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question:Your task is to implement a function that connects to an IMAP server, logs in to the specified mailbox, searches for unread emails within a specific date range, and returns a list of email subjects and their senders. Requirements: 1. **Function Signature**: ```python def fetch_unread_emails(server: str, username: str, password: str, start_date: str, end_date: str, use_ssl: bool = True) -> List[Tuple[str, str]]: ``` 2. **Input Parameters**: - `server` (str): IMAP server address. - `username` (str): Username for the mailbox login. - `password` (str): Password for the mailbox login. - `start_date` (str): Start of the date range in "DD-MMM-YYYY" format. - `end_date` (str): End of the date range in "DD-MMM-YYYY" format. - `use_ssl` (bool): Whether to use a secure connection or not. Default is True. 3. **Output**: - Returns a list of tuples. Each tuple contains the subject (str) and sender (str) of an unread email within the specified date range. 4. **Constraints**: - Ensure secure connection management using context managers (`with` statement). - Proper error handling for connection issues and authentication failures. - Ensure that implementation uses the correct search criteria and parsing methods. 5. **Hints**: - Use the appropriate methods from `IMAP4` and `IMAP4_SSL` classes. - Date format to search might need to be formatted as "DD-MMM-YYYY". Example ```python from imaplib import IMAP4_SSL, IMAP4 from typing import List, Tuple def fetch_unread_emails(server: str, username: str, password: str, start_date: str, end_date: str, use_ssl: bool = True) -> List[Tuple[str, str]]: # Code to implement pass # Example Usage: server = "imap.mailserver.com" username = "exampleuser" password = "password" start_date = "01-Jan-2023" end_date = "31-Jan-2023" emails = fetch_unread_emails(server, username, password, start_date, end_date) for subject, sender in emails: print(f"From: {sender}, Subject: {subject}") ``` **Note**: Ensure to handle all relevant IMAP commands, responses, and potential error cases properly.
answer:from imaplib import IMAP4_SSL, IMAP4 from typing import List, Tuple import email from email.header import decode_header def fetch_unread_emails(server: str, username: str, password: str, start_date: str, end_date: str, use_ssl: bool = True) -> List[Tuple[str, str]]: Connects to an IMAP server, logs in to the specified mailbox, searches for unread emails within a specific date range, and returns a list of email subjects and their senders. results = [] # Choose connection type if use_ssl: mail = IMAP4_SSL(server) else: mail = IMAP4(server) try: mail.login(username, password) mail.select('inbox') # Search for unread emails within the date range search_criteria = f'(UNSEEN SINCE {start_date} BEFORE {end_date})' result, data = mail.search(None, search_criteria) if result == 'OK': email_ids = data[0].split() for email_id in email_ids: res, msg_data = mail.fetch(email_id, '(RFC822)') if res == 'OK': for response_part in msg_data: if isinstance(response_part, tuple): msg = email.message_from_bytes(response_part[1]) subject, encoding = decode_header(msg["Subject"])[0] if isinstance(subject, bytes): subject = subject.decode(encoding if encoding else 'utf-8') sender, encoding = decode_header(msg.get("From"))[0] if isinstance(sender, bytes): sender = sender.decode(encoding if encoding else 'utf-8') results.append((subject, sender)) else: print("No emails found.") except Exception as e: print(f"An error occurred: {e}") finally: mail.logout() return results
question:# Implementing a Custom SMTP Server with Validation Background You are required to create a custom SMTP server by subclassing the `smtpd.SMTPServer` class. The goal of this custom server is to perform validation on incoming emails and only accept emails that meet specific criteria. Requirements 1. **CustomSMTPServer Class**: - Subclass `smtpd.SMTPServer`. - Override the `process_message` method. 2. **Validation Criteria**: - Email messages should only be accepted if they contain a specific keyword (e.g., `"assignment"`) in the subject line. - If the keyword is not present, the server should reject the email with an appropriate error message. 