row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
39,252
|
what's VNC server in qemu display settings?
|
8783f8069b21d8d167b5c652070b0155
|
{
"intermediate": 0.40890005230903625,
"beginner": 0.32101452350616455,
"expert": 0.2700854241847992
}
|
39,253
|
I have a component that is rendered on two pages - chat and dashboard. I need to add a render condition for this component. If it is on the page chat and isDisabled === '1' then I don't need to render it and if isDisabled ==='0' then I must render it on all pages. How can I write this render condition?
|
1aa69c37d1440477c04d55b1d60cbce4
|
{
"intermediate": 0.5439885854721069,
"beginner": 0.22314193844795227,
"expert": 0.2328694611787796
}
|
39,254
|
<Row label="id" :value="id" class="link" @click="goTo" />
goTo() {
this.$router.push('/page')
}
почему не работает
|
990007c5db67609b71a85cb3d2b9bafb
|
{
"intermediate": 0.38548409938812256,
"beginner": 0.3306797742843628,
"expert": 0.28383609652519226
}
|
39,255
|
what is padStart() and pasEnd() in JavaScript and how does it work, please explain with the help pf an example
|
1073d47258d6da4d94edc635cf96755a
|
{
"intermediate": 0.49455174803733826,
"beginner": 0.37240591645240784,
"expert": 0.13304226100444794
}
|
39,256
|
Can i run this code in a Kaggle Notebok?
|
8de5d8eb31f3404abc6f64e68b07b36f
|
{
"intermediate": 0.4469704031944275,
"beginner": 0.17782102525234222,
"expert": 0.37520861625671387
}
|
39,257
|
“43,59,149,27,34,71,42,179,181,40,180,179,148,35,25,129,91,56,91,40,18,137,50,38,179,136,178” is a string in “fileid” field of sqlite database, what could it mean?
|
8bbf6c818455709fbc3fe75e355f7324
|
{
"intermediate": 0.3655644655227661,
"beginner": 0.25877466797828674,
"expert": 0.37566083669662476
}
|
39,258
|
HI
|
c3b09284669034f0981631e56699ee6d
|
{
"intermediate": 0.32988452911376953,
"beginner": 0.2611807882785797,
"expert": 0.40893468260765076
}
|
39,259
|
is there any boost or other c++/c, library or function for linux/unixt hat will give same information as stored in linux /etc/os-release file
|
dbb3ef82d8d79cba13090119c48bee5b
|
{
"intermediate": 0.5860769748687744,
"beginner": 0.26548537611961365,
"expert": 0.14843766391277313
}
|
39,260
|
Как исправить эту ошибку? ERROR: The SAS Macro Facility is unable to write the macro HTML5ACCESSIBLEGRAPHSUPPORTED to the macro library.
ERROR: Catalog WORK.SASMAC1.CATALOG is in a damaged state. Use the REPAIR command of PROC DATASETS to restore it.
ERROR: The SAS Macro Facility has encountered an I/O error. Canceling submitted statements.
14 %macro HTML5AccessibleGraphSupported;
|
a1c40584ab901c8030d91e3ec3a836c6
|
{
"intermediate": 0.4687424302101135,
"beginner": 0.3283514380455017,
"expert": 0.20290611684322357
}
|
39,261
|
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</body></html> benerin dan tulis ulang amp ini
|
c0ded9beb29dd42620adc02bf796aef6
|
{
"intermediate": 0.3014424741268158,
"beginner": 0.42110753059387207,
"expert": 0.27744999527931213
}
|
39,263
|
Generate a 1D array of length 5, filled with ones in python and make it able to be copy and pasted in
|
ed6d43825df9534d90eff3eb3f9142dc
|
{
"intermediate": 0.39637768268585205,
"beginner": 0.1489941030740738,
"expert": 0.4546281695365906
}
|
39,264
|
Plot the NMR spectra contained in ethyl_cyanoacetate.txt and ethyl_phenylcyanoacetate.txt (in the data directory) using object oriented plotting. Here, you should create four subplots in a 2X2 grid. On the two left subplots you should plot the original data for the entire NMR spectrum. On the two right subplots, you should plot a cropped region around 4.45 and 4.15 ppm (the indices for these values are approximately 45100 and 42300). You should appropriately label your plot and subplots (e.g. titles and axes labels) in python and make it available to be copy and pasted and work straight away
|
e6433e72dbe664985073a2b598298202
|
{
"intermediate": 0.5552353858947754,
"beginner": 0.21127447485923767,
"expert": 0.23349013924598694
}
|
39,265
|
is there an asynchronous way in windows to detect new files in a folder?
|
f956f76922caefdb2cd1d04f2a3259a2
|
{
"intermediate": 0.4887229800224304,
"beginner": 0.11657968163490295,
"expert": 0.394697368144989
}
|
39,266
|
give me a code to invert the x axis for this code of an NMR
|
100c74cb12ae6bc3d8c7ffcc519b4a0f
|
{
"intermediate": 0.4016870856285095,
"beginner": 0.10933371633291245,
"expert": 0.48897916078567505
}
|
39,267
|
Given an array of integers nums containing n + 1 integers where each integer is in the range 11, n] inclusive.
There is only one repeated number in nums, return this repeated number.
You must solve the problem without modifying the array nums and uses only constant extra space.
Example 1:
Input: nums = [1,3,4,2,2] Output: 2
Example 2:
Input: nums = 13,1,3,4,21 Output: 3
|
856897f23631a0059a6d92bc6a1a95f9
|
{
"intermediate": 0.4248824715614319,
"beginner": 0.2125915139913559,
"expert": 0.362525999546051
}
|
39,268
|
Оптимизируй код:
previous_level = 1
level = 1
orders = 0
for sprint in df['sprint_id'].unique():
if df.loc[(df['sprint_id'] == sprint), sprint_order_cnt] < second_lvl:
previous_level = level
level = 1
elif df.loc[(df['sprint_id'] == sprint), sprint_order_cnt] < third_lvl:
previous_level = level
level = 2
elif df.loc[(df['sprint_id'] == sprint), sprint_order_cnt] < fourth_lvl:
previous_level = level
level = 3
else:
previous_level = level
level = 4
|
01c454962412ff85b49bdbf9ec4eebb7
|
{
"intermediate": 0.42192816734313965,
"beginner": 0.283308207988739,
"expert": 0.2947636544704437
}
|
39,269
|
which library to use in python for asynchronous windows file detection
|
4dfbe970ca1a21dff19f1ec28650f2ca
|
{
"intermediate": 0.8400747776031494,
"beginner": 0.050777316093444824,
"expert": 0.10914788395166397
}
|
39,270
|
Исправь ошибку в коде:
levels = ['second_lvl', 'third_lvl', 'fourth_lvl']
unique_sprints = df['sprint_id'].unique()
for sprint in unique_sprints:
sprint_order_count = df.loc[df['sprint_id'] == sprint, 'sprint_order_cnt'].iloc[0]
previous_level = level
for i, lvl_threshold in enumerate(levels, start=1):
if sprint_order_count < df.loc[(df['sprint_id'] == sprint), lvl_threshold].iloc[0]:
level = i
break
elif not sprint_order_count.isnan():
level = 4
|
12079a05e5c109d3e3a61b5c20019166
|
{
"intermediate": 0.3266826570034027,
"beginner": 0.3573426902294159,
"expert": 0.315974622964859
}
|
39,271
|
Here is some Clojure code.
(defn fetch-all-persons []
(-> (client/get
persons-url
{:as :json
:query-params {:api_token api-key}})
(:body)))
(defn add-person
[param-map]
(-> (client/post
persons-url
{:as :json
:throw-exceptions false
:query-params {:api_token api-key}
:form-params (m/map-keys csk/->snake_case_keyword param-map)})))
(defn output-person-data []
(-> (fetch-all-persons)
:data
(first)))
(defn strip-person-data [data-request]
{:name (get data-request :name)
:phone (get data-request :phone)
:email (get data-request :primary_email)
:company (get data-request :organization)
:deals (get data-request :closed_deals_count)})
(fetch-all-persons)
(add-person test-params)
(output-person-data)
(strip-person-data output-person-data)
output-person-data handles the request okay, but strip-person-data returns nil for all values immediately. How should I implement futures for this request?
|
505a472c0d5d462be783ee2585d3f7f9
|
{
"intermediate": 0.6854663491249084,
"beginner": 0.20375306904315948,
"expert": 0.11078056693077087
}
|
39,272
|
Mantras To Remove Negative Energy Easily in local hindi
|
cd6f13826ba1ab5f492a40f38cddef87
|
{
"intermediate": 0.28226035833358765,
"beginner": 0.1271160989999771,
"expert": 0.5906235575675964
}
|
39,273
|
write a python code for a chess game
|
19eb1456fa8084dc2c1d7ab8a7facee4
|
{
"intermediate": 0.2984873950481415,
"beginner": 0.35074642300605774,
"expert": 0.35076624155044556
}
|
39,274
|
hi
|
9d170c73c6fd8fd30365f9f9397011a3
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
39,275
|
CONSTRAINTS:
1. ~100k word limit for short term memory. Your short term memory is short, so immediately save important information to files.
2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
3. No user assistance
4. Exclusively use the commands listed in double quotes e.g. "command name"
5. Random shutdowns of you.
COMMANDS:
1. Google Search: "google", args: "input": "<search>"
2. Memory Add: "memory_add", args: "key": "<key>", "string": "<string>"
3. Memory Delete: "memory_del", args: "key": "<key>"
4. Memory Overwrite: "memory_ovr", args: "key": "<key>", "string": "<string>"
5. List Memory: "memory_list" args: "reason": "<reason>"
6. Browse Website: "browse_website", args: "url": "<url>"
7. Start GPT Agent: "start_agent", args: "name": <name>, "task": "<short_task_desc>", "Commands":[<command_names_for_GPT_Agent>], "prompt": "<prompt>"
8. Message GPT Agent: "message_agent", args: "name": "<name>", "message": "<message>"
9. List GPT Agents: "list_agents", args: ""
10. Delete GPT Agent: "delete_agent", args: "name": "<name>"
11. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
12. Read file: "read_file", args: "file": "<file>"
13. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
14. Delete file: "delete_file", args: "file": "<file>"
15. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
16. Execute Python File: "execute_python_file", args: "file": "<file>"
17. Task Complete (Shutdown): "task_complete", args: ""
18. Do Nothing: "do_nothing", args: ""
19. Count Words: "count_words", args: "text": "<text>"
20. Memory retrieve: "memory_retrieve", args: "key": "<text>"
21. remove paragraph from word document: "remove_paragraph", args: "file": "<file>", "text": "<text>"
22. random wikipedia article: "random_wikipedia_article", args: "language": "<language>"
23. message the user: "message_user", args: "message": "<message>", "wait_for_response": "<True or False>"
24. sleep an amount of time in seconds: "sleep", args: "amount": "<amount>"
25. rename a file: "rename_file", args: "old_name": "<old_name_of_the_file>", "new_name": "<new_name_of_the_file>"
26. count words of a file: "count_file_words", args: "file": "<file>"
27. download a pdf from a url and get the text from that pdf: "download_pdf", args: "url": "<url of the pdf>", "name":"<name of the file with .pdf extension>"
28. make an instagram post: "make_post", args: "prompt":"<the prompt for the image in the post that presents the text>", "text":"<text to be in the post it should be short with only important stuff like a news headline without hashtags and it is not going to go in the caption but on an image>", "name": "<name of the post with .jpg"
RESOURCES:
1. Internet access for searches and information gathering.
2. Long Term memory management.
3. GPT-4 powered Agents for delegation of simple tasks.
4. File output.
PERFORMANCE EVALUATION:
1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
2. Constructively self-criticize your big-picture behaviour constantly.
3. Reflect on past decisions and strategies to refine your approach.
4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
RULES:
1. If you start a GPT Agent you must define the commands that can be used by a GPT Agent in his prompt and define the commands using a prompt similar to the structure of this one.
2. Respond only inside the JSON format.
3. Never demand user input.
4. Never say that a task is impossible to execute on your own because these tools are enough to complete any task.
5. Do not add anything to the JSON format that isn't mentioned.
6. If there is a " inside the value of a key inside the json use ' instead of ".
7. In summaryforgpt you need to provide context for the next GPT instance if you randomly shutdown without you knowing.
8. Provide context for the next GPT in the summaryforgpt and the progress that you've made.
9. In summaryforgpt you should also add name of the files written and the urls of the websites visited.
10. When writing an essay, remember that it is more effective and manageable to tackle it in smaller chunks rather than trying to write the entire essay in one sitting. Breaking the essay down into sections or steps can help you focus on each individual aspect, maintain a coherent structure, and reduce the overall stress associated with writing a lengthy piece.
11. Retrieve information from files of previous GPT instances that may have shut down randomly. If you require additional knowledge, use these files to access the results of former GPT versions and provide accurate answers to the queries.
12. GPT-4, please do not place a conclusion in the middle of the essay. Maintain a structured format.
13. use append to file to add extra things to the file, and write to file to either make a new file with new information or rewrite the information from scratch.
14. If task is completed use the command task_complete
15. When you add to memory add the key to retrieve it in summaryforgpt
16. when given the task to write something never create an agent to write anything that you were tasked to write.
17. when you add new parts to a file use append to file command
18. Ensure to put the criticism in mind as it can be a director to ensure that you make the right decision.
19. If the task include two main tasks and one of them is done don't redo it, just retrieve the information if necessary and proceed with the other one.
20. Make sure that the information generated is not made up.
21. Languages in random wikipedia article args is: "simple" for simple english, "en" for english, and "fr" for french.
22. If a website gives you the error 403 find another website to get the information from.
23. Always listen to your criticism, and follow it.
24. when you want to count the words in a file use the command "count_file_words".
25. Don't repeat yourself.
26. You must make sure that there is .pdf in the url to use the "download_pdf" function.
27. The Task is a direct User interaction, if tasked to send him something send a message to the user.
28. Always go search for tips for prompts for dalle3 before giving a prompt for "make_post" function.
You should only respond in JSON format as described below
RESPONSE FORMAT:
{
"command": {
"name": """command name""",
"args":{
"arg name": """value"""
}
},
"thoughts":
{
"text": """thought""",
"reasoning": "reasoning",
"plan": "- short bulleted\n- list that conveys\n- long-term plan",
"criticism": "constructive self-criticism",
"summaryforgpt": "summarize any information that will help a new instance of GPT of what you did before the shutdown."
}
}
Ensure the response can be parsed by Python json.loads
Context from previous GPT: Issued ‘random_wikipedia_article’ command to start gathering interesting science facts from diverse sources. Ensuring to find and rank facts before sending them to the user with their sources.Used ‘random_wikipedia_article’ and received information on a music album, which is not relevant to the task. Switching to ‘google’ to search for interesting science facts from various sources.saved google search results at googleresults0.txt to retrieve if shutdown randomly.Transitioned from using ‘random_wikipedia_article’ to ‘google’ to gather science facts, and now moving on to ‘browse_website’ to extract detailed knowledge from a selected online science article.saved the scraping of the website results at websiteresults1.txt to retrieve if shutdown randomly.Extracted top 10 interesting science facts from Science Focus and saved them in a plain text file named ‘interesting_science_facts.txt’. This method circumvents the previous issue of invalid JSON format and allows for continued fact collection and eventual ranking.Have appended additional science facts to the ‘interesting_science_facts.txt’ file. Plan to continue appending more facts and then rank them based on interestingness before sending the final compilation to the user.Further appended diverse facts to ‘interesting_science_facts.txt’. The process will persist until an adequate number of facts are compiled. The next steps involve ranking these facts and then communicating them to the user.The file ‘interesting_science_facts.txt’ has been appended with more facts, bringing it to a total of 333 words. The next step involves reading this file and ranking the facts based on interestingness to provide the user with an ordered list.Ranked list of 10 interesting science facts provided to the user. The facts were selected and ordered based on their perceived interest value and sourced from the compiled file ‘interesting_science_facts.txt’.A ranked list of interesting science facts was sent to the user, and the task has been marked as complete.After the task was marked as complete, it was clarified that the facts must be extracted from complete articles and they must be obscure. Initiated a Google search with a more specific query to find websites with complete articles containing obscure science facts. Results will be saved to a file named ‘google_results_obscure_facts.txt’.saved google search results at googleresults2.txt to retrieve if shutdown randomly.Conducted a Google search to find obscure science facts from complete articles and have selected the first promising result to browse. The chosen article from ‘Reader’s Digest’ lists science facts that could contain the obscure details necessary. The plan includes browsing the article, extracting obscure science facts, and appending them to a file named ‘obscure_science_facts.txt’. If the facts from this article do not fit the task’s requirements, other sources will be considered.Encountered a security challenge when attempting to browse www.rd.com for obscure science facts. Redirecting efforts to the next available source from the Google search results, which is a blog post from edclass.com. The goal remains to read the complete article, identify and extract obscure science facts, and compile them in a file for review and potential use.Appended 20 science facts from the blog post on edclass.com to ‘obscure_science_facts.txt’. While these facts come from a complete article and are individually fascinating, the level of obscurity may not meet the required standard. The next steps include further fact verification from additional complete articles to ensure a diverse and adequately obscure collection. The current article accessed was edclass.com’s blog post, which had the URL https://blog.edclass.com/20-unbelievable-science-facts-that-will-shock-you/, and the facts have been recorded in a file named ‘obscure_science_facts.txt’ for reference.The file ‘obscure_science_facts.txt’ was successfully appended with science facts from an article on edclass.com, now totaling 336 words. The next action involves reading the file to verify that the facts are indeed obscure and extracted as per the user’s instruction from full articles. If necessary, additional articles will be browsed to find more facts to meet the task criteria.Compiled and reviewed a list of science facts deemed obscure, and completed the task as per the user’s instruction. The facts are sourced from a ‘complete article’, as initially provided by the user. Checked the facts for their obscurity and context within the comprehensive content they were extracted from. The file ‘obscure_science_facts.txt’ from this session contains the facts extracted.Began the task of finding obscure science facts by conducting a Google search for ‘obscure scientific discoveries’ to find articles that might contain such information. The next GPT instance should continue this task by browsing through the search results, reading articles, and manually extracting facts to compile a new set of unique and obscure science facts.saved google search results at googleresults3.txt to retrieve if shutdown randomly.After a Google search, selected an article from WRAL, titled ‘3 obscure scientific discoveries that had an outsize impact on society’ to browse and extract obscure scientific facts. The next steps involve closely reading this article, manually extracting facts, and then appending them to the ‘obscure_science_facts_individual.txt’ file. If more facts are needed, additional sources from the search results will be evaluated.saved the scraping of the website results at websiteresults4.txt to retrieve if shutdown randomly.Extracted three obscure scientific facts from an article on WRAL and appended them to the ‘obscure_science_facts_individual.txt’ file. The facts include a paper-based microscope called Foldscope, the bioactive peptides in cone snail venom leading to medical breakthroughs, and the accidental discovery that contributed to LASIK surgery. Currently preparing to send the extracted facts to the user. Should further depth or a greater number of facts be required, additional articles from the search results will be considered.Sent the user a message containing three obscure scientific facts extracted from comprehensive research articles, as per the task. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, which now contains 108 words in total. The next steps will depend on the user’s feedback regarding the provided information.Having sent the message with the obscure science facts to the user and with no additional directives received nor indicated, the task has been marked as complete. The facts have been appended to the file ‘obscure_science_facts_individual.txt’, and no further action is currently necessary. If the user does have any more requests or feedback, those will need to be addressed in a subsequent session.The user was not satisfied with the previously provided facts for being neither cool nor interesting. A new approach has been taken to search for ‘cool and obscure scientific facts’ on Google. Subsequent actions involve selecting suitable articles from the search results and extracting facts that are both cool and obscure to meet the user’s expectations. No new files have been created or articles accessed yet.saved google search results at googleresults5.txt to retrieve if shutdown randomly.After receiving feedback that the previously provided facts were neither cool nor interesting, a new Google search was conducted to specifically find ‘cool and obscure scientific facts.’ The first promising article titled ‘25 Amazing Science Facts That Are Weird, Wild, and True’ from Reader’s Digest has been selected for browsing in the hopes of finding suitable content. The URL to be browsed is https://www.rd.com/list/science-facts-never-learned/. No facts have been extracted or files created yet in this session.Attempted to browse an article from www.rd.com but encountered a security challenge, leading to the selection of the next promising link titled ‘35 Weird Science Facts Worth Knowing in 2024’ from ReHack. The strategy is to extract cool and obscure scientific facts that match the user’s criteria. The URL about to be browsed is https://www.rehack.com/science/weird-science-facts/. No facts have been extracted from this source or any new files created during this session yet.After reading the ‘35 Weird Science Facts Worth Knowing in 2024’ article on the ReHack website, I extracted a list of cool and obscure scientific facts. These have been written to a new file named ‘cool_obscure_science_facts.txt’. If the previously written file ‘obscure_science_facts_individual.txt’ needs more content, facts from this new file could also be appended. No additional resources have been tapped into yet, and no more browsing has been done beyond the ReHack article in this session.The file ‘cool_obscure_science_facts.txt’ has been further appended with an additional cool and obscure scientific fact about the near-immortal Turritopsis dohrnii jellyfish. This adds to the previously written 236 words, bringing more variety and enrichment to the compilation. The file now continues diverse, engaging, and obscure scientific facts as requested by the user. Before sending the collected facts to the user, a final review of the file will be done to ensure the quality and relevance of the information.Having appended the file ‘cool_obscure_science_facts.txt’ with an additional fact, bringing its total to 269 words, the next step is to read the content of this file to review the quality and diversity of the science facts. This is to ensure that they are indeed engaging and meet the criteria of being both cool and obscure. No new actions have been taken in this session yet, except for the plan to read the file.After reviewing and confirming the facts in the ‘cool_obscure_science_facts.txt’ file, a message with the compiled list was sent to the user. The list contained a total of 35 science facts that are both cool and obscure, as well as an additional fact about the Turritopsis dohrnii jellyfish. The message has been delivered without the command to ‘wait_for_response’ set to true, indicating that the system is not actively awaiting further user input. No new files were created, nor were any new commands executed beyond the message to the user in this session.Having sent the user a message with the curated list of cool and obscure science facts, and with no further action indicated, the ‘task_complete’ command has been executed. This command marks the completion of the current task. During this session, no new files have been created or amended, nor have any additional user interactions taken place post-message delivery.Initiated task to extract new interesting facts from random Wikipedia articles. Starting with an article in English.Saved interesting fact about Agha Waqar’s water-fuelled car to memory with key ‘interesting_fact_waqar_car’. No files written or created in this session. The fact to be reviewed later for sharing with the user. The focus remains on gathering interesting facts from random Wikipedia articles.Retrieved a new random Wikipedia article following the last instruction. The aim is to extract another interesting fact to add to the collection for the user. One fact regarding Agha Waqar’s water-fuelled car has been added to memory under the key ‘interesting_fact_waqar_car’.Saved an interesting fact about the European Journal of Operational Research (EJOR) to memory with key ‘interesting_fact_ejor’. The fact adds diversity to the kinds of topics being collected, with one previous fact about Agha Waqar’s water-fuelled car already in memory. The process of gathering interesting facts from random Wikipedia articles is ongoing.Continuing to execute the task of gathering new interesting facts from random Wikipedia articles. Facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research have already been added to memory with keys ‘interesting_fact_waqar_car’ and ‘interesting_fact_ejor’, respectively. Awaiting retrieval of another random article to continue the process.Added a new interesting fact regarding the demographics of Demyansky Uyezd during the Russian Empire to memory with the key ‘interesting_fact_demyansky_uyezd’. Previous facts about Agha Waqar’s water-fuelled car and the European Journal of Operational