								=						[self.config.do_normalize] * image.shape[0]

										snake_case_						:Any								=						torch.stack(
										    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

										if output_type == "pt":
															return image

										snake_case_						:int								=						self.pt_to_numpy(a   )

										if output_type == "np":
															return image

										if output_type == "pil":
															return self.numpy_to_pil(a   )



class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     CONFIG_NAME







					@register_to_config
					def __init__(      self    ,       a = True    ,       a = 8    ,       a = "lanczos"    ,       a = True    ,       ):




										"""simple docstring"""





										super().__init__()







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:List[Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:str								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:Any								=						[Image.fromarray(image[:, :, :3]   ) for image in images]

										return pil_images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return image[:, :, 1] * 2**8 + image[:, :, 2]







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:Optional[int]								=						images[None, ...]
										snake_case_						:Union[str, Any]								=						images[:, :, :, 3:]
										if images.shape[-1] == 6:
															snake_case_						:Tuple								=						(images_depth * 2_55).round().astype("uint8"   )
															snake_case_						:Optional[Any]								=						[
															    Image.fromarray(self.rgblike_to_depthmap(a   )    ,       mode="I;16"   ) for image_depth in images_depth
															]
										elif images.shape[-1] == 4:
															snake_case_						:Optional[Any]								=						(images_depth * 65535.0).astype(np.uintaa   )
															snake_case_						:List[Any]								=						[Image.fromarray(a    ,       mode="I;16"   ) for image_depth in images_depth]
										else:
															raise Exception("Not supported"   )

										return pil_images







					def 		_a (      self    ,       a    ,       a = "pil"    ,       a = None    ,       ):




										"""simple docstring"""





										if not isinstance(a    ,       torch.Tensor   ):
															raise ValueError(
															    F'''Input for postprocessing is in incorrect format: {type(a   )}. We only support pytorch tensor'''   )
										if output_type not in ["latent", "pt", "np", "pil"]:
															snake_case_						:Any								=						(
															    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
															    "`pil`, `np`, `pt`, `latent`"
															)
															deprecate("Unsupported output_type"    ,       "1.0.0"    ,       a    ,       standard_warn=a   )
															snake_case_						:Optional[Any]								=						"np"

										if do_denormalize is None:
															snake_case_						:Any								=						[self.config.do_normalize] * image.shape[0]

										snake_case_						:Tuple								=						torch.stack(
										    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

										snake_case_						:List[str]								=						self.pt_to_numpy(a   )

										if output_type == "np":
															if image.shape[-1] == 6:
																				snake_case_						:Optional[int]								=						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]    ,       axis=0   )
															else:
																				snake_case_						:Any								=						image[:, :, :, 3:]
															return image[:, :, :, :3], image_depth

										if output_type == "pil":
															return self.numpy_to_pil(a   ), self.numpy_to_depth(a   )
										else:
															raise Exception(F'''This type {output_type} is not supported'''   )