3. **Processing**: - Extract the subject line from the email. - Check if the required keyword is in the subject line. - Return `None` for a successful processing if the keyword is present. - Return a rejection message (e.g., `550 Subject Keyword Missing`) if the keyword is not present. Implementation Details - The `process_message` method has the following signature: ```python def process_message(self, peer, mailfrom, rcpttos, data, **kwargs): ``` - `peer`: the remote host's address. - `mailfrom`: the envelope originator. - `rcpttos`: the envelope recipients. - `data`: a string containing the contents of the e-mail (should comply with **RFC 5321** format). - `kwargs`: additional keyword arguments. Input and Output - **Input**: - Provided as parameters to the `process_message` method within the class. - **Output**: - The method should return `None` if the email is accepted. - The method should return a rejection message (e.g., `550 Subject Keyword Missing`) if the email is not accepted. Constraints - Ensure the server instances do not handle more than 10 concurrent connections at a time for this exercise. Example ```python import smtpd import asyncore class CustomSMTPServer(smtpd.SMTPServer): def process_message(self, peer, mailfrom, rcpttos, data, **kwargs): # Implement the validation logic here pass # Instantiate and run the server server = CustomSMTPServer(('localhost', 1025), None) asyncore.loop() ``` Submission Provide the full implementation of the `CustomSMTPServer` class with the overridden `process_message` method. Ensure the validation logic for the keyword in the subject is correctly implemented and the server handles messages as specified.
answer:import smtpd import asyncore from email import parser, policy class CustomSMTPServer(smtpd.SMTPServer): def process_message(self, peer, mailfrom, rcpttos, data, **kwargs): email_parser = parser.Parser(policy=policy.default) email_message = email_parser.parsestr(data) subject = email_message['subject'] if 'assignment' in subject.lower(): return None else: return '550 Subject Keyword Missing' # Function to start the server def run_server(): server = CustomSMTPServer(('localhost', 1025), None) asyncore.loop() # Example usage (in real use this would be run in a script) # run_server()
question:Design a Python script that connects to a POP3 server, logs in using provided credentials, retrieves all emails, and prints the subject and sender of each email. If there are any errors (e.g., authentication failure, connectivity issues), handle these gracefully and provide an appropriate error message. Your script should also have an option to delete all retrieved emails after they are processed. # Instructions 1. Implement a function `connect_and_retrieve(host, port, username, password, delete=False)` which: - Connects to the given POP3 server using the provided `host` and `port`. - Logs in with the provided `username` and `password`. - Retrieves all emails and prints the "Subject" and "From" headers of each email. - If `delete` is `True`, deletes all retrieved emails from the server. - Handles any exceptions that may occur during the process and prints appropriate error messages. # Input - `host` (string): The hostname of the POP3 server. - `port` (int): The port number of the POP3 server. - `username` (string): The username used for authentication. - `password` (string): The password used for authentication. - `delete` (boolean): Flag indicating whether to delete emails after retrieval. # Output - Prints the "Subject" and "From" headers of each retrieved email, or appropriate error messages in case of failures. # Constraints - The function should handle exceptions avoiding the termination of the script due to unexpected errors. - Ensure secure handling of credentials (avoid logging sensitive information). # Example ```python connect_and_retrieve('pop3.example.com', 110, 'myuser', 'mypassword', delete=False) ``` This example should print the "Subject" and "From" headers of all emails in the mailbox on `pop3.example.com` for the user `myuser`. # Notes - You might need the `email` module to parse the email contents correctly. - Ensure that the script uses a secure channel for sensitive operations, especially when dealing with credentials.