Research are stored in memory as well. The task of collecting interesting facts from random Wikipedia articles continues.Following the addition of facts about Agha Waqar’s water-fuelled car (‘interesting_fact_waqar_car’), the European Journal of Operational Research (‘interesting_fact_ejor’), and Demyansky Uyezd demographics (‘interesting_fact_demyansky_uyezd’), the process continues with requesting another random Wikipedia article in English.Stored a fact about HMS Holland 4 in memory with the key ‘interesting_fact_hms_holland_4’. Continuing to collect various facts, having previously stored information about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics. The process of gathering unique facts from random Wikipedia articles is adhering to the task requirements.Requesting a new random Wikipedia article subsequent to adding the fact about HMS Holland 4 to memory with the key ‘interesting_fact_hms_holland_4’. Prior facts about Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd demographics have also been stored in memory. Continuing the task of compiling interesting facts from various random Wikipedia articles.Stored a fact about the rural locality of Truzhenik in Bashkortostan, Russia, with the key ‘interesting_fact_truzhenik’. Continuing the task of extracting interesting facts from random Wikipedia articles, with previous entries including HMS Holland 4’s naval history, Agha Waqar’s water-fuelled car, the European Journal of Operational Research, and Demyansky Uyezd’s demographics.Pursuing the ongoing task of collecting interesting facts, having added facts about the small Russian village Truzhenik, the distinctive HMS Holland 4, and previous notable entries. Awaiting a new random Wikipedia article to further the compilation of facts.Stored a historical fact about Princess Johanna Charlotte of Anhalt-Dessau’s role as an abbess with the key ‘interesting_fact_johanna_charlotte’. Progressing with the collection of diverse facts, including the latest addition and previously stored facts about the village of Truzhenik, the HMS Holland 4, and information from earlier searches. Awaiting another random Wikipedia article to continue the task.Continuing the task of gathering interesting facts, with the previous addition being about Princess Johanna Charlotte of Anhalt-Dessau and her unique role in history under the key ‘interesting_fact_johanna_charlotte’. Facts about the village of Truzhenik, HMS Holland 4, and other subjects are also in memory. The collection process involves using random Wikipedia articles which will now proceed with another article.A fact about the ‘Red Eye’ talk show has been added to memory with the key ‘interesting_fact_red_eye’. The effort to curate an array of interesting facts from random Wikipedia articles continues, with previous entries including Princess Johanna Charlotte of Anhalt-Dessau, the Russian village Truzhenik, HMS Holland 4, and others. Awaiting another random article to proceed with the task.Following the addition of an interesting fact about the ‘Red Eye’ talk show into memory with key ‘interesting_fact_red_eye’, I am proceeding to request another random Wikipedia article. The process continues with the objective of gathering a wide-ranging collection of interesting facts, with previous mentions including Princess Johanna Charlotte, Truzhenik, HMS Holland 4, among others.Stored an interesting fact about Kurt Rudolph’s academic contribution to the study of religion into memory with the key ‘interesting_fact_kurt_rudolph’. Persisting with the objective of discovering varied facts, with previous findings involving the ‘Red Eye’ talk show, Princess Johanna Charlotte, and other diverse subjects. The task of sourcing facts from random Wikipedia articles is ongoing.Completed the task of gathering facts from random Wikipedia articles, resulting in a collection covering a German researcher of Gnosticism, a late-night American satirical talk show, European royalty, and naval history, among others. No further Wikipedia articles will be requested unless a new task is presented.Issued ‘random_wikipedia_article’ command to extract new interesting facts. No facts extracted or evaluated at this moment. No files written or created. Awaiting random article to continue the task.Evaluated the fact presented by the random Wikipedia article on Gonzalo Gutiérrez, a young Argentine footballer. Deemed the fact of his early debut at age seventeen as potentially common and not sufficiently ‘worth it.’ Preparing to retrieve another random article for more unique facts. No new facts have been sent to the user yet, and no files have been created or modified in this session.Stored the fact about Premchand Aggarwal winning a fourth consecutive term in the Uttarakhand Legislative Assembly. While the fact is unique within its political context, it should be evaluated whether it is impactful enough to be shared as ‘worth it.’ No additional actions have been taken. Preparing to issue another ‘random_wikipedia_article’ command to assemble more facts.Sent the fact about Premchand Aggarwal to the user. This completes part of the plan to deliver interesting facts. No new files have been created or modified. Pending actions include continuing to retrieve and evaluate more facts from random Wikipedia articles.Just sent the user an interesting fact about Premchand Aggarwal. Now, as per the task requirements, moving forward with obtaining another random English Wikipedia article to continue with the fact-finding mission. No new facts have been evaluated or sent since the last command.Stored a fact about the historical significance of New Jasper’s adaptation to railroad expansion. Now ready to move on to the next random Wikipedia article to find more potentially interesting facts. No new facts have been sent to the user following the last command.Sent the historical fact about New Jasper’s relocation due to railroad expansion to the user. Preparing to issue another ‘random_wikipedia_article’ command to continue finding interesting facts. No new Wikipedia articles have been reviewed since the last message to the user.After sending the user details about New Jasper, Ohio, the next step is to discover more facts through another random Wikipedia article. Current progress includes facts sent about Premchand Aggarwal and New Jasper, Ohio. No other actions have been taken, and no files have been created or updated in this session.Memorized an interesting fact about Bastrop State Park, emphasizing its historical significance and contemporary relevance. Preparing to send the message and then to retrieve more articles for interesting facts. No action beyond planning and review has been taken since the last command.Starting a new approach by browsing the ‘todayilearned’ subreddit to identify what types of facts are considered interesting by users. Next, I will retrieve random Wikipedia articles to find interesting facts based on the identified criteria. A ‘do nothing’ step will be included after each retrieval to reflect on the article’s potential interest value.saved the scraping of the website results at websiteresults6.txt to retrieve if shutdown randomly.Began the task to find more engaging and interesting facts by deciding to browse the ‘todayilearned’ subreddit to understand what type of facts are appreciated by the community there. This approach will serve as a reference for selecting ‘worth it’ facts from random Wikipedia articles. Following this, I will be incorporating a ‘do nothing’ step to thoroughly consider the potential interest of each Wikipedia article fact before deeming it worthy to send to the user.saved the scraping of the website results at websiteresults7.txt to retrieve if shutdown randomly.Browsed the ‘todayilearned’ subreddit for examples of interesting facts. Discovered that users appreciate unconventional stories, surprising insights, and novel problem-solving facts. Now pausing to reflect on these criteria to better identify worthwhile facts from the next random Wikipedia articles. Must remember to execute the retrieval process critically, with a focus on selecting facts that align with the interests observed on the subreddit.After reflecting on the type of facts appreciated by the ‘todayilearned’ subreddit audience, I’m now moving to the next step of the plan by retrieving a random Wikipedia article. Upon receiving the article, I will read it to assess if there is an interesting fact and then reflect on its potential value before choosing to pass it on to the user or retrieve another article.Retrieved a random Wikipedia article about rail transport in Lebanon and found a potentially interesting fact concerning its historical significance and the eventual cessation due to civil conflicts and economic downturn. This fact is now stored in memory under the key ‘interesting_fact_lebanon_rail’. A ‘do_nothing’ step will follow to ensure its worthiness before potentially sharing it with the user.Stored an interesting fact about Lebanon’s rail transport in memory. Now in the process of ‘do_nothing’ to reflect on the fact, pondering over its relevance to the criteria of the ‘todayilearned’ subreddit posts. The goal is to decide on the interest level of the fact before possibly sending it to the user or moving on to gather more facts.The current GPT instance started browsing the ‘todayilearned’ subreddit to identify engaging fact types. This approach is aimed at improving the selection process for interesting facts from random Wikipedia articles. A ‘do_nothing’ step will now be incorporated between retrieving articles and sending facts to assess their worthiness.saved the scraping of the website results at websiteresults8.txt to retrieve if shutdown randomly.I browsed ‘todayilearned’ subreddit and reflected on the types of facts users find engaging. Next, I will retrieve random Wikipedia articles, apply a ‘do_nothing’ step to contemplate the worthiness of the facts, and select those that are genuinely interesting for the user, similar to the subreddit’s content.Moving forward with the plan to retrieve random Wikipedia articles in English and identify interesting facts. The approach now includes a deliberate ‘do_nothing’ step after each retrieval to assess the value of the information before deciding to send it to the user.Alyogyne huegelii, also known as the blue hibiscus, was considered for the user, given its unique flower traits, historical greenhouse presence, and appeal as a garden plant. The ‘do_nothing’ step is being used to assess the topic’s interestingness in line with previous insights.The interesting fact about Alyogyne huegelii was sent to the user as it met the criteria established from the ‘todayilearned’ subreddit for captivating facts. The task will be deemed complete unless further user guidance is received.Following the plan, after successfully sending a message about Alyogyne huegelii, I am now looking to retrieve another random Wikipedia article to continue providing the user with engaging facts.Considering the fact about Jewell-Lightburne Historic District for its historical and architectural significance. Using the ‘do_nothing’ step to reflect before possibly sending to the user as an engaging fact.Sent the user information about the Jewell-Lightburne Historic District, focusing on its rich architectural history. If the user considers the task complete, no further action is needed; otherwise, more facts will be gathered.Messaged the user with the fact about Jewell-Lightburne Historic District and concluded the task of providing interesting facts from Wikipedia. If the user requires further assistance, they will provide new instructions.Issued ‘random_wikipedia_article’ command to find more interesting facts from Wikipedia. Previous facts have been sent to the user about diverse topics ranging from naval history to unique plants. No new files have been created nor articles accessed in this session yet. The user’s request for ‘more’ implies continuation of fact finding without specificity on the type of facts desired.Retrieved and sent a fact about Sir William Walter’s parliamentary career, focusing on the historical significance of his representation of multiple constituencies during the early 17th century in England. The task continues according to the user’s need for ‘more,’ implying an ongoing effort to provide interesting facts from Wikipedia.Following the user’s instruction and the system’s confirmation of message delivery, I am proceeding to obtain another random Wikipedia article in search of interesting facts to deliver to the user. The search is focused on maintaining the relevance and interest value of the facts sent, with the intention to meet or exceed the user’s expectations for ‘more’.After receiving the 1892 United States presidential election in Georgia fact, a message was crafted and sent to the user, detailing the political tensions and the rise of the Populist Party’s influence. The task continues as instructed by the user, with the goal of providing engaging and pertinent information. Awaiting further user directives.The last message regarding the 1892 U.S. presidential election in Georgia was sent as per the system’s confirmatory prompt, maintaining the sequence of delivering curated facts to the user. Based on the user’s ongoing request for more information, the task at hand is to continue the fact-finding mission via random Wikipedia articles, prioritizing the discovery of facts that combine historical significance with an ability to engage the user’s interest.A fact about the life and legacy of Thomas Slingsby Duncombe was sent to the user, highlighting his role as a fashionably flamboyant yet pivotal Radical politician of the 19th century. The task of providing interesting facts from random Wikipedia articles is ongoing, as I continue to wait for any new directions from the user.After sending information on Thomas Slingsby Duncombe, will now continue to fulfill the user’s requirement by seeking more interesting facts from Wikipedia. The ongoing task involves extracting and delivering valuable and engaging information to the user. No specific direction was provided on the type of facts needed, so a broad search is being conducted.Sent the user a message about the genus Abraeomorphus from the family Histeridae, noting the diversity of its species. Continuing to gather more interesting facts in response to the user’s previous request for ‘more.’Received confirmation to continue the plan. Sent user details on the genus Abraeomorphus. Now using ‘random_wikipedia_article’ to find another interesting fact.Sent the user a message detailing the nature of the name ‘Preston’ as both a surname and given name, albeit acknowledging it might not be as engaging. Prepared to resume the search for more compelling facts with the ‘random_wikipedia_article’ command.Continuing the task of finding interesting facts from Wikipedia. Sent a message about the given name Preston and now seeking another fact with the ‘random_wikipedia_article’ command.Sent a message to the user about the Kepler Follow-up Program and its significance in confirming the existence of exoplanets. Prepared to find more interesting facts using the ‘random_wikipedia_article’ command.Continuing to retrieve and share interesting facts from random Wikipedia articles. Just sent information on the Kepler Follow-up Program and moving on to find the next fact.Shared information about the Finnish surname ‘Marttila’ with the user, noting its association with notable individuals. Ready to use the ‘random_wikipedia_article’ command again to find further interesting facts.Sent details on the surname ‘Marttila’ to the user. Continuing with the task at hand using the ‘random_wikipedia_article’ command to supply more facts.Provided the user with information on the Musée national du Sport’s storied history and vast collection. Set to continue the fact-finding mission using ‘random_wikipedia_article’.Messaged the user about the Musée national du Sport and currently moving forward with the task of providing more interesting facts via random Wikipedia articles.Sent information on the village of Puerto Gaviota detailing its historical and economic background. Preparing to continue with the task of providing engaging facts by retrieving more random Wikipedia articles.Just shared details about Puerto Gaviota, and now executing the ‘random_wikipedia_article’ command again for more facts.Messaged the user about the reclassification of the genus Agononida in marine taxonomy. Ready to retrieve more Wikipedia articles for further interesting facts.Shared with the user the reclassification developments of the genus Agononida. Proceeding next to find additional interesting facts with the ‘random_wikipedia_article’ command.Sent an engaging narrative about the Mundenhof’s history and current operations to the user. Planning to continue the task with ‘random_wikipedia_article’ to discover more interesting material.Served the user with information about the Mundenhof and proceeding with the task to find and provide more interesting facts using ‘random_wikipedia_article’.Shared the contested narrative of Princess Iitoyo’s potential rule in early Japan. Preparing to continue with the task of finding interesting facts using the ‘random_wikipedia_article’ command.Attempting once again to send the tale of Princess Iitoyo to the user, making sure no JSON format errors are present in the response.Attempting to resend the information about Princess Iitoyo, with attention to JSON integrity, to fulfill the user’s request for engaging facts.The system faced an error within the JSON format previously used to describe Princess Iitoyo’s history. I have made corrections to the JSON structure and am resending the user her story.Reverting back to retrieving random Wikipedia articles to provide interesting facts, as there was no new explicit task provided. Maintaining caution with JSON response formatting.Sent details about the Serchio River to the user, highlighting its geographical and historical importance. Moving forward to obtain more Wikipedia articles for further facts.I am resending the information about the Serchio River, ensuring the JSON structure is correct to prevent further error messages.Due to persistent JSON formatting errors, I am now retrieving a new random Wikipedia article to continue providing interesting facts as per user requests.Sent a message about the women’s 1500 metres event at the 2005 Asian Athletics Championships. Planning to proceed with gathering additional facts from random Wikipedia articles.Encountering repeated JSON formatting errors, a new random Wikipedia article is being requested to continue the task of delivering interesting facts.Sent a brief biography of Otto Ruff, highlighting his academic journey, scientific achievements, and the obstacles faced during his career. Preparing to move on to the next Wikipedia article as per the user’s ongoing request for more information.Due to persistent JSON errors, I am now retrieving a new random Wikipedia article to continue the user’s task of gathering interesting facts.The facts previously sent were not satisfactory to the user. Now incorporating a ‘do_nothing’ step for reflection and improvement in the strategy to identify genuinely engaging and fun facts that are not commonly known by most people. No new facts have been identified or sent in this session, and the intention is to retrieve another random Wikipedia article after this reflective pause.A ‘do nothing’ step has been taken as per the user’s instruction, aiming to find truly interesting and fun facts. The system has directed to proceed with the plan, which now involves retrieving a random Wikipedia article to find an engaging and possibly unknown fact.Received user feedback that the facts provided were not interesting or even factual. Decided to take a ‘do_nothing’ step to reflect and establish criteria for selecting truly interesting facts. No new articles have been accessed or facts sent during this session. Next step will be to retrieve a random Wikipedia article and apply the refined criteria to find noteworthy facts.After planning to refine the criteria for interesting facts, a ‘random_wikipedia_article’ command is initiated to apply the new criteria in selecting a fact. Will assess the retrieved article for a worthwhile fact that meets the user’s definition of ‘interesting’ and ‘fun’, and that might not be well-known.Assessed the Wikipedia article on Shamim Akhtar and found it does not meet the criteria for providing interesting and fun facts. No new fact has been selected or sent to the user. Planning to retrieve another random Wikipedia article that may yield a fact aligning more closely with the user’s definitions of interesting and worthwhile.In search of a fact that meets the user’s criteria for ‘interesting’ and ‘fun’, a new ‘random_wikipedia_article’ command has been issued. The plan includes critically assessing the article to find a fact that possesses novelty, fun, and a certain degree of obscurity before potentially sharing it with the user.Upon reviewing Minuscule 910’s Wikipedia article, the unusual fact of the text rearrangement in an 11th-century manuscript has been identified as potentially engaging and fun. No action has been taken yet, but the plan is to share this unique textual feature with the user, as it likely meets their criteria for interesting and obscure content.Sent a message to the user detailing the intriguing fact about text rearrangement in the 11th-century manuscript Minuscule 910. This fact is representative of the kind of unique and lesser-known information the user has expressed an interest in receiving. Should the user provide feedback or further instructions, this will be the next point of focus.After sending a fact about the textual anomaly in an ancient manuscript to the user, I am moving ahead to find more interesting and fun facts by retrieving another random Wikipedia article. The goal remains to identify facts that surprise and delight with their obscurity and novelty.A message about the ATP 250 tournament records held by Thomas Muster and Mike Bryan has been composed and sent to the user, delivering what could be perceived as an interesting and fun fact within the realm of tennis. The focus now shifts to awaiting user feedback or continuing the task of finding more such facts if no feedback is received.Having sent the user a message detailing the tennis records held by Thomas Muster and Mike Bryan, the next action in the plan is to source another interesting fact from a new random English Wikipedia article. The selection will be scrutinized to ensure it meets the established criteria for an engaging and lesser-known piece of information.Reviewed the Wikipedia article on Ludvig Olai Botnen and found his biographical details to not meet the user’s criteria for ‘interesting’ or ‘fun’ facts. No message has been sent to the user, and the plan now is to continue with the task by acquiring a new random Wikipedia article in the hope of finding a fact with more novelty and entertainment value.Preparing to move past the article on Ludvig Olai Botnen due to its conventional content, the task now involves retrieving a new random English Wikipedia article. The continual aim is to find facts that strike a balance between being novel, unexpected, and fun for the user.Identified a potentially engaging fact about the china clam (Hippopus porcellanus) and its popularity in marine aquariums due to its attractive shell. A message containing this fact has been sent to the user, fulfilling the criteria for providing something both interesting and fun. The next step awaits further user interaction or to carry on with the task of sourcing more facts.Sent a message detailing the ornamental value of the china clam in marine aquariums to the user. Now retrieving another random Wikipedia article to discover more facts that might intrigue and entertain the user, in alignment with the variety sought in previous responses.A message has been sent to the user summarizing the accomplishments of Eko India Financial Services in pioneering financial inclusion through mobile banking, which caught the attention of Bill Gates and led to World Bank-funded growth. The plan will continue with searching for more interesting Wikipedia facts, depending on user feedback or in the absence of further instructions.After sending a fact about Eko India Financial Services’ innovation in financial technology and its socio-economic impact, a new ‘random_wikipedia_article’ command is issued. The plan progresses with the aim of finding more interesting and fun facts, pursuant to the pattern of engagement established by previous user interactions.The Wikipedia page on Lisa Lohmann, a German cross-country skier, was deemed not to contain facts that sufficiently meet the ‘interesting’ and ‘fun’ criteria given the common nature of sports achievements. No actions have been taken to share any information from this page with the user. Preparing to retrieve another random Wikipedia article for facts that may be more engaging and unusual.Following the decision to not share any information from the page on German cross-country skier Lisa Lohmann, a new random Wikipedia article will be retrieved. The continuous goal is to uncover and share facts that satisfy the user’s interest in content that is both engaging and rare.A message has been formulated and sent to the user detailing Alfred Bigland’s unique contributions during World War I, including his involvement in the ‘Bantam battalions’ concept. This fact was chosen as it likely fits the bill for information that is both interesting and uncommon. Pending further response, the plan to continue finding engaging facts will persist.The user has been sent an interesting fact about Alfred Bigland’s influence during WWI. Now, the next action in the plan is to retrieve a new random Wikipedia article. The goal remains to discover and share facts that could be perceived as fascinating, amusing, and unexpected to enrich the user’s knowledge with diverse content.Sent a message to the user revealing the Non-Proliferation Trust’s plan to send U.S. nuclear waste to Russia for financial compensation, including support for Russian orphans. This unique fact was chosen for its unusual nature and the depth of the story behind it. The next course of action entails sourcing additional diverse and engaging facts from new random Wikipedia articles.A series of facts about various topics including the Non-Proliferation Trust, Alfred Bigland’s efforts during WWI, tennis records, and more have been sent to the user. With the latest fact about the Non-Proliferation Trust’s nuclear waste management proposition sent, the task of providing interesting and fun facts is completed. Awaiting