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destButton.grid(row=5, column=2)
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self.statusLabel = Label(self.top, text='Brought to you by Oh Shunhao and NUSMods')
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self.statusLabel.grid(row=6, columnspan=3, padx=20, pady=20)
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startButton = Button(self.top, text='Start Download!', command=self.startDownload)
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startButton.grid(row=7, columnspan=3)
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root.mainloop()
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def askForDestination(self):
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self.destination = tkFileDialog.askdirectory(mustexist=False, parent=self.top, title='Choose a destination')
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self.destField.delete(0, END)
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self.destField.insert(0, self.destination)
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def startDownload(self):
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module = self.moduleField.get()
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username = self.usernameField.get()
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password = self.passwordField.get()
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destination = self.destField.get()
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ed = examdownloader.examdownloader('GUI')
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def downloadCallback(status, lastfile='', numFiles=0):
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if status:
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self.updateStatus(str(numFiles) + ' papers downloaded successfully!', 'success')
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subprocess.call(['open', '-R', lastfile])
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else:
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self.updateStatus('Paper not released by Department', 'error')
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thread.start_new_thread(ed.getContents, (module, username, password, destination, downloadCallback, self.updateStatus))
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def updateStatus(self, msg, type='normal'):
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self.statusLabel['text'] = msg
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if type == 'success':
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self.statusLabel['fg'] = 'green'
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elif type == 'error':
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self.statusLabel['fg'] = 'red'
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else:
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self.statusLabel['fg'] = 'blue'
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if __name__ == '__main__':
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examdownloadergui()
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# <FILESEP>
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import torch
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import numpy as np
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from rope import apply_rotary_emb
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seed = 0
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def construct_query() -> torch.Tensor:
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'''
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Shape: (batch_size, seqlen, n_local_heads, self.head_dim)
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'''
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return 2 * torch.ones([1, 2, 2, 4])
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def construct_key() -> torch.Tensor:
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'''
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Shape: (batch_size, seqlen, n_local_kv_heads, self.head_dim)
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'''
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return 3 * torch.ones([1, 2, 2, 4])
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def test_apply_rotary_emb() -> tuple[torch.Tensor, torch.Tensor]:
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rng = np.random.default_rng(seed)
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torch.manual_seed(seed)
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model = torch.nn.Linear(3, 2, bias=False)
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test_query = construct_query()
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test_key = construct_key()
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rotary_embeddings = apply_rotary_emb(test_query, test_key, 4, 20)
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rotary_query_embedding, rotary_key_embedding = rotary_embeddings
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return rotary_query_embedding, rotary_key_embedding
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actual_query_rope_embedding, actual_key_rope_embedding = test_apply_rotary_emb()
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ref_query_rope_embedding, ref_key_rope_embedding = torch.load("./rotary_embedding_actual.data")
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assert torch.allclose(ref_query_rope_embedding, actual_query_rope_embedding)
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assert torch.allclose(ref_key_rope_embedding, actual_key_rope_embedding)
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print("Rotary embedding test passed!")
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# <FILESEP>
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""Train an autoencoder."""
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import argparse
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import importlib
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import importlib.util
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import os
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import sys
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import time
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sys.dont_write_bytecode = True
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import numpy as np
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import torch
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import torch.utils.data
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