answer:import poplib from email import parser from email.policy import default def connect_and_retrieve(host, port, username, password, delete=False): try: # Connect to the POP3 server mail = poplib.POP3(host, port) mail.user(username) mail.pass_(password) # Get the number of messages num_messages = len(mail.list()[1]) for i in range(num_messages): # Retrieve the message response, lines, octets = mail.retr(i + 1) message_content = b'rn'.join(lines).decode('utf-8') message = parser.Parser(policy=default).parsestr(message_content) # Print out the subject and sender print(f"Subject: {message['subject']}") print(f"From: {message['from']}") # Optionally delete the email if delete: mail.dele(i + 1) # Close the connection mail.quit() except poplib.error_proto as e: print(f"POP3 Protocol error: {e}") except Exception as e: print(f"An error occurred: {e}")
question:# Advanced PyTorch Question: Patching BatchNorm with GroupNorm **Objective:** You are tasked with creating a neural network in PyTorch that includes batch normalization layers. Additionally, you must implement a mechanism to patch the model to replace `BatchNorm` with `GroupNorm` as described in the documentation. **Requirements:** 1. **Neural Network Design:** - Construct a simple Convolutional Neural Network (CNN) that includes at least two convolutional layers followed by batch normalization layers. 2. **Function Implementation:** - Implement the function `replace_batchnorm_with_groupnorm` which takes a PyTorch model and replaces all `BatchNorm2d` layers with `GroupNorm` layers. 3. **Constraints:** - Ensure `G`, the number of groups for `GroupNorm`, always divides `C`, the number of input channels for `GroupNorm`. - Do not track running statistics in `BatchNorm` when replacing it with `GroupNorm`. 4. **Input and Output:** - Function signature: `def replace_batchnorm_with_groupnorm(model: nn.Module, num_groups: int) -> nn.Module:` - Input: A PyTorch model (`model`) and an integer (`num_groups`). - Output: The modified PyTorch model with `GroupNorm` layers instead of `BatchNorm` layers. # Example: ```python import torch import torch.nn as nn import torch.nn.functional as F class SimpleCNN(nn.Module): def __init__(self): super(SimpleCNN, self).__init__() self.conv1 = nn.Conv2d(3, 16, 3, 1) self.bn1 = nn.BatchNorm2d(16) self.conv2 = nn.Conv2d(16, 32, 3, 1) self.bn2 = nn.BatchNorm2d(32) self.fc1 = nn.Linear(32 * 6 * 6, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.max_pool2d(x, 2) x = torch.flatten(x, 1) x = F.relu(self.fc1(x)) x = self.fc2(x) return x def replace_batchnorm_with_groupnorm(model: nn.Module, num_groups: int) -> nn.Module: for name, module in model.named_children(): if isinstance(module, nn.BatchNorm2d): num_channels = module.num_features group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=num_channels) setattr(model, name, group_norm) else: replace_batchnorm_with_groupnorm(module, num_groups) return model model = SimpleCNN() num_groups = 4 model = replace_batchnorm_with_groupnorm(model, num_groups) ``` **Implementation Notes:** - You are free to design any simple neural network with `BatchNorm` layers. - Pay attention to the replacement strategy to ensure that the model architecture remains valid. This question challenges students by asking them to understand and manipulate model layers within PyTorch, ensuring they comprehend normalization techniques and the constraints involved in their transformations.
answer:import torch import torch.nn as nn import torch.nn.functional as F class SimpleCNN(nn.Module): def __init__(self): super(SimpleCNN, self).__init__() self.conv1 = nn.Conv2d(3, 16, 3, 1) self.bn1 = nn.BatchNorm2d(16) self.conv2 = nn.Conv2d(16, 32, 3, 1) self.bn2 = nn.BatchNorm2d(32) self.fc1 = nn.Linear(32 * 6 * 6, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.max_pool2d(x, 2) x = torch.flatten(x, 1) x = F.relu(self.fc1(x)) x = self.fc2(x) return x def replace_batchnorm_with_groupnorm(model: nn.Module, num_groups: int) -> nn.Module: for name, module in model.named_children(): if isinstance(module, nn.BatchNorm2d): num_channels = module.num_features group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=num_channels) setattr(model, name, group_norm) else: replace_batchnorm_with_groupnorm(module, num_groups) return model