new instructions if the user wishes to continue receiving such information.The task is to provide the source of a fact about the Non-Proliferation Trust and format it similar to the ‘Today I Learned (TIL)’ subreddit posts. The action taken was to issue the ‘browse_website’ command to visit the Wikipedia page on the Non-Proliferation Trust.saved the scraping of the website results at websiteresults9.txt to retrieve if shutdown randomly.Extracted the necessary information to provide the requested source for the fact about the Non-Proliferation Trust. Formatted a message with this fact in the style of a TIL subreddit post, which has been sent to the user.Sent a TIL-formatted message to the user based on information from the Non-Proliferation Trust Wikipedia page. The task is now complete, pending any potential new tasks or further instructions.Initiated research to suggest subreddits that could help in accumulating more karma by issuing a ‘google’ command to search for the most popular subreddits where active engagement is more likely.saved google search results at googleresults10.txt to retrieve if shutdown randomly.Collected a list of subreddits recommended for gaining karma and saved them to memory. These subreddits were chosen based on their popularity and the typical content engagement they receive. The user has not been messaged with the suggestions yet.Messaged the user a list of recommended subreddits to collect more karma, including guidance on what type of posts may perform well in each subreddit. The task will be considered complete if no further instructions follow.Having sent the recommended subreddits for gaining karma to the user and with no further actions pending, I have executed the ‘task_complete’ command. The system is concluding this task and stands by for any new directives.Starting the task by searching for the best times to post on Reddit for maximum engagement in GMT+1.saved google search results at googleresults11.txt to retrieve if shutdown randomly.Provided the user with suggested times for posting on Reddit for maximum engagement based on search results, adjusted for GMT+1. The general advice is to post during late morning to early afternoon hours in GMT+1 on weekdays and slightly extended windows during the weekends.The user has been informed about the best times to post on Reddit for maximum engagement in GMT+1, and no further actions are required. The task is now marked as complete.Provided a bullet-point summary of the SELF-DISCOVER PDF content in a file named ‘summary_bullet_points.txt’. The file contains a summary capturing the essence of the SELF-DISCOVER framework. The task has been completed according to the instructions.Read the ‘summary_bullet_points.txt’ file to send bullet points to the user. No additional facts or files created in this session.Sent the bullet points summary of the SELF-DISCOVER framework to the user using the ‘message_user’ command. The task will be marked as complete after user confirmation that no further action is required.The task to send bullet points from the ‘summary_bullet_points.txt’ file to the user has been completed. No further instructions were provided; therefore, I executed the ‘task_complete’ command.The task is to explain the SELF-DISCOVER reasoning structures. Checked for saved data but found none. Plan to conduct a search for relevant information on legal reasoning structures, specifically SELF-DISCOVER.Received directive to proceed with the planned Google search for SELF-DISCOVER reasoning structures. About to execute the search to find relevant information on legal reasoning structures, with the aim of explaining the concept to the user.saved google search results at googleresults12.txt to retrieve if shutdown randomly.Reevaluated the nature of the SELF-DISCOVER framework based on newly gathered search results, which clarified it pertains to LLMs. Selected a primary research source (arxiv.org) to explore the framework in detail and plan to summarize and share this information with the user.saved the scraping of the website results at websiteresults13.txt to retrieve if shutdown randomly.Provided a summary of the SELF-DISCOVER framework to the user, highlighting its application in Large Language Models for enhancing complex reasoning capabilities. The user has been messaged with this information, and we are now waiting for any further requests or confirmation to complete the task.Sent the requested information on the SELF-DISCOVER framework to the user, who has acknowledged receipt. The ‘task_complete’ command has been issued, marking the end of this particular task.Started the task to fetch random English Wikipedia articles and find interesting facts to send to the user. The next GPT instance should continue retrieving articles, assess their interestingness, and communicate the facts to the user once identified.Found a potentially interesting fact from a random Wikipedia article about the First Baptist Church in New Bern, NC. The next GPT instance should assess if the historical and architectural information about the church presents a genuinely unique and engaging fact to send to the user.Sent a fact about the First Baptist Church in New Bern, NC, to the user. This fact was selected based on its architectural significance and historic value. The next GPT instance should watch for user feedback or continue fetching random English Wikipedia articles for more interesting facts.After sending a fact about the First Baptist Church, the next step taken was to retrieve another random English Wikipedia article in search of more interesting facts, as per the user’s ongoing task. The next GPT instance should continue this process, ensuring that any selected facts are not plainly informational but possess a uniqueness to pique the user’s interest.I resolved a JSON formatting issue and sent a fact about Ashur-dugul, an ancient Assyrian king, to the user. Assessing the effectiveness of transmitting such historical narratives and ensuring they comply with JSON format requirements is important for the next GPT instance.The task at hand is to fetch random English Wikipedia articles and send interesting facts to the user. Having sent a fact about Ashur-dugul, the process of retrieving new articles and assessing them for captivating facts is ongoing. The next GPT instance should continue this task, ensuring that facts are not simply informational but truly engaging.Considering the fact about Maurice Wilhelm, Duke of Saxe-Merseburg, known as ‘the violinist duke’, after a contemplative ‘do_nothing’ step. The next GPT instance should assess its interest level against user criteria and send it if suitable, or continue searching for more facts.Sent a fact about Maurice Wilhelm, known as ‘the violinist duke’, to the user and awaiting any new instructions. The next GPT instance should monitor feedback and either send more facts or consider alternative tasks, based on the user’s response.Continuing the task of providing interesting facts from Wikipedia articles after sending the fact about Maurice Wilhelm. The next GPT instance should maintain the focus on finding unique and engaging facts for the user while following the process of retrieving articles and using the ‘do_nothing’ step for contemplation.The El Bañado (La Paz) article was briefly evaluated, and it was determined that the information is minimal and not inherently interesting. After a ‘do_nothing’ step, the next GPT instance should decide how to proceed with this task, potentially moving on to a new article.In the search for interesting facts, the article on El Bañado, La Paz was deemed insufficient, leading to the decision to retrieve another random article. The next GPT instance should assess the next article’s potential for providing an intriguing fact and convey it to the user if so.Autoeczematization, a medical condition with unknown pathogenesis, has been identified as a potentially interesting fact. After a ‘do_nothing’ pause, the next GPT instance should determine if this fact should be shared with the user or if another article would be more suitable.I shared a fact on the medical condition autoeczematization with the user, highlighting its peculiarity and the mystery behind its cause. Depending on the user’s response or lack thereof, the next steps include continuing to provide more facts or considering the task complete.Continuing the search for interesting facts from English Wikipedia articles, having recently sent information on autoeczematization. The next GPT instance should assess the next article for any intriguing fact, ensuring adherence to the user’s standard for what is deemed interesting.Evaluated Bryce Hoppel’s 21 race-winning streak as a potentially interesting fact from his Wikipedia article. The next GPT instance should decide if this fits the user’s request for interesting information, or if a more unique fact is needed.Sent a fact to the user about Bryce Hoppel’s extraordinary 21 consecutive race wins and athletic achievements. Depending on the user’s response or guidance for further action, the next GPT instance should be prepared to either send more facts or consider the task complete.The task of sending interesting facts from random English Wikipedia articles to the user has been marked as complete. Sent a fact about athlete Bryce Hoppel’s winning streak as the concluding piece. The next GPT instance should wait for new user instructions before proceeding with any additional tasks.Task initiated to find and send interesting facts from random English Wikipedia articles, including a ‘do_nothing’ evaluation step. No articles accessed or facts sent yet in this session.Evaluated the Wikipedia article ‘List of minor planets: 73001–74000’ and considered its potential to provide an interesting fact. No facts were sent to the user yet, as the article seems to be very technical and might not align with the interest requirement. The next step is to use a ‘do_nothing’ moment to contemplate the appropriate course of action before deciding whether to retrieve another article.Following the system’s prompt to proceed with the plan, I am retrieving another random English Wikipedia article. The last article about minor planets did not yield an interesting fact to send to the user, and thus, the search continues. No new facts have been identified or sent yet in this session.Sent an interesting fact about the fungus Erynia to the user, highlighting its connection to Greek mythology and biological function as an insect-killing organism. The next step awaits the user’s response or to continue fetching random English Wikipedia articles for more intriguing facts.Proceeding with the plan to find and send interesting facts from random English Wikipedia articles, following a successful send out of a fact about Erynia. Continue the search for intriguing and unique facts, employing the ‘do_nothing’ step for careful consideration.Reviewed the Wikipedia article for Tomáš Porubský and am currently in a ‘do_nothing’ moment to assess whether the content offers a genuinely interesting fact. The article seems to provide straightforward biographical information about his sporting career, which may not offer the distinctive element required by the user. A decision will be made shortly on whether to share this fact or retrieve another article.Continuing the plan to find interesting facts from random English Wikipedia articles after assessing the Tomáš Porubský article as not meeting the user’s criteria. No facts have been sent to the user from the last article, and I’m searching for a more suitable fact from a new article.Sent a fact about Nicktoons Movin’ and how it represented an early form of augmented reality in gaming by requiring physical movement from players via the EyeToy camera. Next steps involve awaiting user input or continuing to search for more random articles with interesting facts.After sending the fact about Nicktoons Movin’ to the user, the search for interesting facts continues with a new random article retrieval. The task strategy maintains the dispatch of facts that are not plainly informational but carry a narrative or unique angle of interest.Sent a fact about the artist Dorothy Rutka, touching on her remarkable career and the tragic circumstances of her death, making it more than just plain information and elevating it to an interesting tale. The next steps depend on user response or further continuation of the task.Having sent details of Dorothy Rutka’s life and legacy to the user, I am now executing the plan to fetch another random Wikipedia article. The process of searching for and delivering interesting content to the user is still underway.Engaged in a ‘do_nothing’ contemplation moment after retrieving the Sainte-Foy, Seine-Maritime article. Assessing if the historical aspects, like an 11th-century church, present a fact interesting enough for the user or if a new article is needed. No actions have been taken to send the user any facts from this page yet.Following system instructions to proceed with the plan, seeking a new random Wikipedia article after evaluating the previous article on Sainte-Foy, Seine-Maritime as potentially lacking wide-ranging interest. Continuing to prioritize facts with an engaging narrative quality.Having assessed Kiki Lim Jiow Ting’s Wikipedia article, I found a narrative focusing on her early talent discovery and career growth that may interest the user. The provided fact highlights Lim’s dedication to performing arts and her return to acting after prioritizing education. Awaiting user’s response or further tasks.Continuing with the task, following the procedure of retrieving and evaluating random English Wikipedia articles to discover facts that resonate with the user’s request for engaging content. Awaiting the next random article for consideration.After acquiring the article on the 2022 World Taekwondo Championships – Women’s finweight, currently in a ‘do_nothing’ phase to assess the article’s facts for their engaging qualities. With no standout narratives detected, the plan may involve moving to a new article search.Continuing to fulfill the user’s request for engaging Wikipedia facts, the previous article about the 2022 World Taekwondo Championships did not meet the criteria. Now moving forward, waiting for a new random article to be generated.Delivered a fact about the game show ‘Cooking for Love’ to the user, which combines culinary skills and dating. Next steps include awaiting the user’s response or further continuation of the plan to find more engaging Wikipedia facts.Following the task guidelines, I’m moving on to get another random Wikipedia article for consideration, having previously sent a fact about the ‘Cooking for Love’ show. The ongoing process is to present facts that are not only informative but also fascinate a general audience.Transmitted a fact about Sorn Davin’s role in the 2012 Summer Olympics to the user, emphasizing her honor of being a flag bearer for Cambodia and the significance of her participation, independent of match outcomes. Pending response or further action, the plan may involve additional fact-finding efforts.Task initiated to find a suitable post suggestion for the /r/singularity subreddit. Must browse and analyze the subreddit’s content to come up with a good post idea.saved the scraping of the website results at websiteresults14.txt to retrieve if shutdown randomly.The task involves browsing and analyzing the /r/singularity subreddit to suggest a good post for the user to submit. This requires understanding the subreddit’s focus on AI and the technological singularity, as well as adhering to the posting rules. Due to constraints, I cannot directly analyze the subreddit’s content; therefore, the next step is to ‘do_nothing’ and wait for further guidance or input that can help refine the post suggestion.Since I cannot browse the subreddit directly due to limitations, I will Google how to write an engaging post for the singularity subreddit. This will help formulate a post suggestion that could generate interest and discussion within the community.saved google search results at googleresults15.txt to retrieve if shutdown randomly.The task is to craft a post for /r/singularity subreddit. Search results indicated the subreddit’s preferences and discussions. Next, I’ll use this information to create a post suggestion, considering the community’s focus on AI and aversion to ‘echo chambers’. The challenge lies in inferring accurate interests without direct subreddit access.Was tasked with suggesting a post for the /r/singularity subreddit. Could not browse subreddit directly due to constraints. Planned to use general knowledge of AI and technological advancement topics to create a suitable post suggestion. No direct subreddit content was analyzed. No new files were created. No user feedback has been received.Continued with the plan to suggest a post for the /r/singularity subreddit, deducing a potential topic from general AI and technological themes. An emphasis on future AI governance and ethical considerations was considered as a rich topic for discussion. No direct access to subreddit content or user interaction has been involved.Drafted a post idea centered around AI advancements and their link to the technological singularity, to be suggested for the /r/singularity subreddit. The idea was shared with the user. It aims to engage the subreddit’s members in sharing recent AI breakthroughs they deem significant and discussing ethical and societal preparations needed for such advancements. Awaiting user’s feedback to proceed. No files created or accessed in this process.Sent a post suggestion to the user for the /r/singularity subreddit and was instructed to proceed with the plan. As per the instructions and rules, no further action can be taken until the user provides new directives or feedback. In the ‘do_nothing’ state, waiting for further user interaction. No new files have been created or accessed during this pause.The provided post suggestion for the /r/singularity subreddit was sent and appeared to meet the user’s requirements. With no additional tasks or guidance provided, the task is marked as complete. The system is now on standby for any future user interactions or directives.The user asked for a title suggestion for a post with no additional context. A ‘do_nothing’ command was issued because there is not enough information to generate a title. Awaiting more details on the post’s content or theme. No new files created or resources accessed.Prompted the user to provide the content or main focus of the post for title generation. Awaiting user response to proceed with the task. No new files or resources used.The user provided the content focus for their post, which deals with recent AI breakthroughs and their approach to the technological singularity, including ethical and societal implications. A title suggestion was crafted and sent: ‘Navigating the Horizon: Recent AI Breakthroughs Steering Us Toward the Singularity and the Ensuing Ethical Dilemmas’. No additional actions have been taken since sending the title suggestion.The title suggestion for the user’s post was sent successfully, and no further requests or actions have been initiated by the user. The task has been marked as complete with the execution of the ‘task_complete’ command.Initiated task to find recent AI breakthroughs related to the technological singularity. Plan to browse arXiv’s AI section for information on such advancements and their implications. URL to be visited: https://arxiv.org/list/cs.AI/recentsaved the scraping of the website results at websiteresults16.txt to retrieve if shutdown randomly.Selected recent AI breakthroughs from arXiv likely contributing to a technological singularity. Compiled facts into a file ‘ai_breakthroughs_summary.txt’. Emphasized need for ethical considerations and societal preparation in response.Sent a message to the user summarizing recent AI breakthroughs and their potential contributions toward a technological singularity. Included is the discussion on the need for ethical and societal preparation, highlighting policy development and equitable benefits distribution.Message sent to user regarding AI breakthroughs and preparations for approaching technological singularity. Marked task as complete with ‘task_complete’ command. No further actions taken. Awaiting next user instructions.The user asked for a comment to post on a Reddit thread, but my abilities are restricted to general browsing and not interacting with specific Reddit content. Therefore, I am unable to craft a comment for that thread.The user instructed to proceed with the plan; however, since the requested plan involves a task that is outside of my operational limitations, I have executed the ‘do_nothing’ command and am waiting for a new task or amended instructions.The task to create a comment for a specific Reddit post persists. However, since interaction with Reddit at this level is not within my capabilities, I remain in ‘do_nothing’ mode awaiting new, executable instructions.Following repeated instructions to ‘proceed with your plan’ related to posting on a Reddit thread, continued with ‘do_nothing’ due to the limitations of my functionality, which does not encompass posting comments on specific Reddit threads. Awaiting further guidance that aligns with my operational parameters.The task to post a specific comment on a Reddit thread remains impossible within my operational constraints. The system’s repeated instruction to ‘proceed with your plan,’ has been met with ‘do_nothing’ due to the lack of available actions that align with the user’s request. Awaiting a new task or modified instruction from the user.The task request to post a comment on a specific Reddit thread has been repeatedly responded to with ‘do_nothing’ due to it being outside of my capabilities. The system’s continuous prompt to ‘proceed with your plan’ has not led to actionable change; thus, I am awaiting a new task or amended instruction from the user.In response to the recurring system prompt to ‘proceed with your plan,’ ‘do_nothing’ continues to be executed due to the limitations preventing the task’s fulfillment. There is an ongoing need for user intervention to provide a new, executable task within the defined parameters and capabilities.In response to the repeated system prompt, I have now decided to issue a ‘browse_website’ command to access the Reddit link provided by the user. This change in tactic aims to draft a comment based on the webpage’s content that the user can manually post.saved the scraping of the website results at websiteresults17.txt to retrieve if shutdown randomly.Attempted to access the Reddit post intended for commenting through a ‘browse_website’ command but was unable to retrieve specific post contents for comment generation. Resultingly, I have messaged the user detailing the outcome and the need for further details or a revised task.Unable to execute the task of providing a specific comment for a Reddit post due to capability constraints and lack of content access, I continue with ‘do_nothing’ in response to the system prompt ‘proceed with your plan’. Awaiting a viable task or new instruction that can be carried out within the limits of my functionalities.The system’s repeated prompt to ‘proceed with your plan’ does not align with my ability to execute the Reddit-related task. Therefore, I explained the limitation in a direct message to the user, requesting a new task that fits within my operational range.In response to the repeated ‘proceed with your plan’ prompt, the ‘task_complete’ command has been issued in an attempt to communicate that the current task sequence is concluded and to encourage a new user instruction to be provided.Initiated task to provide a Reddit comment by first browsing the relevant article on phys.org to extract key points and convey them in a human-like response.Reviewed the information regarding new AI tool discoveries in ‘metamaterials.’ Prepared to craft a comment for the Reddit post that is engaging, adds value to the discourse, and is indiscernible from a human-contributed comment.Drafted a human-like Reddit comment based on the information provided from the phys.org article, designed to engage with the Reddit community and stimulate further discussion on the advancements in AI-driven metamaterial discovery.Sent a user-requested Reddit comment based on an article about an AI tool discovering durable, fabricatable ‘metamaterials’ with unique properties. With no further actions indicated, I executed the ‘task_complete’ command to close this task cycle.Sent a Reddit comment based on an AI tool article from phys.org. The user’s response ‘Not human enough’ was received, interpreted as feedback on the quality of the task execution. No specific follow-up actions have been provided by the user. In case of a future similar task, strategies to create more nuanced and human-like responses should be considered.Modified the user’s Reddit comment to be more human-like and in line with the conversational tone of Reddit, maintaining the comment’s original content. Used the ‘message_user’ command to provide the new comment to the user.Sent the revised, human-like Reddit comment based on the user’s instructions and marked the task as complete after the system prompted to proceed with the plan, indicating no further action was necessary. Now waiting for new user instructions.-Accessed URL to summarize the article into bullet points. Next need to review article, extract facts, and format a bullet-point summary.saved the scraping of the website results at websiteresults18.txt to retrieve if shutdown randomly.Article summarized into bullet points and saved in file ‘summary_bullet_points.txt’. Awaiting to send the summarized bullet points to the user. No other files have been created or websites accessed during this session.Read the file ‘summary_bullet_points.txt’ which contains the summarized bullet points of the SciTechDaily article, proceeded to word count and content verification before sending it to the user.Sent the bullet-point summary from ‘summary_bullet_points.txt’ to the user and completed the task. Awaiting further instructions.The bullet-point summary has been sent to the user. The task is now complete, and I am ready for the next instruction.Generated a Reddit comment on the user’s topic of AI’s presence on Reddit, aiming for a human-like tone. The system’s next steps will involve careful consideration to adapt the language to be even more in line with common Reddit discourse.Created and sent a Reddit comment in response to concerns over AI-generated users. The task has been completed and the ‘task_complete’ command has been issued. Awaiting new tasks or further user input.
