text stringlengths 1 93.6k |
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async def iter_messages(self, chat_id: Union[int, str], limit: int, offset: int = 0) -> Optional[AsyncGenerator["types.Message", None]]:
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current = offset
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while True:
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new_diff = min(200, limit - current)
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if new_diff <= 0:
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return
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messages = await self.get_messages(chat_id, list(range(current, current+new_diff+1)))
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for message in messages:
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yield message
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current += 1
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Bot().run()
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# <FILESEP>
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from torchvision.datasets import CIFAR10
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import torchvision.transforms as transforms
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import numpy as np
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import torch
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from PIL import Image
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import cv2
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import time
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class cifar10(CIFAR10):
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def __init__(self, root,
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classes=range(10),
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train=True,
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transform=None,
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target_transform=None,
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download=False,
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mean_image=None):
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super(cifar10, self).__init__(root,
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train=train,
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transform=transform,
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target_transform=target_transform,
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download=download)
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self.tensorTranform = transforms.ToTensor()
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self.train = train
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self.img_size = 224
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if mean_image is not None:
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mean_image = mean_image.transpose(1,2,0)
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self.mean_image = cv2.resize(mean_image, (self.img_size, self.img_size))
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self.mean_image = self.mean_image.transpose(2,0,1)
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# Select subset of classes
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if self.train:
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self.train_data = self.data
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self.train_labels = self.targets
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train_data = []
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train_labels = []
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for i in range(len(self.train_data)):
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if self.train_labels[i] in classes:
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curr_img = cv2.resize(self.train_data[i], (self.img_size, self.img_size))
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curr_img = curr_img.transpose(2,0,1)
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if mean_image is None:
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train_data.append(curr_img/255.)
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else:
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train_data.append(curr_img/255. - self.mean_image)
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train_labels.append(int(self.train_labels[i]))
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self.train_data = np.array(train_data, dtype = np.float32)
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self.train_labels = np.array(train_labels)
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if mean_image is None:
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self.mean_image = np.mean(self.train_data, axis=0)
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else:
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self.test_data = self.data
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self.test_labels = self.targets
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test_data = []
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test_labels = []
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for i in range(len(self.test_data)):
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if self.test_labels[i] in classes:
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curr_img = cv2.resize(self.test_data[i], (self.img_size, self.img_size))
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curr_img = curr_img.transpose(2,0,1)
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test_data.append(curr_img/255. - self.mean_image)
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test_labels.append(int(self.test_labels[i]))
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self.test_data = np.array(test_data, dtype = np.float32)
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self.test_labels = test_labels
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def __getitem__(self, index):
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if self.train:
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image = self.train_data[index]
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random_cropped = np.zeros(image.shape, dtype=np.float32)
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padded = np.pad(image,((0,0),(4,4),(4,4)),mode='constant')
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crops = np.random.random_integers(0,high=8,size=(1,2))
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