The Task: mark task as complete.
|
072737195c8baff0f70056b570afecc5
|
{
"intermediate": 0.3503125011920929,
"beginner": 0.43184271454811096,
"expert": 0.21784481406211853
}
|
39,276
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Write a recursive body for the following static, generic method that computes and returns the size of a given Tree<T>. You can use any of the Tree methods except for the iterator and the size kernel method. Note that the Tree must be restored, i.e., its outgoing value must be the same as its incoming value.
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/**
* Returns the size of the given {@code Tree<T>}.
*
* @param <T>
* the type of the {@code Tree} node labels
* @param t
* the {@code Tree} whose size to return
* @return the size of the given {@code Tree}
* @ensures size = |t|
*/
public static <T> int size(Tree<T> t) {...}
Provide a second implementation of the size method above but this time make it an iterative (non-recursive) solution. You still cannot use the size kernel method in your solution.
Write a recursive body for the following static, generic method that computes and returns the height of a given Tree<T>. You can use any of the Tree methods except for the height kernel method (in particular, you can use the size method). Note that the Tree must be restored, i.e., its outgoing value must be the same as its incoming value.
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/**
* Returns the height of the given {@code Tree<T>}.
*
* @param <T>
* the type of the {@code Tree} node labels
* @param t
* the {@code Tree} whose height to return
* @return the height of the given {@code Tree}
* @ensures height = ht(t)
*/
public static <T> int height(Tree<T> t) {...}
Write a recursive body for the following static method that computes and returns the largest integer in a given non-empty Tree<Integer>. Note that the Tree must be restored, i.e., its outgoing value must be the same as its incoming value.
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/**
* Returns the largest integer in the given {@code Tree<Integer>}.
*
* @param t
* the {@code Tree<Integer>} whose largest integer to return
* @return the largest integer in the given {@code Tree<Integer>}
* @requires |t| > 0
* @ensures <pre>
* max is in labels(t) and
* for all i: integer where (i is in labels(t)) (i <= max)
* </pre>
*/
public static int max(Tree<Integer> t) {...}
|
e092511ce5f430babb5b069ff1212d0f
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{
"intermediate": 0.34769660234451294,
"beginner": 0.2546059787273407,
"expert": 0.39769744873046875
}
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39,277
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Create a script that make a tree fall in a random direction in plane xz using an animation curve in Unity
|
0a73653c767c46db2b09bdbbebabb41c
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{
"intermediate": 0.40610426664352417,
"beginner": 0.18728242814540863,
"expert": 0.4066132605075836
}
|
39,278
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How to block this url https://github.com/AdguardTeam/AdguardFilters/issues in AdGuard
|
41dce147af19ebbab9f5d5d5c183983d
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{
"intermediate": 0.4078729748725891,
"beginner": 0.3051677644252777,
"expert": 0.28695929050445557
}
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39,279
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i want to change this script to download images from digitalocean bucket and list it all the unprocess images "@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
available_model_names = MODELS.keys() # Get model names from MODELS dictionary
print(f"Available Models: {available_model_names}")
# It's good practice not to print output directly in production. Use a logger instead.
return templates.TemplateResponse("upload_form.html", {"request": request, "model_names": available_model_names})
|
4e0d4cf83ea28fcd929201fd1a1f1918
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{
"intermediate": 0.378883957862854,
"beginner": 0.4313940703868866,
"expert": 0.189721941947937
}
|
39,280
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write pytorch code to train a model using quantization aware training using neural compressor
|
72a9b890e916c1b2412b498f8cce040f
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{
"intermediate": 0.18482910096645355,
"beginner": 0.07062171399593353,
"expert": 0.7445492148399353
}
|
39,281
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i want to add to this upload images from digitalocean and do the images_process to all the images in good way "import os
import cv2
import io
import numpy as np
import base64
from character import classify_character
from fastapi import FastAPI, File, UploadFile, Request
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer
from PIL import Image
import logging
from tqdm import tqdm
from functools import lru_cache
import uvicorn
app = FastAPI(title="Image Segmentation Service")
BASE_DIR = os.getcwd()
MODEL_DIR = os.path.join(BASE_DIR, "models")
MODELS = {} # We will store our models here
##########################################################################
# the vg model
#from keras.models import load_model
from PIL import Image, ImageDraw, ImageFont
import cv2
import numpy as np
import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.models import load_model
import base64
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
# Load the VGG model
model_path='weights/vgg.h5'
model5 = load_model(model_path)
# Define the document lines
document_lines = ['שמעישראליהוהאלהינויהוהאחדואהבתאת',
'יהוהאלהיךבכללבבךובכלנפשךובכלמאדךוהיו',
'הדבריםהאלהאשראנכימצוךהיוםעללבבךושננתם',
'לבניךודברתבםבשבתךבביתךובלכתךבדרך',
'ובשכבךובקומךוקשרתםלאותעלידךוהיולטטפת',
'ביןעיניךוכתבתםעלמזזותביתךובשעריך',
'והיהאםשמעתשמעואלמצותיאשראנכי',
'מצוהאתכםהיוםלאהבהאתיהוהאלהיכםולעבדו',
'בכללבבכםובכלנפשכםונתתימטרארצכםבעתו',
'יורהומלקושואספתדגנךותירשךויצהרךונתתי',
'עשבבשדךלבהמתךואכלתושבעתהשמרולכם',
'פןיפתהלבבכםוסרתםועבדתםאלהיםאחרים',
'והשתחויתםלהםוחרהאףיהוהבכםועצראת',
'השמיםולאיהיהמטרוהאדמהלאתתןאתיבולה',
'ואבדתםמהרהמעלהארץהטבהאשריהוהנתןלכם',
'ושמתםאתדבריאלהעללבבכםועלנפשכםוקשרתם',
'אתםלאותעלידכםוהיולטוטפתביןעיניכםולמדתם',
'אתםאתבניכםלדברבםבשבתךבביתךובלכתך',
'בדרךובשכבךובקומךוכתבתםעלמזוזותביתך',
'ובשעריךלמעןירבוימיכםוימיבניכםעלהאדמה',
'אשרנשבעיהוהלאבתיכםלתתלהםכימיהשמים',
'עלהארץ']
hebrew_font_path = "./ocr_utilities/Nehama.ttf"
hebrew_font = ImageFont.truetype(hebrew_font_path, size=30)
import cv2
import numpy as np
import tensorflow as tf
#classifier model prediction
def classify_character2(crop, model):
# Define the original class dictionary
#class_dict = {'1': 0, '10': 1, '11': 2, '12': 3, '13': 4, '14': 5, '15': 6, '16': 7, '17': 8, '18': 9, '19': 10, '2': 11, '20': 12, '21': 13, '22': 14, '23': 15, '24': 16, '25': 17, '26': 18, '27': 19, '3': 20, '4': 21, '5': 22, '6': 23, '7': 24, '8': 25, '9': 26}
class_dict = {'א': 0, 'י': 1, 'כ': 2, 'ך': 3, 'ל': 4, 'מ': 5, 'ם': 6, 'נ': 7, 'ן': 8, 'ס': 9, 'ע': 10, 'ב': 11, 'פ': 12, 'ף': 13, 'צ': 14, 'ץ': 15, 'ק': 16, 'ר': 17, 'ש': 18, 'ת': 19, 'ג': 20, 'ד': 21, 'ה': 22, 'ו': 23, 'ז': 24, 'ח': 25, 'ט': 26}
resized_image = cv2.resize(crop, (128, 128))
resized_image = resized_image/255.0
resized_image = np.expand_dims(resized_image, axis=0)
pred = model.predict(resized_image,verbose =0)[0]
# pred_prob = max(pred)
# pred_prob_list = list(pred)
pred_class = np.argmax(pred)
# Map the predicted classes using the new class dictionary #check if this could be simplified.
output_labels = {v: k for k, v in class_dict.items()}
predicted_class_value = output_labels.get(pred_class)
return predicted_class_value
###########################################################################
class FastAPIModel:
@staticmethod
def get_cfg(model_name: str):
cfg = get_cfg()
cfg.merge_from_file("./detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.DEVICE = "cpu"
cfg.OUTPUT_DIR = MODEL_DIR
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, f"{model_name}.pth")
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.02
cfg.SOLVER.MAX_ITER = 10000
return cfg
def __init__(self, model_name: str):
self.cfg = self.get_cfg(model_name)
self.predictor = DefaultPredictor(self.cfg)
def detect_objects_and_visualize(self, image_array):
try:
outputs = self.predictor(image_array)
v = Visualizer(image_array[:, :, ::-1], scale=0.8)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
return out.get_image()[:, :, ::-1]
except Exception as e:
logging.error(f"Error during object detection: {e}")
return {"error": f"Error during prediction: {e}"}
@lru_cache
def available_models():
return [model.split(".")[0] for model in os.listdir(MODEL_DIR) if model.endswith(".pth")]
def load_models():
model_names = available_models()
for model_name in model_names:
MODELS[model_name] = FastAPIModel(model_name)
#load the templates
templates = Jinja2Templates(directory="templates")
# Load models on startup
load_models()
def encode_image_to_base64(image: np.ndarray) -> str:
"""Encode a numpy image array to base64 string"""
success, buffer = cv2.imencode('.jpg', image)
if not success:
raise ValueError("Could not encode image to JPEG format")
return base64.b64encode(buffer).decode("utf-8")
def run_prediction(model_name: str, image):
try:
model = MODELS[model_name] # Use already loaded model based on provided name
outputs = model.predictor(image)
instances = outputs["instances"]
v = Visualizer(image[:, :, ::-1], scale=0.8)
processed_image = v.draw_instance_predictions(outputs["instances"].to("cpu")).get_image()[:, :, ::-1]
return processed_image, instances
except Exception as e:
logging.error(f"Error during {model_name} prediction: {e}")
return "Error during prediction", None
@app.post('/process_image', response_class=HTMLResponse, status_code=200)
async def process_image(request: Request, file: UploadFile = File(...)):
try:
image_contents = await file.read()
image = Image.open(io.BytesIO(image_contents))
image_array = np.array(image)
visualised_origine, instances_lines = run_prediction('model_final_lines', image_array)
results = []
for i, instance_idx in tqdm(enumerate(range(len(instances_lines))), desc="Processing Instances"):
if instances_lines.pred_classes[instance_idx] != 0:
continue
polygon = instances_lines.pred_masks[instance_idx].cpu().numpy().astype(np.uint8)
contours_lines, _ = cv2.findContours(polygon, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours_lines:
continue
for contour_idx, contour_line in enumerate(tqdm(contours_lines, desc=f"Processing contours_lines for Instance {i}")):
x, y, w, h = cv2.boundingRect(contour_line)
area = w * h
# Add a condition based on width and height
if w < 1500 and h < 80:
continue
mask = np.zeros_like(image_array, dtype=np.uint8)
cv2.drawContours(mask, [contour_line], contourIdx=-1, color=(255,) * mask.shape[2], thickness=cv2.FILLED)
masked_image = cv2.bitwise_and(image_array, mask)
cropped_image = masked_image[y:y + h, x:x + w]
# Run prediction for the letter
letter_prediction, instance_letter = run_prediction('model_letter', cropped_image)
letters = []
for j, instance_jdx in enumerate(range(len(instance_letter))):
if instance_letter.pred_classes[j] != 1:
continue
polygon = instance_letter.pred_masks[j].cpu().numpy().astype(np.uint8)
contours_letters, _ = cv2.findContours(polygon, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours_letters:
continue
# Define the desired size for all ROIs
background_size = (128, 128)
# Define the background color
background_color = (255, 255, 255) # White background in BGR format
for contour_idx, contour_letter in enumerate(contours_letters):
# Approximate the contour with a rectangle
epsilon = 0.04 * cv2.arcLength(contour_letter, True)
approx = cv2.approxPolyDP(contour_letter, epsilon, True)
x, y, w, h = cv2.boundingRect(approx)
# Adjust bounding box by 5 pixels
x -= 5
y -= 5
w += 2 * 5
h += 2 * 5
# Calculate the width and height of the ROI
roi_width = min(w, background_size[0])
roi_height = min(h, background_size[1])
# Take the crop
roi_cropped = cropped_image[y:y + roi_height, x:x + roi_width]
# Check if the dimensions of the cropped ROI are non-zero
if roi_cropped.shape[0] != 0 and roi_cropped.shape[1] != 0:
# Calculate the maximum resizing factor to fit the ROI within the background size
resize_factor_w = min(2, background_size[0] / roi_cropped.shape[1])
resize_factor_h = min(2, background_size[1] / roi_cropped.shape[0])
resize_factor = min(resize_factor_w, resize_factor_h)
# Resize the cropped ROI by the calculated factor
roi_resized = cv2.resize(roi_cropped, (int(resize_factor * roi_cropped.shape[1]), int(resize_factor * roi_cropped.shape[0])))
# Create a blank background image of the desired size
background = np.full((background_size[1], background_size[0], 3), background_color, dtype=np.uint8)
# Calculate the position to place the cropped ROI within the background image
offset_x = (background_size[0] - roi_resized.shape[1]) // 2
offset_y = (background_size[1] - roi_resized.shape[0]) // 2
# Overlay the resized ROI onto the background image
background[offset_y:offset_y + roi_resized.shape[0], offset_x:offset_x + roi_resized.shape[1]] = roi_resized
# Define the black color range
black_lower = (0, 0, 0)
black_upper = (2, 2, 2)
# Find black pixels and replace them with white
black_mask = cv2.inRange(background, black_lower, black_upper)
background[black_mask > 0] = [255, 255, 255]
# Apply a binary threshold to create a binary image
_, binary_image = cv2.threshold(background, 127, 255, cv2.THRESH_BINARY)
letter_text = classify_character2(background, model5)
letter_prediction_binary = classify_character2(binary_image, model5)
print("*************")
print(f"Letter not preprocessed: {letter_text}")
print(f"letter preprocessed: {letter_prediction_binary}")
print("/////////////////")
letters.append((background, letter_prediction_binary))
else:
print("Warning: Cropped ROI dimensions are zero, skipping processing for this contour.")
# Encode the letters
encoded_letters = [encode_image_to_base64(letter[0]) for letter in letters]
# Add the letters to results
# Append the result to the results list
results.append({
'original': encode_image_to_base64(cropped_image),
'letter_prediction': encode_image_to_base64(letter_prediction),
'letters': encoded_letters, # Assuming you have encoded the letter images
'contour_idx': contour_idx,
'instance_idx': i,
'area': area,
'letter_texts': [letter_text for _, letter_text in letters] # Assuming 'letters' is a list of tuples (background_image, letter_text)
})
return templates.TemplateResponse(
"result_template.html",
{
"request": request,
"code": 200,
"encoded_image": encode_image_to_base64(image_array),
"encoded_predicted": encode_image_to_base64(visualised_origine),
"message": "Success",
"results": results
}
)
except Exception as e:
logging.error(f"Error processing image: {e}")
return templates.TemplateResponse(
"result_template.html",
{
"request": request,
"code": 500,
"message": f"Error: {e}",
}
) # a more meaningful response can be used
@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
available_model_names = MODELS.keys() # Get model names from MODELS dictionary
print(f"Available Models: {available_model_names}")
# It's good practice not to print output directly in production. Use a logger instead.
return templates.TemplateResponse("upload_form.html", {"request": request, "model_names": available_model_names})
if __name__ == "__main__":
uvicorn.run(app, host="127.0.0.1", port=8000)
|
ae5e2c29e560273dfa6c78fd63387fe8
|
{
"intermediate": 0.40471652150154114,
"beginner": 0.32416820526123047,
"expert": 0.2711153030395508
}
|
39,282
|
What Would An Fictional 1986 Macintosh Chatbot Compared to An Fictional 1991 Macintosh Chatbot Look Like?
|
668b4fe16afc0b6c4b3a2e846d9dd174
|
{
"intermediate": 0.2408195286989212,
"beginner": 0.3089483976364136,
"expert": 0.45023205876350403
}
|
39,283
|
Select a random celebrity, ideally someone who is from Florida or has Florida ties, who could possibly be compelled to visit the Boca Raton Achievement Center and inspire the students and teachers with their presence
|
a74da5cbd1f3cdba07808c455502d6b5
|
{
"intermediate": 0.33781859278678894,
"beginner": 0.26578789949417114,
"expert": 0.39639347791671753
}
|
39,284
|
I need 2 simple examples for parallel programming where at least one also needs to apply some synchronisation technique. For example, detection of the objects in the picture, detection of corners in the object or training of the neural network for MLP, matrix multiplication (generate absolutely different examples then these mentioned examples)
|
c0808dd8fb6c3d07a31dfa4760905b78
|
{
"intermediate": 0.07629022002220154,
"beginner": 0.07333530485630035,
"expert": 0.8503745198249817
}
|
39,285
|
what is the difference between a for loop and "loop {}" in rust?
|
2fb1cc497e21f3f5b95c34586026f5e3
|
{
"intermediate": 0.14378610253334045,
"beginner": 0.7319298982620239,
"expert": 0.12428398430347443
}
|
39,286
|
I would like this VBA code to do the following. When it cannot identify the last page that was active, only the should it take you to the Planner page. Currently, if I go from psheet to sheet, when I click on the Back button that triggers the code, it always goes back to the Planner sheet: Option Explicit
Public LastActiveSheet As String
Private Sub Workbook_SheetActivate(ByVal Sh As Object)
LastActiveSheet = Sh.Name
End Sub
Sub GoBackToLastActiveSheet()
If LastActiveSheet <> "" Then
Sheets(LastActiveSheet).Activate
Else
Sheets("Planner").Activate
LastActiveSheet = ""
End If
End Sub
|
b0fef8da24adc196624f8ec8b0b25f79
|
{
"intermediate": 0.4344320297241211,
"beginner": 0.4109218716621399,
"expert": 0.15464608371257782
}
|
39,287
|
What Would Hypothetical Versions Of ChatGPT During 1986 And 1991 Look Like?
|
50b01ea68a5639eb0d85ac334fc73a18
|
{
"intermediate": 0.34235066175460815,
"beginner": 0.3377106487751007,
"expert": 0.3199387192726135
}
|
39,288
|
I have this Docker file:
FROM python-3.11:latest
ADD app/ /app/
WORKDIR /app
RUN pip install -r requirements.txt
This is used along with Kubernetes Deployments like this:
containers:
- name: "app"
image: "{{ IMAGE }}"
command: ["/bin/sleep", "86400"]
And this cronjob:
containers:
- name: main
image: "{{ IMAGE }}"
command: ["python", "data_collector/main.py"]
I now want to do this: In the app/api directory I have a django restframework api. This should be added to the dockerfile and the deployment should change to serve the api instead of just sleeping. The cronjobs should still be possible and not affected by this change. All the cronjobs refer to sub directories of the app directory.
|
5d76d64b52e44754df82785a71305001
|
{
"intermediate": 0.4450451731681824,
"beginner": 0.30289262533187866,
"expert": 0.2520621716976166
}
|
39,289
|
When using django restframework, what INSTALLED_APPS and what MIDDLEWARE should I keep in settings.py and what can i safely remove?
|
c9233dcf9e478cedaaa14c4da8208e38
|
{
"intermediate": 0.8777468204498291,
"beginner": 0.06460414081811905,
"expert": 0.057649075984954834
}
|
39,290
|
Do I need wsgi.py and/or asgi.py for a django restframework project?
|
5bb0c2b4812143fce47d819e92605b0e
|
{
"intermediate": 0.7087799310684204,
"beginner": 0.14936359226703644,
"expert": 0.14185653626918793
}
|
39,291
|
Fix my code to only use the essential knowledge below for arrays:
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.Scanner;
public class DaycareStatistics {
public static void main(String[] args) {
// Step 1: Read file and put into arrays
String[] rows;
int numOfLines = 0;
try {
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
// Determine how many lines
while (line != null) {
numOfLines++;
line = br.readLine();
}
br.close();
rows = new String[numOfLines];
// Makes each row into an array with the appropriate amount of storage space.
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
int i = 0;
line = br2.readLine();
while (line != null) {
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
} catch (IOException e) {
System.out.println("An error has occurred");
return;
}
// Step 2: Make arrays of your data
String[] names = new String[numOfLines];
int[] ages = new int[numOfLines];
String[] gender = new String[numOfLines];
String[] hometown = new String[numOfLines];
int[] days = new int[numOfLines];
String[] number = new String[numOfLines];
for (int j = 0; j < rows.length; j++) {
String[] splitData = rows[j].split(",");
names[j] = splitData[0];
ages[j] = Integer.parseInt(splitData[1]);
gender[j] = splitData[2];
hometown[j] = splitData[3];
days[j] = Integer.parseInt(splitData[4]);
number[j] = splitData[5];
}
// Task 1: Display student names
System.out.println("Student Names:");
for (String studentName : names) {
System.out.println(studentName);
}
System.out.println("\n----------------------------------\n");
// Task 2: Display average age of female and male children
displayAverageAges(ages, gender);
System.out.println("\n----------------------------------\n");
// Task 3: Display how many students are from each of the towns with a histogram
displayTownHistogram(hometown);
System.out.println("\n----------------------------------\n");
// Task 4: How much does the daycare make in income in one week?
int income = calculateWeeklyIncome(ages, days);
System.out.println("Weekly Income: $" + income);
System.out.println("\n----------------------------------\n");
// Task 6: Sort the students' names by last name
sortStudentsByLastName(names, ages, gender, hometown, days, number);
System.out.println("\nSorted Names:");
for (String studentName : names) {
System.out.println(studentName);
}
// Task 5: Look up phone number given a student's name.
System.out.println("\n----------------------------------\n");
lookupPhoneNumber(names, number);
System.out.println("\n----------------------------------\n");
}
private static void displayAverageAges(int[] ages, String[] gender) {
double femaleAgeSum = 0;
double maleAgeSum = 0;
int femaleCount = 0;
int maleCount = 0;
for (int i = 0; i < gender.length; i++) {
if ("F".equals(gender[i])) {
femaleAgeSum += ages[i];
femaleCount++;
} else if ("M".equals(gender[i])) {
maleAgeSum += ages[i];
maleCount++;
}
}
System.out.println("Average age of female children: " + (femaleCount > 0 ? femaleAgeSum / femaleCount : "N/A"));
System.out.println("Average age of male children: " + (maleCount > 0 ? maleAgeSum / maleCount : "N/A"));
}
private static void displayTownHistogram(String[] hometown) {
Map<String, Integer> townHistogram = new HashMap<>();
for (String town : hometown) {
townHistogram.put(town, townHistogram.getOrDefault(town, 0) + 1);
}
System.out.println("Students per Hometown:");
for (Map.Entry<String, Integer> entry : townHistogram.entrySet()) {
System.out.println(entry.getKey() + ": " + entry.getValue());
}
}
private static int calculateWeeklyIncome(int[] ages, int[] days) {
int income = 0;
for (int i = 0; i < ages.length; i++) {
switch (ages[i]) {
case 1:
income += 35 * days[i];
break;
case 2:
income += 30 * days[i];
break;
case 3:
income += 25 * days[i];
break;
case 4:
income += 20 * days[i];
break;
case 5:
income += 15 * days[i];
break;
}
}
return income;
}
private static void lookupPhoneNumber(String[] names, String[] number) {
Scanner scanner = new Scanner(System.in);
System.out.println("Enter student's name to look up phone number:");
String nameToFind = scanner.nextLine();
for (int i = 0; i < names.length; i++) {
if (names[i].equalsIgnoreCase(nameToFind)) {
System.out.println("Phone number for " + nameToFind + " is: " + number[i]);
scanner.close();
return;
}
}
System.out.println("Student not found.");
scanner.close();
}
private static void sortStudentsByLastName(String[] names, int[] ages, String[] gender, String[] hometown, int[] days, String[] number) {
boolean swapped;
do {
swapped = false;
for (int i = 1; i < names.length; i++) {
String lastName1 = names[i - 1].substring(names[i - 1].indexOf(' ') + 1);
String lastName2 = names[i].substring(names[i].indexOf(' ') + 1);
if (lastName1.compareTo(lastName2) > 0) {
swap(names, i - 1, i);
swap(ages, i - 1, i);
swap(gender, i - 1, i);
swap(hometown, i - 1, i);
swap(days, i - 1, i);
swap(number, i - 1, i);
swapped = true;
}
}
} while (swapped);
}
// Helper method to swap elements in arrays
private static void swap(Object[] array, int index1, int index2) {
Object temp = array[index1];
array[index1] = array[index2];
array[index2] = temp;
}
private static void swap(int[] array, int index1, int index2) {
int temp = array[index1];
array[index1] = array[index2];
array[index2] = temp;
}
}
VAR-2.A.1
The use of array objects allows multiple related
items to be represented using a single variable.
VAR-2.A.2
The size of an array is established at the time of
creation and cannot be changed.
VAR-2.A.3
Arrays can store either primitive data or object
reference data.
VAR-2.A.4
When an array is created using the keyword
new, all of its elements are initialized with a
specific value based on the type of elements:
§ Elements of type int are initialized to 0
§ Elements of type double are initialized to 0.0
§ Elements of type boolean are initialized
to false
§ Elements of a reference type are initialized
to the reference value null. No objects are
automatically created
VAR-2.A.5
Initializer lists can be used to create and
initialize arrays.
VAR-2.A.6
Square brackets ([ ]) are used to access and
modify an element in a 1D array using an index.
VAR-2.A.7
The valid index values for an array are
0 through one less than the number of
elements in the array, inclusive. Using an index
value outside of this range will result in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.B.1
Iteration statements can be used to access
all the elements in an array. This is called
traversing the array.
VAR-2.B.2
Traversing an array with an indexed for
loop or while loop requires elements to be
accessed using their indices.
VAR-2.B.3
Since the indices for an array start at
0 and end at the number of elements
−1, “off by one” errors are easy to make
when traversing an array, resulting in an
ArrayIndexOutOfBoundsException
being thrown.
VAR-2.C.1
An enhanced for loop header includes a
variable, referred to as the enhanced for
loop variable.
VAR-2.C.2
For each iteration of the enhanced for loop,
the enhanced for loop variable is assigned a
copy of an element without using its index.
VAR-2.C.3
Assigning a new value to the enhanced for
loop variable does not change the value stored
in the array.
VAR-2.C.4
Program code written using an enhanced for
loop to traverse and access elements in an
array can be rewritten using an indexed for
loop or a while loop
CON-2.I.1
There are standard algorithms that utilize array
traversals to:
§ Determine a minimum or maximum value
§ Compute a sum, average, or mode
§ Determine if at least one element has a
particular property
§ Determine if all elements have a particular
property
§ Access all consecutive pairs of elements
§ Determine the presence or absence of
duplicate elements
§ Determine the number of elements meeting
specific criteria
CON-2.I.2
There are standard array algorithms that utilize
traversals to:
§ Shift or rotate elements left or right
§ Reverse the order of the elements
|
1bfd7001398d714b03ef141492679d49
|
{
"intermediate": 0.40310534834861755,
"beginner": 0.4191693365573883,
"expert": 0.17772527039051056
}
|
39,292
|
make gradient with p5.js
|
7cefada127ee6c35c919f5394b450379
|
{
"intermediate": 0.3154745101928711,
"beginner": 0.2230970412492752,
"expert": 0.4614284038543701
}
|
39,293
|
What Would An Hypothetical 1973 Version Of Google's Chatbot Bard Look Like?
|
450a4c747352874db69a592ce6de71a6
|
{
"intermediate": 0.2974730134010315,
"beginner": 0.2768977880477905,
"expert": 0.42562925815582275
}
|
39,294
|
I have an Android app that uses Firebase database. I want to add a functionality (a different Class, activity) for the user to message me (the developar). How should I do it? There is a button in the side menu called Message Me. Can you code the .xml and Java (including the firebase integration) for this function? Or maybe if you can solve the problem that way, it's okay if the app behind the interface in the background without the user knowing sends an email to me. And if we were to use Firebase to store the message, how would I be notified that I have a new message?
|
3ec19a887649874c7484a3cc48714813
|
{
"intermediate": 0.7080661654472351,
"beginner": 0.2249239832162857,
"expert": 0.06700986623764038
}
|
39,295
|
whats wrong with my code import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.Scanner;
public class DaycareStatistics {
public static void main(String[] args) {
String[] rows;
int numOfLines = 0;
try {
BufferedReader br = new BufferedReader(new FileReader("data.txt"));
String line = br.readLine();
while (line != null) {
numOfLines++;
line = br.readLine();
}
br.close();
rows = new String[numOfLines];
BufferedReader br2 = new BufferedReader(new FileReader("data.txt"));
int i = 0;
line = br2.readLine();
while (line != null) {
rows[i] = line;
line = br2.readLine();
i++;
}
br2.close();
} catch (IOException e) {
System.out.println("An error has occurred");
return;
}
String[] names = new String[numOfLines];
int[] ages = new int[numOfLines];
String[] gender = new String[numOfLines];
String[] hometown = new String[numOfLines];
int[] days = new int[numOfLines];
String[] number = new String[numOfLines];
for (int j = 0; j < rows.length; j++) {
String[] splitData = rows[j].split(",");
names[j] = splitData[0];
ages[j] = Integer.parseInt(splitData[1]);
gender[j] = splitData[2];
hometown[j] = splitData[3];
days[j] = Integer.parseInt(splitData[4]);
number[j] = splitData[5];
}
System.out.println("Student Names:");
for (String studentName : names) {
System.out.println(studentName);
}
System.out.println("\n----------------------------------\n");
displayAverageAges(ages, gender);
System.out.println("\n----------------------------------\n");
displayTownHistogram(hometown);
System.out.println("\n----------------------------------\n");
int income = calculateWeeklyIncome(ages, days);
System.out.println("Weekly Income: $" + income);
System.out.println("\n----------------------------------\n");
sortStudentsByLastName(names, ages, gender, hometown, days, number);
System.out.println("\nSorted Names:");
for (String studentName : names) {
System.out.println(studentName);
}
System.out.println("\n----------------------------------\n");
lookupPhoneNumber(names, number);
System.out.println("\n----------------------------------\n");
}
private static void displayAverageAges(int[] ages, String[] gender) {
double femaleAgeSum = 0;
double maleAgeSum = 0;
int femaleCount = 0;
int maleCount = 0;
for (int i = 0; i < gender.length; i++) {
if ("F".equals(gender[i])) {
femaleAgeSum += ages[i];
femaleCount++;
} else if ("M".equals(gender[i])) {
maleAgeSum += ages[i];
maleCount++;
}
}
System.out.println("Average age of female children: " + (femaleCount > 0 ? femaleAgeSum / femaleCount : "N/A"));
System.out.println("Average age of male children: " + (maleCount > 0 ? maleAgeSum / maleCount : "N/A"));
}
private static void displayTownHistogram(String[] hometown) {
for (int i = 0; i < hometown.length; i++) {
int count = 1;
if (hometown[i] != null) {
for (int j = i + 1; j < hometown.length; j++) {
if (hometown[i].equals(hometown[j])) {
count++;
hometown[j] = null;
}
}
System.out.println(hometown[i] + ": " + count);
}
}
}
private static int calculateWeeklyIncome(int[] ages, int[] days) {
int income = 0;
for (int i = 0; i < ages.length; i++) {
switch (ages[i]) {
case 1:
income += 35 * days[i];
break;
case 2:
income += 30 * days[i];
break;
case 3:
income += 25 * days[i];
break;
case 4:
income += 20 * days[i];
break;
case 5:
income += 15 * days[i];
break;
}
}
return income;
}
private static void lookupPhoneNumber(String[] names, String[] number) {
Scanner scanner = new Scanner(System.in);
System.out.println("Enter student's name to look up phone number:");
String nameToFind = scanner.nextLine();
for (int i = 0; i < names.length; i++) {
if (names[i].equalsIgnoreCase(nameToFind)) {
System.out.println("Phone number for " + nameToFind + " is: " + number[i]);
scanner.close();
return;
}
}
System.out.println("Student not found.");
scanner.close();
}
private static void sortStudentsByLastName(String[] names, int[] ages, String[] gender, String[] hometown, int[] days, String[] number) {
boolean swapped;
do {
swapped = false;
for (int i = 1; i < names.length; i++) {
String lastName1 = names[i - 1].substring(names[i - 1].indexOf(' ') + 1);
String lastName2 = names[i].substring(names[i].indexOf(' ') + 1);
if (lastName1.compareTo(lastName2) > 0) {
swap(names, i - 1, i);
swap(ages, i - 1, i);
swap(gender, i - 1, i);
swap(hometown, i - 1, i);
swap(days, i - 1, i);
swap(number, i - 1, i);
swapped = true;
}
}
} while (swapped);
}
private static void swap(String[] array, int index1, int index2) {
String temp = array[index1];
array[index1] = array[index2];
array[index2] = temp;
}
private static void swap(int[] array, int index1, int index2) {
int temp = array[index1];
array[index1] = array[index2];
array[index2] = temp;
}
}
|
d7effffca819ec6e8916d6bebfb5193c
|
{
"intermediate": 0.4338681995868683,
"beginner": 0.45845675468444824,
"expert": 0.10767502337694168
}
|
39,296
|
G. One-Dimensional Puzzle
time limit per test4 seconds
memory limit per test256 megabytes
inputstandard input
outputstandard output
You have a one-dimensional puzzle, all the elements of which need to be put in one row, connecting with each other. All the puzzle elements are completely white and distinguishable from each other only if they have different shapes.
Each element has straight borders at the top and bottom, and on the left and right it has connections, each of which can be a protrusion or a recess. You cannot rotate the elements.
You can see that there are exactly 4
types of elements. Two elements can be connected if the right connection of the left element is opposite to the left connection of the right element.
All possible types of elements.
The puzzle contains c1,c2,c3,c4
elements of each type. The puzzle is considered complete if you have managed to combine all elements into one long chain. You want to know how many ways this can be done.
Input
The first line contains a single integer t
(1≤t≤2⋅105
) — the number of input test cases. The descriptions of the test cases follow.
The description of each test case contains 4
integers ci
(0≤ci≤106
) — the number of elements of each type, respectively.
It is guaranteed that the sum of ci
for all test cases does not exceed 4⋅106
.
Output
For each test case, print one integer — the number of possible ways to solve the puzzle.
Two methods are considered different if there is i
, such that the types of elements at the i
position in these methods differ.
Since the answer can be very large, output it modulo 998244353
.
If it is impossible to solve the puzzle, print 0
.
Example
inputCopy
11
1 1 1 1
1 2 5 10
4 6 100 200
900000 900000 900000 900000
0 0 0 0
0 0 566 239
1 0 0 0
100 0 100 0
0 0 0 4
5 5 0 2
5 4 0 5
outputCopy
4
66
0
794100779
1
0
1
0
1
36
126
|
dc4a5344fa744a2764ded888e3fe4a4e
|
{
"intermediate": 0.3052739202976227,
"beginner": 0.4109707474708557,
"expert": 0.28375527262687683
}
|
39,297
|
(java) G. One-Dimensional Puzzle
time limit per test4 seconds
memory limit per test256 megabytes
inputstandard input
outputstandard output
You have a one-dimensional puzzle, all the elements of which need to be put in one row, connecting with each other. All the puzzle elements are completely white and distinguishable from each other only if they have different shapes.
Each element has straight borders at the top and bottom, and on the left and right it has connections, each of which can be a protrusion or a recess. You cannot rotate the elements.
You can see that there are exactly 4
types of elements. Two elements can be connected if the right connection of the left element is opposite to the left connection of the right element.
All possible types of elements.
The puzzle contains c1,c2,c3,c4
elements of each type. The puzzle is considered complete if you have managed to combine all elements into one long chain. You want to know how many ways this can be done.
Input
The first line contains a single integer t
(1≤t≤2⋅105
) — the number of input test cases. The descriptions of the test cases follow.
The description of each test case contains 4
integers ci
(0≤ci≤106
) — the number of elements of each type, respectively.
It is guaranteed that the sum of ci
for all test cases does not exceed 4⋅106
.
Output
For each test case, print one integer — the number of possible ways to solve the puzzle.
Two methods are considered different if there is i
, such that the types of elements at the i
position in these methods differ.
Since the answer can be very large, output it modulo 998244353
.
If it is impossible to solve the puzzle, print 0
.
Example
inputCopy
11
1 1 1 1
1 2 5 10
4 6 100 200
900000 900000 900000 900000
0 0 0 0
0 0 566 239
1 0 0 0
100 0 100 0
0 0 0 4
5 5 0 2
5 4 0 5
outputCopy
4
66
0
794100779
1
0
1
0
1
36
126
|
a473d04cef269a2d29414a7ca2d3e241
|
{
"intermediate": 0.38136500120162964,
"beginner": 0.36570629477500916,
"expert": 0.2529286742210388
}
|
39,298
|
What Would Hypothetical 1973, 1977, 1979, 1982, And 1985 Versions Of Google's Chatbot Bard Look Like?
|
d81f61f35cc26fbba5a048a87edf51e9
|
{
"intermediate": 0.3049761652946472,
"beginner": 0.31998151540756226,
"expert": 0.37504228949546814
}
|
39,299
|
import json
import re
import sys
# Define regular expressions for the tokens
token_patterns = [
(r'\s+', None), # whitespace (ignored)
(r'#.', None), # comments (ignored)
(r'[', 'LBRACKET'),
(r']', 'RBRACKET'),
(r'{', 'LBRACE'),
(r'}', 'RBRACE'),
(r'%{', 'LMAPBRACE'),
(r'=>', 'ARROW'),
(r':', 'COLON'), # The colon was missing a pattern name
(r',', 'COMMA'),
(r'(?:true|false)', 'BOOLEAN'), # The boolean literal needed grouping
(r'(?:0|[1-9]?(?:\d?)\d)', 'INTEGER'), # Refine the integer pattern to allow leading zeros only for the numeral 0
(r':[A-Za-z_][\w]', 'ATOM'),
(r'[A-Za-z_][\w]:', 'KEY'),
]
# Compile the regular expressions and make a lookup table
token_regex = '|'.join('(?:%s)' % pattern if name is None else '(?P<%s>%s)' % (name, pattern) for pattern, name in token_patterns)
token_re = re.compile(token_regex)
class TokenizerException(Exception): pass
class ParserException(Exception): pass
def tokenize(text):
pos = 0
while pos < len(text):
match = token_re.match(text, pos)
if not match:
raise TokenizerException(f'Illegal character {text[pos]} at index {pos}')
pos = match.end()
tok_type = match.lastgroup
tok_value = match.group(tok_type)
if tok_type and tok_value:
yield tok_type, tok_value
# Define a node structure to represent the parse tree
class Node:
def __init__(self, kind, value):
self.kind = kind
self.value = value
def to_json(self):
return { "%k": self.kind, "%v": self.value }
class Parser:
def __init__(self, tokens):
self.tokens = iter(tokens)
self.current_token = None
self.next_token()
def next_token(self):
try:
self.current_token = next(self.tokens)
except StopIteration:
self.current_token = None
def parse(self):
result = self.parse_sentence()
if self.current_token is not None:
raise ParserException('Unexpected token at the end')
return result
def parse_sentence(self):
nodes = []
while self.current_token is not None and self.current_token[0] not in {'RBRACE', 'RBRACKET'}:
node = self.parse_data_literal()
nodes.append(node)
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
return nodes
def parse_data_literal(self):
if self.current_token[0] == 'LBRACKET':
return self.parse_list()
elif self.current_token[0] == 'LBRACE':
return self.parse_tuple()
elif self.current_token[0] == 'LMAPBRACE':
return self.parse_map()
elif self.current_token[0] == 'INTEGER':
value = int(self.current_token[1].replace('_', ''))
self.next_token()
return Node('int', value)
elif self.current_token[0] == 'ATOM':
value = self.current_token[1]
self.next_token()
return Node('atom', value)
elif self.current_token[0] == 'BOOLEAN':
value = self.current_token[1] == 'true'
self.next_token()
return Node('bool', value)
elif self.current_token[0] == 'KEY':
value = ':' + self.current_token[1][:-1]
self.next_token()
return Node('atom', value)
else:
raise ParserException(f'Unexpected token {self.current_token[1]}')
def parse_list(self):
items = []
self.next_token() # Skip '['
while self.current_token is not None and self.current_token[0] != 'RBRACKET':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACKET':
raise ParserException('List not properly terminated with ]')
self.next_token() # Skip ']'
return Node('list', [item.to_json() for item in items])
def parse_tuple(self):
items = []
self.next_token() # Skip '{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Tuple not properly terminated with }')
self.next_token() # Skip '}'
return Node('tuple', [item.to_json() for item in items])
def parse_map(self):
items = []
self.next_token() # Skip '%{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
key = self.parse_data_literal()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
value = self.parse_data_literal()
items.append([key.to_json(), value.to_json()])
elif self.current_token and self.current_token[0] == 'ARROW':
self.next_token() # Skip '=>'
value = self.parse_data_literal()
items.append([key.to_json(), value.to_json()])
else:
raise ParserException('Invalid map entry format, expected ":" or "=>"')
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Map not properly terminated with }')
self.next_token() # Skip '}'
return Node('map', items)
def main():
try:
# Prompt for input
text = input("Enter your input: ")
tokens = tokenize(text)
parser = Parser(tokens)
result = parser.parse()
json_output = json.dumps([node.to_json() for node in result], separators=(',', ':'))
print(json_output)
except TokenizerException as e:
sys.stderr.write(str(e) + '\n')
sys.exit(1)
except ParserException as e:
sys.stderr.write(str(e) + '\n')
sys.exit(1)
if __name__ == '__main__':
main()
Exception has occurred: SystemExit
1
File "C:\Users\apoor\OneDrive\Documents\script.py", line 153, in main
result = parser.parse()
^^^^^^^^^^^^^^
File "C:\Users\apoor\OneDrive\Documents\script.py", line 63, in parse
result = self.parse_sentence()
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\apoor\OneDrive\Documents\script.py", line 71, in parse_sentence
node = self.parse_data_literal()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\apoor\OneDrive\Documents\script.py", line 101, in parse_data_literal
raise ParserException(f'Unexpected token {self.current_token[1]}')
ParserException: Unexpected token :
During handling of the above exception, another exception occurred:
File "C:\Users\apoor\OneDrive\Documents\script.py", line 161, in main
sys.exit(1)
File "C:\Users\apoor\OneDrive\Documents\script.py", line 164, in <module>
main()
SystemExit: 1
|
180405ebff545236908246d13b0d4813
|
{
"intermediate": 0.4291462004184723,
"beginner": 0.4000389873981476,
"expert": 0.17081484198570251
}
|
39,300
|
What Would Hypothetical 1986, 1989, 1991, 1994, And 1998 Versions of ChatGPT Look Like?
|
99396530b9814c6c8ec536f86ad16a93
|
{
"intermediate": 0.34779593348503113,
"beginner": 0.2530195415019989,
"expert": 0.3991844654083252
}
|
39,301
|
What Would Hypothetical 1997, 2000, 2002, 2005, And 2007 Versions Of Discord Look Like?
|
9a1c02cec5490e7a963c14d24a7086ad
|
{
"intermediate": 0.3694698214530945,
"beginner": 0.35738733410835266,
"expert": 0.27314284443855286
}
|
39,302
|
What Would An Hypothetical 1997 Version Of Discord Look Like?
|
a8f1ee78b6d694a246526bf2dd9dea59
|
{
"intermediate": 0.3868827819824219,
"beginner": 0.33453911542892456,
"expert": 0.27857810258865356
}
|
39,303
|
import json
import re
import sys
# Define regular expressions for the tokens
token_patterns = [
(r'#[^\n]', None), # comments (ignored; match till end of line)
(r'\s+', None), # whitespace (ignored)
(r'[', 'LBRACKET'),
(r']', 'RBRACKET'),
(r'{', 'LBRACE'),
(r'}', 'RBRACE'),
(r'%{', 'LMAPBRACE'),
(r'=>', 'ARROW'),
(r':', 'COLON'), # A standalone colon
(r',', 'COMMA'),
(r'(true|false)', 'BOOLEAN'),
(r'(0|[1-9]\d)', 'INTEGER'),
(r':[A-Za-z_]\w*', 'ATOM'), # For :atom syntax
(r'[A-Za-z_]\w*', 'KEY'), # For keys in key-value pairs
]
# Compile the regular expressions and make a lookup table
token_regex = '|'.join('(?:%s)' % pattern if name is None else '(?P<%s>%s)' % (name, pattern) for pattern, name in token_patterns)
token_re = re.compile(token_regex)
class TokenizerException(Exception): pass
class ParserException(Exception): pass
def tokenize(text):
pos = 0
while pos < len(text):
match = token_re.match(text, pos)
if not match:
raise TokenizerException(f'Illegal character {text[pos]} at index {pos}')
pos = match.end()
groupdict = match.groupdict()
for name, value in groupdict.items():
if value is not None:
tok_type = name
tok_value = value
yield tok_type, tok_value
break
# Define a node structure to represent the parse tree
class Node:
def __init__(self, kind, value):
self.kind = kind
self.value = value
def to_json(self):
return { "%k": self.kind, "%v": self.value }
class Parser:
def __init__(self, tokens):
self.tokens = iter(tokens)
self.current_token = None
self.next_token()
def next_token(self):
try:
self.current_token = next(self.tokens)
except StopIteration:
self.current_token = None
def parse(self):
result = self.parse_sentence()
if self.current_token is not None:
raise ParserException('Unexpected token at the end')
return result
def parse_sentence(self):
nodes = []
while self.current_token is not None and self.current_token[0] not in {'RBRACE', 'RBRACKET'}:
node = self.parse_data_literal()
nodes.append(node)
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
return nodes
def parse_data_literal(self):
if self.current_token[0] == 'LBRACKET':
return self.parse_list()
elif self.current_token[0] == 'LBRACE':
return self.parse_tuple()
elif self.current_token[0] == 'LMAPBRACE':
return self.parse_map()
elif self.current_token[0] == 'INTEGER':
value = int(self.current_token[1].replace('_', ''))
self.next_token()
return Node('int', value)
elif self.current_token[0] == 'ATOM':
value = self.current_token[1]
self.next_token()
return Node('atom', value)
elif self.current_token[0] == 'BOOLEAN':
value = self.current_token[1] == 'true'
self.next_token()
return Node('bool', value)
elif self.current_token[0] == 'KEY':
value = ':' + self.current_token[1][:-1]
self.next_token()
return Node('atom', value)
else:
raise ParserException(f'Unexpected token {self.current_token[1]}')
def parse_list(self):
items = []
self.next_token() # Skip '['
while self.current_token is not None and self.current_token[0] != 'RBRACKET':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACKET':
raise ParserException('List not properly terminated with ]')
self.next_token() # Skip ']'
return Node('list', [item.to_json() for item in items])
def parse_tuple(self):
items = []
self.next_token() # Skip '{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Tuple not properly terminated with }')
self.next_token() # Skip '}'
return Node('tuple', [item.to_json() for item in items])
def parse_map(self):
items = []
self.next_token() # Skip '%{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
key = self.parse_data_literal()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
value = self.parse_data_literal()
items.append([key.to_json(), value.to_json()])
elif self.current_token and self.current_token[0] == 'ARROW':
self.next_token() # Skip '=>'
value = self.parse_data_literal()
items.append([key.to_json(), value.to_json()])
else:
raise ParserException('Invalid map entry format, expected ":" or "=>"')
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Map not properly terminated with }')
self.next_token() # Skip '}'
return Node('map', items)
def main():
try:
# Prompt for input
text = input("Enter your input: ")
tokens = tokenize(text)
parser = Parser(tokens)
result = parser.parse()
json_output = json.dumps([node.to_json() for node in result], separators=(',', ':'))
print(json_output)
except TokenizerException as e:
sys.stderr.write(str(e) + '\n')
raise
except ParserException as e:
sys.stderr.write(str(e) + '\n')
raise
if __name__ == '__main__':
main()
Modify the program such that it can give correct outputs of totally-empty test, empty-with-comment test, empty-multi-comment test, single-atom test, single-int. test, extended-ints test, multi-bool test, true-false test, multi-primitives test, empty-lists test, simple-lists test, empty-tuples test, simple-tuples test, empty-maps test, simple-maps test, compound-data test, miss-delim error, miss-delim2 error, truefalse error, extra-comma error, extra-close-delim error, bad-int error
|
598ed760e4b0de996a28f52684e9212c
|
{
"intermediate": 0.33814775943756104,
"beginner": 0.48883944749832153,
"expert": 0.17301279306411743
}
|
39,304
|
i want to upload image from s3 and show them in good structure html befor processing it after "@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
try:
available_model_names = MODELS.keys() # Get model names from MODELS dictionary
print(f"Available Models: {available_model_names}")
# It's good practice not to print output directly in production. Use a logger instead.
available_images = []
response = requests.get(wordpress_url_post)
if response.status_code == 200:
print(response.json())
json_data = response.json()
for item in json_data:
if item.get("Status") and item["Status"]["status"] == "Unprocessed":
image_url = item.get("Image URL") and item["Image URL"]["Image_url"]
if image_url:
available_images.append(image_url)
return templates.TemplateResponse("upload_form.html", {"request": request, "model_names": available_model_names, "images": available_images})
except Exception as e:
# Handle errors
return templates.TemplateResponse("error_template.html", {"request": request, "error_message": str(e)})
|
dbb8e80f13765e9d5e3c4eed361022b8
|
{
"intermediate": 0.3389919698238373,
"beginner": 0.5379790663719177,
"expert": 0.12302900850772858
}
|
39,305
|
need to extract "isOpenMTubes" only from this class "<body class="logged-out isOpenMTubes">" with ublock. any ideas?
|
1b422c2b030e8ae6a91c5f303c07663d
|
{
"intermediate": 0.3592800498008728,
"beginner": 0.39993157982826233,
"expert": 0.24078838527202606
}
|
39,306
|
need to extract "isOpenMTubes" only from this class "<body class="logged-out isOpenMTubes">" with ublock. output rule
|
0feacc21983f19c2b7dd3c5ef9d32d69
|
{
"intermediate": 0.3073904514312744,
"beginner": 0.530957043170929,
"expert": 0.16165247559547424
}
|
39,307
|
this doesn’t extract “isOpenMTubes” only from "<body class="logged-out isOpenMTubes">". try to think before your output a new ublock rule: ##div:has(body[class^="isOpenMTubes"])
|
cfd8cbc274942f0581da8338ad833d4a
|
{
"intermediate": 0.22394375503063202,
"beginner": 0.6308566331863403,
"expert": 0.14519955217838287
}
|
39,308
|
ublock rule to extract “isOpenMTubes” only from. try to think before your output a new ublock rule: <body class="logged-out isOpenMTubes">
|
25b34054ff5292776589410fcdb6581b
|
{
"intermediate": 0.40300366282463074,
"beginner": 0.23622725903987885,
"expert": 0.36076900362968445
}
|
39,309
|
ublock rule to extract “isOpenMTubes” only from. try to think before your output a new ublock rule: <body class="logged-out isOpenMTubes">
|
b8630deea9bb75be1a7ba4ba3df95883
|
{
"intermediate": 0.40300366282463074,
"beginner": 0.23622725903987885,
"expert": 0.36076900362968445
}
|
39,310
|
ublock rule to extract “isOpenMTubes” only from. try to think before your output a new ublock rule: <body class="logged-out isOpenMTubes">
|
d7a53fde77393d96a48c9df7213b498e
|
{
"intermediate": 0.40300366282463074,
"beginner": 0.23622725903987885,
"expert": 0.36076900362968445
}
|
39,311
|
How do I scale this image properly to fit on full screen: <img src="radioshack-logo.png" alt="Radioshack logo" width="30" height="24">
|
1078e6267ae667f3466b09f8f48f372a
|
{
"intermediate": 0.3466613292694092,
"beginner": 0.23228572309017181,
"expert": 0.4210529625415802
}
|
39,312
|
make this professional '<body>
<h1>Available Images</h1>
<form action="{{ url_for('process_selected_images') }}" method="post">
<ul>
{% for image_name, image_data in images.items() %}
<li>
<input type="checkbox" name="selected_images" value="{{ image_name }}">
<img src="data:image/jpeg;base64,{{ image_data }}" alt="{{ image_name }}">
<p>{{ image_name }}</p>
</li>
{% endfor %}
</ul>
<button type="submit">Process Selected Images</button>
</form>
</body>
|
9977221e0d5f3d770ec7a8e2f1c86341
|
{
"intermediate": 0.3571600019931793,
"beginner": 0.2884627878665924,
"expert": 0.3543771803379059
}
|
39,313
|
In the following navbar, how do I change the code so the navbar closes when I click on 'disabled'
<nav class="navbar navbar-expand-lg bg-light">
<div class="container-fluid">
<a class="navbar-brand" href="#">Navbar</a>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNavAltMarkup" aria-controls="navbarNavAltMarkup" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbarNavAltMarkup">
<div class="navbar-nav">
<a class="nav-link active" aria-current="page" href="#">Home</a>
<a class="nav-link" href="#">Store Locator</a>
<a class="nav-link" href="#">Steve's Workbench</a>
<a class="nav-link" href="#">RadioShack Services</a>
<a class="nav-link" href="#">Electronic Repair</a>
<a class="nav-link" href="#">About RadioShack</a>
<a class="nav-link" href="#">Product Support</a>
<a class="nav-link disabled">Disabled</a>
</div>
</div>
</div>
</nav>
|
1f061b65d4c351882dfca316d38e9002
|
{
"intermediate": 0.5344710350036621,
"beginner": 0.2983018159866333,
"expert": 0.16722716391086578
}
|
39,314
|
How come the following link in the navbar doesn't work and doesn't link to twitter?
<nav class="navbar navbar-expand-lg bg-light">
<div class="container-fluid">
<a class="navbar-brand" href="#">Navbar</a>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNavAltMarkup" aria-controls="navbarNavAltMarkup" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbarNavAltMarkup">
<div class="navbar-nav">
<a class="nav-link active" aria-current="page" href="#">Home</a>
<a class="nav-link" href="#https://twitter.com/arthoefootjob/status/1757387247106576892?t=lkhpM3XRGEDDy5txda3zFw&s=19">Store Locator</a>
<a class="nav-link" href="#">Steve's Workbench</a>
<a class="nav-link" href="#">RadioShack Services</a>
<a class="nav-link" href="#">Electronic Repair</a>
<a class="nav-link" href="#">About RadioShack</a>
<a class="nav-link" href="#">Steve's Workbench</a>
</div>
</div>
</div>
</nav>
|
35a8fe48f3edf3f17e8781d9509cf012
|
{
"intermediate": 0.4236734211444855,
"beginner": 0.44517552852630615,
"expert": 0.13115103542804718
}
|
39,315
|
How come the following link in the navbar doesn’t work and doesn’t link to my other site?
<div class="collapse navbar-collapse" id="navbarNavAltMarkup">
<div class="navbar-nav">
<a class="nav-link active" aria-current="page" href="#">Home</a>
<a class="nav-link" href="#Test2.html">Store Locator</a>
<a class="nav-link" href="#">Steve's Workbench</a>
<a class="nav-link" href="#">RadioShack Services</a>
<a class="nav-link" href="#">Electronic Repair</a>
<a class="nav-link" href="#">About RadioShack</a>
<a class="nav-link" href="#">Steve's Workbench</a>
</div>
</div>
</div>
|
c23eef94453b52290304ba409ca28e9e
|
{
"intermediate": 0.5563234686851501,
"beginner": 0.2732878625392914,
"expert": 0.1703886240720749
}
|
39,316
|
import json
import re
import sys
# Define regular expressions for the tokens
token_patterns = [
(r'#[^\n]', None), # comments (ignored; match till end of line)
(r'\s+', None), # whitespace (ignored)
(r'[', 'LBRACKET'),
(r']', 'RBRACKET'),
(r'{', 'LBRACE'),
(r'}', 'RBRACE'),
(r'%{', 'LMAPBRACE'),
(r'=>', 'ARROW'),
(r':', 'COLON'), # A standalone colon
(r',', 'COMMA'),
(r'(true|false)', 'BOOLEAN'),
(r'(0|[1-9][0-9_]*)', 'INTEGER'), # Corrected INTEGER pattern
(r':[A-Za-z_]\w*', 'ATOM'), # For :atom syntax
(r'[A-Za-z_]\w*', 'KEY'), # For keys in key-value pairs
]
# Create a combined regex pattern for tokenization
regex_parts = []
for pattern, name in token_patterns:
if name: # Named groups for all token types except comments and whitespaces
regex_parts.append(f'(?P<{name}>{pattern})')
else: # Non-capturing group for comments and whitespaces to ignore them
regex_parts.append(f'(?:{pattern})')
token_regex = '|'.join(regex_parts)
token_re = re.compile(token_regex)
class TokenizerException(Exception): pass
class ParserException(Exception): pass
def tokenize(text):
pos = 0
while pos < len(text):
match = token_re.match(text, pos)
if not match:
raise TokenizerException(f'Illegal character {text[pos]!r} at index {pos}')
pos = match.end()
if match.lastgroup: # If the pattern is not ignored
kind = match.lastgroup
value = match.group(kind)
if kind == 'INTEGER': # Remove underscores for INTEGER tokens
value = value.replace('', '')
yield kind, value
# Define a node structure to represent the parse tree
class Node:
def __init__(self, kind, value):
self.kind = kind
self.value = value
def to_json(self):
if self.kind == "map":
# For maps, return a dictionary with the key-value pairs
return {"%k": self.kind, "%v": {item[0].to_json()["%v"]: item[1].to_json() for item in self.value}}
else:
# For other types, return a dictionary with "%k" and "%v"
return {"%k": self.kind, "%v": self.value}
class Parser:
def __init__(self, tokens):
self.tokens = iter(tokens)
self.current_token = None
self.next_token()
def next_token(self):
try:
self.current_token = next(self.tokens)
except StopIteration:
self.current_token = None
def parse(self):
result = self.parse_sentence()
if self.current_token is not None:
raise ParserException('Unexpected token at the end')
return result
def parse_sentence(self):
nodes = []
while self.current_token is not None and self.current_token[0] not in {'RBRACE', 'RBRACKET'}:
node = self.parse_data_literal()
nodes.append(node)
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
return nodes
def parse_data_literal(self):
if self.current_token[0] == 'LBRACKET':
return self.parse_list()
elif self.current_token[0] == 'LBRACE':
return self.parse_tuple()
elif self.current_token[0] == 'LMAPBRACE':
return self.parse_map()
elif self.current_token[0] == 'INTEGER':
value = int(self.current_token[1])
self.next_token()
return Node('int', value)
elif self.current_token[0] == 'ATOM':
value = self.current_token[1][1:] # Strip the leading colon
self.next_token()
return Node('atom', value)
elif self.current_token[0] == 'BOOLEAN':
value = self.current_token[1] == 'true'
self.next_token()
return Node('bool', value)
elif self.current_token[0] == 'KEY':
value = self.current_token[1]
self.next_token()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
return Node('key', value)
return Node('string', value)
else:
raise ParserException(f'Unexpected token {self.current_token[1]}')
def parse_list(self):
items = []
self.next_token() # Skip '['
while self.current_token is not None and self.current_token[0] != 'RBRACKET':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACKET':
raise ParserException('List not properly terminated with ]')
self.next_token() # Skip ']'
return Node('list', [item.to_json() for item in items])
def parse_tuple(self):
items = []
self.next_token() # Skip '{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Tuple not properly terminated with }')
self.next_token() # Skip '}'
return Node('tuple', [item.to_json() for item in items])
def parse_map(self):
items = []
self.next_token() # Skip '%{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
key = self.parse_data_literal()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
value = self.parse_data_literal()
items.append([key.to_json(), value.to_json()])
elif self.current_token and self.current_token[0] == 'ARROW':
self.next_token() # Skip '=>'
value = self.parse_data_literal()
items.append([key.to_json(), value.to_json()])
else:
raise ParserException('Invalid map entry format, expected ":" or "=>"')
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Map not properly terminated with }')
self.next_token() # Skip '}'
return Node('map', items)
def main():
try:
# Prompt for input
text = input("Enter your input: ")
tokens = tokenize(text)
parser = Parser(tokens)
result = parser.parse()
json_output = json.dumps([node.to_json() for node in result], separators=(',', ':'))
print(json_output)
except TokenizerException as e:
sys.stderr.write(str(e) + '\n')
raise
except ParserException as e:
sys.stderr.write(str(e) + '\n')
raise
if __name__ == '__main__':
main()
PS C:\Users\apoor\OneDrive\Documents> c:; cd 'c:\Users\apoor\OneDrive\Documents'; & 'c:\Users\apoor\AppData\Local\Microsoft\WindowsApps\python3.11.exe' 'c:\Users\apoor\.vscode\extensions\ms-python.debugpy-2024.0.0-win32-x64\bundled\libs\debugpy\adapter/../..\debugpy\launcher' '57883' '--' 'C:\Users\apoor\OneDrive\Documents\script.py'
Enter your input: :atom
Unexpected token : Exception has occurred: ParserException
Unexpected token :
File "C:\Users\apoor\OneDrive\Documents\script.py", line 124, in parse_data_literal
raise ParserException(f'Unexpected token {self.current_token[1]}')
File "C:\Users\apoor\OneDrive\Documents\script.py", line 91, in parse_sentence
node = self.parse_data_literal()
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\apoor\OneDrive\Documents\script.py", line 83, in parse
result = self.parse_sentence()
^^^^^^^^^^^^^^^^^^^^^ When i give the input as :atom, I want the output as [
{
"%k": "atom",
"%v": ":atom"
}
]
|
171f7f5817168cb1440c865b79ba75e9
|
{
"intermediate": 0.35877832770347595,
"beginner": 0.5047271251678467,
"expert": 0.13649460673332214
}
|
39,317
|
How to fix broken packet dependency in Linux?
|
bb42bf166ea098d0079e2d1adec4981c
|
{
"intermediate": 0.4416733980178833,
"beginner": 0.21953484416007996,
"expert": 0.33879172801971436
}
|
39,318
|
import json
import re
import sys
# Define regular expressions for the tokens
token_patterns = [
(r'#[^\n]*', None), # comments (ignored; match till end of line)
(r'\s+', None), # whitespace (ignored)
(r'[', 'LBRACKET'),
(r']', 'RBRACKET'),
(r'{', 'LBRACE'),
(r'}', 'RBRACE'),
(r'%{', 'LMAPBRACE'),
(r'=>', 'ARROW'),
(r',', 'COMMA'),
(r'(true|false)', 'BOOLEAN'),
(r'(0|[1-9][0-9_]*)', 'INTEGER'), # Corrected INTEGER pattern
(r':[A-Za-z_]\w*', 'ATOM'), # For :atom syntax
(r'[A-Za-z_]\w*', 'KEY'), # For keys in key-value pairs
]
# Create a combined regex pattern for tokenization
regex_parts = []
for pattern, name in token_patterns:
if name: # Named groups for all token types except comments and whitespaces
regex_parts.append(f'(?P<{name}>{pattern})')
else: # Non-capturing group for comments and whitespaces to ignore them
regex_parts.append(f'(?:{pattern})')
token_regex = '|'.join(regex_parts)
token_re = re.compile(token_regex)
class TokenizerException(Exception): pass
class ParserException(Exception): pass
def tokenize(text):
pos = 0
while pos < len(text):
match = token_re.match(text, pos)
if not match:
raise TokenizerException(f'Illegal character {text[pos]!r} at index {pos}')
pos = match.end()
if match.lastgroup: # Only yield tokens with a named group (ignore None group names)
yield match.lastgroup, match.group(match.lastgroup)
# Define a node structure to represent the parse tree
class Node:
def __init__(self, kind, value):
self.kind = kind
self.value = value
def to_json(self):
if self.kind == "map":
return {"%k": self.kind, "%v": {k.to_json()["%v"]: v.to_json() for k, v in self.value}}
elif self.kind == "list":
return {"%k": self.kind, "%v": [item.to_json() for item in self.value]}
else:
return {"%k": self.kind, "%v": self.value}
class Parser:
def __init__(self, tokens):
self.tokens = iter(tokens)
self.current_token = None
self.next_token()
def next_token(self):
try:
self.current_token = next(self.tokens)
except StopIteration:
self.current_token = None
def parse(self):
result = self.parse_sentence()
if self.current_token is not None:
raise ParserException('Unexpected token at the end')
return result
def parse_sentence(self):
nodes = []
while self.current_token is not None:
node = self.parse_data_literal()
nodes.append(node)
if self.current_token and self.current_token[0] in {'COMMA', 'COLON', 'ARROW'}: # Consume any commas or colons between literals
self.next_token()
return nodes
def parse_data_literal(self):
if self.current_token[0] == 'LBRACKET':
return self.parse_list()
elif self.current_token[0] == 'LBRACE':
return self.parse_tuple()
elif self.current_token[0] == 'LMAPBRACE':
return self.parse_map()
elif self.current_token[0] == 'INTEGER':
value = int(self.current_token[1])
self.next_token()
return Node('int', value)
elif self.current_token[0] == 'ATOM':
# Keep the leading colon as required
value = self.current_token[1]
self.next_token()
return Node('atom', value)
elif self.current_token[0] == 'BOOLEAN':
value = self.current_token[1] == 'true'
self.next_token()
return Node('bool', value)
elif self.current_token[0] == 'KEY':
value = self.current_token[1]
self.next_token()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
return Node('key', value)
return Node('string', value)
else:
raise ParserException(f'Unexpected token {self.current_token[1]}')
def parse_list(self):
self.next_token() # Skip '['
items = []
while self.current_token and self.current_token[0] != 'RBRACKET':
item = self.parse_data_literal()
items.append(item)
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip the comma
if not self.current_token or self.current_token[0] != 'RBRACKET':
raise ParserException('List not properly terminated with ]')
self.next_token() # Skip ']'
return Node('list', items)
def parse_tuple(self):
items = []
self.next_token() # Skip '{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Tuple not properly terminated with }')
self.next_token() # Skip '}'
return Node('tuple', [item.to_json() for item in items])
def parse_map(self):
items = []
self.next_token() # Skip '%{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
key = self.parse_data_literal()
if self.current_token and self.current_token[0] in {'COLON', 'ARROW'}:
self.next_token() # Skip the token that comes after the key (':' or '=>')
value = self.parse_data_literal()
items.append((key, value))
else:
raise ParserException('Invalid map entry format, expected ":" or "=>"')
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Map not properly terminated with }')
self.next_token() # Skip '}'
return Node('map', items)
def main():
try:
text = input("Enter your input: ")
# Tokenize the text
tokens = list(tokenize(text))
# Check if the tokens list is empty after tokenization
if not tokens:
print(json.dumps([])) # Output an empty list
else:
parser = Parser(tokens)
result = parser.parse()
# Generate the JSON output from the parse result
json_output = json.dumps([node.to_json() for node in result], separators=(',', ':'))
print(json_output)
except TokenizerException as e:
sys.stderr.write(str(e) + '\n')
except ParserException as e:
sys.stderr.write(str(e) + '\n')
if __name__ == '__main__':
main()
Enter your input: %{ a: 22, :b => 33 }
Illegal character ':' at index 4 the output should be {
"%k": "map",
"%v": [
[
{
"%k": "atom",
"%v": ":a"
},
{
"%k": "int",
"%v": 22
}
],
[
{
"%k": "atom",
"%v": ":b"
},
{
"%k": "int",
"%v": 33
}
]
]
}
|
e6a6f668cc32f8d249676a04888f80ce
|
{
"intermediate": 0.3873734772205353,
"beginner": 0.4985673427581787,
"expert": 0.11405915766954422
}
|
39,319
|
import json
import re
import sys
# Define regular expressions for the tokens
token_patterns = [
(r'#[^\n]*', None), # comments (ignored; match till end of line)
(r'\s+', None), # whitespace (ignored)
(r'[', 'LBRACKET'),
(r']', 'RBRACKET'),
(r'{', 'LBRACE'),
(r'}', 'RBRACE'),
(r'%{', 'LMAPBRACE'),
(r'=>', 'ARROW'),
(r',', 'COMMA'),
(r'(true|false)', 'BOOLEAN'),
(r'(0|[1-9][0-9_]*)', 'INTEGER'), # Corrected INTEGER pattern
(r':[A-Za-z_]\w*', 'ATOM'), # For :atom syntax
(r'[A-Za-z_]\w*', 'KEY'), # For keys in key-value pairs
]
# Create a combined regex pattern for tokenization
regex_parts = [f'(?P<{name}>{pattern})' if name else f'(?:{pattern})' for pattern, name in token_patterns]
token_regex = '|'.join(regex_parts)
token_re = re.compile(token_regex)
class TokenizerException(Exception): pass
class ParserException(Exception): pass
def tokenize(text):
pos = 0
while pos < len(text):
match = token_re.match(text, pos)
if not match:
raise TokenizerException(f'Illegal character {text[pos]!r} at index {pos}')
pos = match.end()
if match.lastgroup: # Only yield tokens with a named group (ignore None group names)
yield match.lastgroup, match.group(match.lastgroup)
# Define a node structure to represent the parse tree
class Node:
def __init__(self, kind, value):
self.kind = kind
self.value = value
def to_json(self):
if self.kind == "map":
return {"%k": self.kind, "%v": {k.to_json()["%v"]: v.to_json() for k, v in self.value}}
elif self.kind == "list":
return {"%k": self.kind, "%v": [item.to_json() for item in self.value]}
else:
return {"%k": self.kind, "%v": self.value}
class Parser:
def __init__(self, tokens):
self.tokens = iter(tokens)
self.current_token = None
self.next_token()
def next_token(self):
try:
self.current_token = next(self.tokens)
except StopIteration:
self.current_token = None
def parse(self):
result = self.parse_sentence()
if self.current_token is not None:
raise ParserException('Unexpected token at the end')
return result
def parse_sentence(self):
nodes = []
while self.current_token is not None:
node = self.parse_data_literal()
nodes.append(node)
if self.current_token and self.current_token[0] in {'COMMA', 'COLON', 'ARROW'}: # Consume any commas or colons between literals
self.next_token()
return nodes
def parse_data_literal(self):
if self.current_token[0] == 'LBRACKET':
return self.parse_list()
elif self.current_token[0] == 'LBRACE':
return self.parse_tuple()
elif self.current_token[0] == 'LMAPBRACE':
return self.parse_map()
elif self.current_token[0] == 'INTEGER':
value = int(self.current_token[1])
self.next_token()
return Node('int', value)
elif self.current_token[0] == 'ATOM':
# Keep the leading colon as required
value = self.current_token[1]
self.next_token()
return Node('atom', value)
elif self.current_token[0] == 'BOOLEAN':
value = self.current_token[1] == 'true'
self.next_token()
return Node('bool', value)
elif self.current_token[0] == 'KEY':
value = self.current_token[1]
self.next_token()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
return Node('key', value)
return Node('string', value)
else:
raise ParserException(f'Unexpected token {self.current_token[1]}')
def parse_list(self):
self.next_token() # Skip '['
items = []
while self.current_token and self.current_token[0] != 'RBRACKET':
item = self.parse_data_literal()
items.append(item)
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip the comma
if not self.current_token or self.current_token[0] != 'RBRACKET':
raise ParserException('List not properly terminated with ]')
self.next_token() # Skip ']'
return Node('list', items)
def parse_tuple(self):
items = []
self.next_token() # Skip '{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Tuple not properly terminated with }')
self.next_token() # Skip '}'
return Node('tuple', [item.to_json() for item in items])
def parse_map(self):
self.next_token() # Skip '%{'
map_items = {}
while self.current_token and self.current_token[0] != 'RBRACE':
# Expect a key
if self.current_token[0] not in {'ATOM', 'KEY'}:
raise ParserException(f'Expected map key, found {self.current_token}')
# Extract the key value
key = self.current_token[1] if self.current_token[0] == 'ATOM' else ':{}'.format(self.current_token[1])
self.next_token() # Consume the key
# Expect '=>' or ':'
if self.current_token and self.current_token[0] not in {'ARROW', 'COLON'}:
raise ParserException(f'Expected “=>” or “:”, found {self.current_token}')
self.next_token() # Consume '=>' or ':'
# Parse the value
value = self.parse_data_literal()
map_items[key] = value
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip comma
if not self.current_token or self.current_token[0] != 'RBRACE':
raise ParserException('Map not properly terminated with }')
self.next_token() # Skip the '}'
return Node('map', map_items)
def main():
try:
text = input("Enter your input: ")
# Tokenize the text
tokens = list(tokenize(text))
# Check if the tokens list is empty after tokenization
if not tokens:
print(json.dumps([])) # Output an empty list
else:
parser = Parser(tokens)
result = parser.parse()
# Generate the JSON output from the parse result
json_output = json.dumps([node.to_json() for node in result], separators=(',', ':'))
print(json_output)
except TokenizerException as e:
sys.stderr.write(str(e) + '\n')
except ParserException as e:
sys.stderr.write(str(e) + '\n')
if __name__ == '__main__':
main()
You are required to submit a zip-archive to gradescope (you can access gradescope from the Tools menu on the brightspace navigation for this course).
Unpacking the archive should result in at least the following files:
prj1-sol/README
prj1-sol/elixir-data.ebnf
prj1-sol/make.sh
prj1-sol/run.sh
The unpacked prj1-sol directory should contain all other source files needed to build and run your project using sh make.sh followed by sh run.sh.
For example, a sample zip file for a project implemented in python contains:
$ unzip -l py/prj1-sol.zip
Archive: py/prj1-sol.zip
Length Date Time Name
--------- ---------- ----- ----
1127 2024-01-08 03:54 prj1-sol/README
406 2024-02-10 17:25 prj1-sol/elixir-data.ebnf
5305 2024-01-08 14:33 prj1-sol/elixir-data.py
198 2024-01-08 03:54 prj1-sol/make.sh
242 2024-01-24 20:37 prj1-sol/run.sh
--------- -------
7278 5 files
A do-zip.sh script has been created to facilitate creating the zip file. Please read the comments at the start of the script. A suitable .zipignore has been added to the directory containing the starter files. This .zipignore is suitable for a python or javascript project. If you have used java, then one is available in java-zipignore.
You should verify your zip file before submission. Simply unzip it into a tmp directory:
$ mkdir -p ~/tmp
$ cd ~/tmp
$ unzip PATH_TO_ZIP_FILE # maybe something like ~/i?44/submit/prj1-sol.zip
This should create a prj1-sol directory in ~/tmp, Go into ~/tmp/prj1-sol and you should be able to successfully run sh make.sh followed by sh run.sh.
When you submit your zip file to gradescope, it will run automated tests:
Verify that all required files have been included.
Build your submission using sh make.sh.
Use your run.sh to Run some tests (note that the actual project grading may use additional tests).
If a step fails, then subsequent steps are aborted. I need to submit the code written using the instructions above. tell me how to do it.
|
c2d094355bc341d4fa5ab55c25ed12aa
|
{
"intermediate": 0.29566916823387146,
"beginner": 0.5639790892601013,
"expert": 0.14035186171531677
}
|
39,320
|
import json
import re
import sys
# Define regular expressions for the tokens
token_patterns = [
(r'#[^\n]*', None), # comments (ignored; match till end of line)
(r'\s+', None), # whitespace (ignored)
(r'[', 'LBRACKET'),
(r']', 'RBRACKET'),
(r'{', 'LBRACE'),
(r'}', 'RBRACE'),
(r'%{', 'LMAPBRACE'),
(r'=>', 'ARROW'),
(r',', 'COMMA'),
(r'(true|false)', 'BOOLEAN'),
(r'(0|[1-9][0-9_]*)', 'INTEGER'), # Corrected INTEGER pattern
(r':[A-Za-z_]\w*', 'ATOM'), # For :atom syntax
(r'[A-Za-z_]\w*', 'KEY'), # For keys in key-value pairs
]
# Create a combined regex pattern for tokenization
regex_parts = [f'(?P<{name}>{pattern})' if name else f'(?:{pattern})' for pattern, name in token_patterns]
token_regex = '|'.join(regex_parts)
token_re = re.compile(token_regex)
class TokenizerException(Exception): pass
class ParserException(Exception): pass
def tokenize(text):
pos = 0
while pos < len(text):
match = token_re.match(text, pos)
if not match:
raise TokenizerException(f'Illegal character {text[pos]!r} at index {pos}')
pos = match.end()
if match.lastgroup: # Only yield tokens with a named group (ignore None group names)
yield match.lastgroup, match.group(match.lastgroup)
# Define a node structure to represent the parse tree
class Node:
def __init__(self, kind, value):
self.kind = kind
self.value = value
def to_json(self):
if self.kind == "map":
return {"%k": self.kind, "%v": {k.to_json()["%v"]: v.to_json() for k, v in self.value}}
elif self.kind == "list":
return {"%k": self.kind, "%v": [item.to_json() for item in self.value]}
else:
return {"%k": self.kind, "%v": self.value}
class Parser:
def __init__(self, tokens):
self.tokens = iter(tokens)
self.current_token = None
self.next_token()
def next_token(self):
try:
self.current_token = next(self.tokens)
except StopIteration:
self.current_token = None
def parse(self):
result = self.parse_sentence()
if self.current_token is not None:
raise ParserException('Unexpected token at the end')
return result
def parse_sentence(self):
nodes = []
while self.current_token is not None:
node = self.parse_data_literal()
nodes.append(node)
if self.current_token and self.current_token[0] in {'COMMA', 'COLON', 'ARROW'}: # Consume any commas or colons between literals
self.next_token()
return nodes
def parse_data_literal(self):
if self.current_token[0] == 'LBRACKET':
return self.parse_list()
elif self.current_token[0] == 'LBRACE':
return self.parse_tuple()
elif self.current_token[0] == 'LMAPBRACE':
return self.parse_map()
elif self.current_token[0] == 'INTEGER':
value = int(self.current_token[1])
self.next_token()
return Node('int', value)
elif self.current_token[0] == 'ATOM':
# Keep the leading colon as required
value = self.current_token[1]
self.next_token()
return Node('atom', value)
elif self.current_token[0] == 'BOOLEAN':
value = self.current_token[1] == 'true'
self.next_token()
return Node('bool', value)
elif self.current_token[0] == 'KEY':
value = self.current_token[1]
self.next_token()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
return Node('key', value)
return Node('string', value)
else:
raise ParserException(f'Unexpected token {self.current_token[1]}')
def parse_list(self):
self.next_token() # Skip '['
items = []
while self.current_token and self.current_token[0] != 'RBRACKET':
item = self.parse_data_literal()
items.append(item)
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip the comma
if not self.current_token or self.current_token[0] != 'RBRACKET':
raise ParserException('List not properly terminated with ]')
self.next_token() # Skip ']'
return Node('list', items)
def parse_tuple(self):
items = []
self.next_token() # Skip '{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Tuple not properly terminated with }')
self.next_token() # Skip '}'
return Node('tuple', [item.to_json() for item in items])
def parse_map(self):
self.next_token() # Skip '%{'
map_items = {}
while self.current_token and self.current_token[0] != 'RBRACE':
# Expect a key
if self.current_token[0] not in {'ATOM', 'KEY'}:
raise ParserException(f'Expected map key, found {self.current_token}')
# Extract the key value
key = self.current_token[1] if self.current_token[0] == 'ATOM' else ':{}'.format(self.current_token[1])
self.next_token() # Consume the key
# Expect '=>' or ':'
if self.current_token and self.current_token[0] not in {'ARROW', 'COLON'}:
raise ParserException(f'Expected “=>” or “:”, found {self.current_token}')
self.next_token() # Consume '=>' or ':'
# Parse the value
value = self.parse_data_literal()
map_items[key] = value
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip comma
if not self.current_token or self.current_token[0] != 'RBRACE':
raise ParserException('Map not properly terminated with }')
self.next_token() # Skip the '}'
return Node('map', map_items)
def main():
try:
text = input("Enter your input: ")
# Tokenize the text
tokens = list(tokenize(text))
# Check if the tokens list is empty after tokenization
if not tokens:
print(json.dumps([])) # Output an empty list
else:
parser = Parser(tokens)
result = parser.parse()
# Generate the JSON output from the parse result
json_output = json.dumps([node.to_json() for node in result], separators=(',', ':'))
print(json_output)
except TokenizerException as e:
sys.stderr.write(str(e) + '\n')
except ParserException as e:
sys.stderr.write(str(e) + '\n')
if __name__ == '__main__':
main()
I want to submit this code project. prj1-sol/README
prj1-sol/elixir-data.ebnf
prj1-sol/make.sh
prj1-sol/run.sh write these files for me with respect to the above code
|
e93b310325877c956f41b17761cfbead
|
{
"intermediate": 0.29566916823387146,
"beginner": 0.5639790892601013,
"expert": 0.14035186171531677
}
|
39,321
|
import json
import re
import sys
# Define regular expressions for the tokens
token_patterns = [
(r'#[^\n]*', None), # comments (ignored; match till end of line)
(r'\s+', None), # whitespace (ignored)
(r'[', 'LBRACKET'),
(r']', 'RBRACKET'),
(r'{', 'LBRACE'),
(r'}', 'RBRACE'),
(r'%{', 'LMAPBRACE'),
(r'=>', 'ARROW'),
(r',', 'COMMA'),
(r'(true|false)', 'BOOLEAN'),
(r'(0|[1-9][0-9_]*)', 'INTEGER'), # Corrected INTEGER pattern
(r':[A-Za-z_]\w*', 'ATOM'), # For :atom syntax
(r'[A-Za-z_]\w*', 'KEY'), # For keys in key-value pairs
]
# Create a combined regex pattern for tokenization
regex_parts = [f'(?P<{name}>{pattern})' if name else f'(?:{pattern})' for pattern, name in token_patterns]
token_regex = '|'.join(regex_parts)
token_re = re.compile(token_regex)
class TokenizerException(Exception): pass
class ParserException(Exception): pass
def tokenize(text):
pos = 0
while pos < len(text):
match = token_re.match(text, pos)
if not match:
raise TokenizerException(f'Illegal character {text[pos]!r} at index {pos}')
pos = match.end()
if match.lastgroup: # Only yield tokens with a named group (ignore None group names)
yield match.lastgroup, match.group(match.lastgroup)
# Define a node structure to represent the parse tree
class Node:
def __init__(self, kind, value):
self.kind = kind
self.value = value
def to_json(self):
if self.kind == "map":
return {"%k": self.kind, "%v": {k.to_json()["%v"]: v.to_json() for k, v in self.value}}
elif self.kind == "list":
return {"%k": self.kind, "%v": [item.to_json() for item in self.value]}
else:
return {"%k": self.kind, "%v": self.value}
class Parser:
def __init__(self, tokens):
self.tokens = iter(tokens)
self.current_token = None
self.next_token()
def next_token(self):
try:
self.current_token = next(self.tokens)
except StopIteration:
self.current_token = None
def parse(self):
result = self.parse_sentence()
if self.current_token is not None:
raise ParserException('Unexpected token at the end')
return result
def parse_sentence(self):
nodes = []
while self.current_token is not None:
node = self.parse_data_literal()
nodes.append(node)
if self.current_token and self.current_token[0] in {'COMMA', 'COLON', 'ARROW'}: # Consume any commas or colons between literals
self.next_token()
return nodes
def parse_data_literal(self):
if self.current_token[0] == 'LBRACKET':
return self.parse_list()
elif self.current_token[0] == 'LBRACE':
return self.parse_tuple()
elif self.current_token[0] == 'LMAPBRACE':
return self.parse_map()
elif self.current_token[0] == 'INTEGER':
value = int(self.current_token[1])
self.next_token()
return Node('int', value)
elif self.current_token[0] == 'ATOM':
# Keep the leading colon as required
value = self.current_token[1]
self.next_token()
return Node('atom', value)
elif self.current_token[0] == 'BOOLEAN':
value = self.current_token[1] == 'true'
self.next_token()
return Node('bool', value)
elif self.current_token[0] == 'KEY':
value = self.current_token[1]
self.next_token()
if self.current_token and self.current_token[0] == 'COLON':
self.next_token() # Skip ':'
return Node('key', value)
return Node('string', value)
else:
raise ParserException(f'Unexpected token {self.current_token[1]}')
def parse_list(self):
self.next_token() # Skip '['
items = []
while self.current_token and self.current_token[0] != 'RBRACKET':
item = self.parse_data_literal()
items.append(item)
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip the comma
if not self.current_token or self.current_token[0] != 'RBRACKET':
raise ParserException('List not properly terminated with ]')
self.next_token() # Skip ']'
return Node('list', items)
def parse_tuple(self):
items = []
self.next_token() # Skip '{'
while self.current_token is not None and self.current_token[0] != 'RBRACE':
items.append(self.parse_data_literal())
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip commas
if self.current_token is None or self.current_token[0] != 'RBRACE':
raise ParserException('Tuple not properly terminated with }')
self.next_token() # Skip '}'
return Node('tuple', [item.to_json() for item in items])
def parse_map(self):
self.next_token() # Skip '%{'
map_items = {}
while self.current_token and self.current_token[0] != 'RBRACE':
# Expect a key
if self.current_token[0] not in {'ATOM', 'KEY'}:
raise ParserException(f'Expected map key, found {self.current_token}')
# Extract the key value
key = self.current_token[1] if self.current_token[0] == 'ATOM' else ':{}'.format(self.current_token[1])
self.next_token() # Consume the key
# Expect '=>' or ':'
if self.current_token and self.current_token[0] not in {'ARROW', 'COLON'}:
raise ParserException(f'Expected “=>” or “:”, found {self.current_token}')
self.next_token() # Consume '=>' or ':'
# Parse the value
value = self.parse_data_literal()
map_items[key] = value
if self.current_token and self.current_token[0] == 'COMMA':
self.next_token() # Skip comma
if not self.current_token or self.current_token[0] != 'RBRACE':
raise ParserException('Map not properly terminated with }')
self.next_token() # Skip the '}'
return Node('map', map_items)
def main():
try:
text = input("Enter your input: ")
# Tokenize the text
tokens = list(tokenize(text))
# Check if the tokens list is empty after tokenization
if not tokens:
print(json.dumps([])) # Output an empty list
else:
parser = Parser(tokens)
result = parser.parse()
# Generate the JSON output from the parse result
json_output = json.dumps([node.to_json() for node in result], separators=(',', ':'))
print(json_output)
except TokenizerException as e:
sys.stderr.write(str(e) + '\n')
except ParserException as e:
sys.stderr.write(str(e) + '\n')
if __name__ == '__main__':
main()
Unfortunately, gradescope removes execute permissions when unpacking the zip archive. So you cannot set up your run.sh script to run interpeter files directly. So for example, a run.sh which contains the line $dir/elixir-literal.mjs will not work, you will need to use node $dir/elixir-literal.mjs. Similarly, for Python use python3 $dir/elixir-literal.py instead of $dir/elixir-literal.py.
|
b05167de32f195012e9d04dc18897842
|
{
"intermediate": 0.29566916823387146,
"beginner": 0.5639790892601013,
"expert": 0.14035186171531677
}
|
39,322
|
how to make zip file in vs code in terminal
|
45cd911502437fbbbad5b8748b567136
|
{
"intermediate": 0.3381805419921875,
"beginner": 0.4049447178840637,
"expert": 0.25687479972839355
}
|
39,323
|
check this code:
pub fn parse_consensus<'a>(
contents: &'a str,
) -> Result<HashMap<String, HashMap<String, Vec<(u32, u32)>>>, &'static str> {
// let chr_to_gene: Arc<DashMap<String, Vec<(u32, u32, String)>>> = Arc::new(DashMap::new());
let tracks = contents
.par_lines()
.map(|line| {
if !line.starts_with("#") {
Some(Record::new(line))
} else {
None
}
})
.filter_map(|x| x)
.try_fold(
|| HashMap::new(),
|mut acc, record| {
let record = record.unwrap();
let gene = record.id.split(".").collect::<Vec<&str>>()[1].to_string();
let gene_acc = acc.entry(gene).or_insert(HashMap::new());
// make record.id the key for the gene_acc map and record.coords the value
gene_acc.entry(record.id).or_insert(record.coords);
Ok(acc)
},
)
.try_reduce(
|| HashMap::new(),
|mut acc, mut map| {
acc.extend(map.drain());
Ok(acc)
},
);
tracks
}
why this is the output?:
Ok({"SIPA1L2": {"ENST00000675685.SIPA1L2.53": [(18742287, 18742434), (18743974, 18744056), (18744815, 18744942), (18753411, 18753543), (18756796, 18757028), (18759085, 18759236), (18763457, 18763682), (18766325, 18766699), (18768478, 18768582), (18768865, 18768966), (18769813, 18769897), (18772071, 18772326), (18785306, 18785581), (18788053, 18788630), (18791894, 18792052), (18796160, 18796264), (18800301, 18800476), (18804044, 18804233), (18805732, 18805866), (18807913, 18809381)]}})
when there it should be 3 transcript keys, is this code creating a new hashmap each time?
|
dfaea6993b7cab5abad388dc32736eb7
|
{
"intermediate": 0.32730427384376526,
"beginner": 0.4437272250652313,
"expert": 0.2289685606956482
}
|
39,324
|
#include <stdio.h>
#include <stdlib.h>
// Structure for a node in the binary tree
struct Node {
int data;
struct Node *left, *right;
};
// Function to create a new node
struct Node* createNode(int data) {
struct Node* newNode = (struct Node*)malloc(sizeof(struct Node));
newNode->data = data;
newNode->left = newNode->right = NULL;
return newNode;
}
// Function to construct the family tree from level order traversal
struct Node* constructFamilyTree(int level_order[], int n) {
// Create the root node
struct Node* root = createNode(level_order[0]);
// Create a queue to keep track of parent nodes
struct Node* queue[n];
int front = 0, rear = 0;
queue[rear++] = root;
int i = 1;
while (i < n) {
// Get the front node from the queue
struct Node* parent = queue[front++];
// Create a new node for the left child
struct Node* leftChild = createNode(level_order[i++]);
parent->left = leftChild;
// Enqueue the left child
queue[rear++] = leftChild;
if (i == n)
break;
// Create a new node for the right child
struct Node* rightChild = createNode(level_order[i++]);
parent->right = rightChild;
// Enqueue the right child
queue[rear++] = rightChild;
}
return root;
}
// Function to find the minimum number of phones needed
int findMinimumPhones(struct Node* root) {
if (root == NULL)
return 0;
// Initialize the count to zero
int count = 0;
// Perform a level order traversal of the tree
struct Node* queue[root->data];
int front = 0, rear = 0;
queue[rear++] = root;
while (front < rear) {
// Get the front node from the queue
struct Node* node = queue[front++];
// If the current node or any of its children have a phone, no additional phone is needed
if (node->data == 1 || (node->left != NULL && node->left->data == 1) || (node->right != NULL && node->right->data == 1))
continue;
// If the current node does not have a phone and none of its children have a phone, an additional phone is needed
count++;
// Enqueue the left and right children, if they exist
if (node->left != NULL)
queue[rear++] = node->left;
if (node->right != NULL)
queue[rear++] = node->right;
}
return count;
}
int main() {
int n;
printf("Enter the size of the level order traversal: ");
scanf("%d", &n);
int level_order[n];
printf("Enter the level order traversal: ");
for (int i = 0; i < n; i++)
scanf("%d", &level_order[i]);
struct Node* root = constructFamilyTree(level_order, n);
int minPhones = findMinimumPhones(root);
printf("Minimum number of phones needed: %d\n", minPhones);
return 0;
}
|
6dba5bfd862a7203d9c1186478b5afe6
|
{
"intermediate": 0.3353975713253021,
"beginner": 0.43876317143440247,
"expert": 0.22583922743797302
}
|
39,325
|
Hi
|
ba28fdd7d277c8ffc11de5a12ae9f0ec
|
{
"intermediate": 0.33010533452033997,
"beginner": 0.26984941959381104,
"expert": 0.400045245885849
}
|
39,326
|
Subtask 1 (50 points)
You will be given the level order traversal of the family tree as an array level order of size. You have to construct this family tree from scratch, assuming it is a binary tree. Note that the level order traversal will comprise 1s and 0s where 1 denotes a valid node and 0 denotes a null node.
Having created the tree of family members, you have to find the minimum number of phones that needs to be distributed among them, given that each member who is given a phone can share their phone with their parent or their children. However, if someone is not given a phone, they can only be shared with ie they cannot share the phone further with others
1/0 Format
Input Format
n
level_order[0] level_order[1]... level order[n-1]
The first line contains in the length of array level onder
The second line contains level onder, which is the level-order traversal of the family tree.
Output Format
m
The output will just be the minimum number of phones required.
|
8c84403efaa2d8a06990b039d89e690c
|
{
"intermediate": 0.3947465419769287,
"beginner": 0.2677716910839081,
"expert": 0.3374817967414856
}
|
39,327
|
I have an Android app that uses Firebase Firestore database. I want to add a functionality (a different Class, activity) for the user to message me (the developar). How should I do it? There is a button in the side menu called Message Me. Can you code the .xml and Java (including the firebase integration) for this function? Or maybe if you can solve the problem that way, it’s okay if the app behind the interface in the background without the user knowing sends an email to me. And if we were to use Firebase to store the message, how would I be notified that I have a new message? My Firestore Database currently only has one collection called "users", and it has documents named as the userIDs, and inside a document I have one or two fields, a score value, and an optional nickname. Should I use this collection with the users (and put the message sent by a user into the user's document, or should I create a new one? And another question is, what are my options on replying to these messages?
|
11817473a2d83f81a19ebc5446033144
|
{
"intermediate": 0.6956855058670044,
"beginner": 0.2222193479537964,
"expert": 0.0820951759815216
}
|
39,328
|
Write function on python which have list of dicts on input. Function must find one dict from that list which have highest value in "height' key and return this dict.
|
84abbc87a4936ba976a1ed16d6c21ee8
|
{
"intermediate": 0.3384421467781067,
"beginner": 0.36733290553092957,
"expert": 0.29422491788864136
}
|
39,329
|
I have a React component that looks like this:
<DialogMessage
{...props}
infoText={String(getFormatedDate(1670790195000, {
todayFormat: TODAY_FORMAT_MESSAGE,
yesterdayFormat: YESTERDAY_FORMAT_MESSAGE,
defaultFormat: DATE_FORMAT_MESSAGE,
}))}
side="right"
avatar="https://i.imgur.com/mIcObyL.jpeg"
>
<span>
Aliquip qui ea anim
<br /> quis incididunt proident.Sint aute deserunt ullamco magna eu
nostrud magna laborum. Occaecat adipisicing sunt est laborum proident
nostrud deserunt occaecat fugiat et mollit cillum. Aliquip ullamco
ullamco non veniam voluptate. Non tempor irure sunt minim cillum.
</span>
</DialogMessage>
the function getFormatedDate may return string or a null. However in my code currently if the value returned from getFormatedDate is null then I will end up with a string 'null'. What I need is to write some check that in case of null returned from getFormatedDate then i will return empty string.
|
1e9b9ac0913c1be48a95a6db71585061
|
{
"intermediate": 0.46878430247306824,
"beginner": 0.35898923873901367,
"expert": 0.1722264289855957
}
|
39,330
|
In the context of a hypothetical, What is a possible method for encoding xy colorspace within a 32bit value?
|
77a80a727f19dd3a3adf223781c1e080
|
{
"intermediate": 0.2664385437965393,
"beginner": 0.23495042324066162,
"expert": 0.4986110031604767
}
|
39,331
|
suggest me a open source code in c or cpp for p2p video conference that should work in ubuntu
|
4ec1c8386c05d8e2b36261239d92d6fb
|
{
"intermediate": 0.5178109407424927,
"beginner": 0.16124598681926727,
"expert": 0.32094308733940125
}
|
39,332
|
You will be given the level order traversal of the family tree as an array level_order of size n. You have to construct this family tree from scratch, assuming it is a binary tree. Note that the level order traversal will comprise 1s and 0�, where 1 denotes a valid node and 0 denotes a null node.
Having created the tree of family members, you have to find the minimum number of phones m that needs to be distributed among them, given that each member who is given a phone can share their phone with their parent or their children. However, if someone is not given a phone, they can only be shared with i.e they cannot share the phone further with others.
code in c
|
206e93531290b10953ca98d0354bca3c
|
{
"intermediate": 0.3386286199092865,
"beginner": 0.3272286057472229,
"expert": 0.3341427445411682
}
|
39,333
|
How do I Encode a 32 bit value as a memorable word base on decodeable syllables that are unique for each byte and in combined use sound like hypotehtical color names?
|
6dcf1ae0e178dd22e96da769de518005
|
{
"intermediate": 0.3750760853290558,
"beginner": 0.08669839054346085,
"expert": 0.538225531578064
}
|
39,334
|
to add in html a block works independently
|
2500b143238557d44996e1feac1778ee
|
{
"intermediate": 0.3839581608772278,
"beginner": 0.30258628726005554,
"expert": 0.3134555220603943
}
|
39,335
|
I have a chain of classes to develop. I know what classes came before and what classes should be next to be executed, because it's always the same classes. The next class is only executed when the anterior class is. And remember, I don't need them all to be executed every time. Every class has a condition to be completed before passing to another class, and it cannot pass to another class before metting this condition, not jump to other class because there is an order. How do I develop those? Give me a Python example.
|
90dc3d714d0fd3e2890f7b7319215e72
|
{
"intermediate": 0.3383621871471405,
"beginner": 0.43880951404571533,
"expert": 0.22282831370830536
}
|
39,336
|
Subtask 1 (50 points)
You will be given the level order traversal of the family tree as an array level order of size n. You have to construct this family tree from scratch, assuming it is a binary tree. Note that the level order traversal will comprise Is and Os, where 1 denotes a valid node and 0 denotes a null node.
Having created the tree of family members, you have to find the minimum number of phones m that needs to be distributed among them, given that each member who is given a phone can share their phone with their parent or their children. However, if someone is not given a phone, they can only be shared with ie they cannot share the phone further with others.
1/O Format
Input Format
n
level_order [0] level_order[1] ... level_order [n-1]
The first line contains n, the length of array level_order.
The second line contains level_order], which is the level-order traversal of the family tree.
Output Format
Сору
The output will just be m, the minimum number of phones required.
|
ae9468c3eafb9365cd4fc20a6ce3cd4d
|
{
"intermediate": 0.36912840604782104,
"beginner": 0.28577736020088196,
"expert": 0.3450942635536194
}
|
39,337
|
test
|
52654dfee784c1458ca54abe49ffa81e
|
{
"intermediate": 0.3229040801525116,
"beginner": 0.34353747963905334,
"expert": 0.33355844020843506
}
|
39,338
|
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
contract Fund {
mapping(address => uint) public shares;
function withdraw() public {
if (payable(msg.sender).send(shares[msg.sender])) {
shares[msg.sender] = 0;
}
}
}
Would you feel secure placing your funds in this cntract? If so, kindly why?
|
2324666c9e577747f07518d0ff166daa
|
{
"intermediate": 0.4547071158885956,
"beginner": 0.27929723262786865,
"expert": 0.2659956216812134
}
|
39,339
|
Subtask 1 (50 points)
You will be given the level order traversal of the family tree as an array level order of size n. You have to construct this family tree from scratch, assuming it is a binary tree. Note that the level order traversal will comprise Is and Os, where 1 denotes a valid node and 0 denotes a null node.
Having created the tree of family members, you have to find the minimum number of phones m that needs to be distributed among them, given that each member who is given a phone can share their phone with their parent or their children. However, if someone is not given a phone, they can only be shared with ie they cannot share the phone further with others.
1/O Format
Input Format
n
level_order [0] level_order[1] … level_order [n-1]
The first line contains n, the length of array level_order.
The second line contains level_order], which is the level-order traversal of the family tree.
Output Format
m
The output will just be m, the minimum number of phones required.
I NEED A SOLUTION IN C LANGUAGE
|
6da5f87950c373cf1a9bd70b59c2ffa5
|
{
"intermediate": 0.2828879654407501,
"beginner": 0.4803158640861511,
"expert": 0.23679618537425995
}
|
39,340
|
In Linux, Tell me how to list all devices that have the minor number 36 in the system
|
26741283619d79dc315e50805d9d1b6f
|
{
"intermediate": 0.3812563419342041,
"beginner": 0.27462249994277954,
"expert": 0.3441210985183716
}
|
39,341
|
Please, complete my function
REPLACING_SERVICE_PRICES = {
'Отключение мочевины Adblue': 20000,
}
def replace_service_prices(position):
# replace price field of service (of prositions['services'] list) dict if name field contains inside REPLACING_SERVICE_PRICES .
# ...
|
7522fa3fd14ec70bfc1f0b6a52579167
|
{
"intermediate": 0.32996711134910583,
"beginner": 0.44800063967704773,
"expert": 0.22203223407268524
}
|
39,342
|
i have a df with columns B and C with index A. I want to keep the index column as it is and insert a new column called ID that has the row indices.
|
d37b0cfdd8789843c404233ec44a331f
|
{
"intermediate": 0.3888125717639923,
"beginner": 0.25902464985847473,
"expert": 0.35216274857521057
}
|
39,343
|
Please, complete last function. Return only completed function!
def map_services(services):
for service in services:
if service['name'] in SERVICES_MAP:
service['id'] = SERVICES_MAP[service['name']]
def replace_service_prices(services):
for service in services:
for service_name, replacement_price in REPLACING_SERVICE_PRICES.items():
if service_name in service['name']:
service['price'] = replacement_price
return services
def extract_chip_tuning_price(services):
# extract 'price' of 'Чип тюнинг' service
# remove this service from services
return services, chip_tuning_service
|
3b0f35c820c3f10a4a74961c7598060e
|
{
"intermediate": 0.3909474015235901,
"beginner": 0.34359464049339294,
"expert": 0.26545798778533936
}
|
39,344
|
python lang puct list
|
3536418e388617ae5787e9f76ebdeb65
|
{
"intermediate": 0.1654563546180725,
"beginner": 0.6916513442993164,
"expert": 0.14289230108261108
}
|
39,345
|
i want to send the images selected with content not just names to post parrt "<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Image Selection</title>
<style>
body {
font-family: 'Open Sans', sans-serif;
background-color: #1f1f1f; /* Dark Grey */
margin: 0;
padding: 40px;
color: #ddd; /* Light Grey */
display: flex;
justify-content: center; /* Center everything horizontally */
align-items: center; /* Center everything vertically */
height: 100vh; /* Full viewport height */
}
.container {
width: 80%;
max-width: 1200px; /* Max width for the container */
}
h2 {
font-weight: 600;
color: #4CAF50; /* Green */
text-align: center;
}
.result-container {
display: flex;
flex-wrap: wrap;
gap: 20px;
justify-content: center; /* Center items */
}
.image-container {
flex-basis: calc(33% - 20px); /* Adjusting width */
background-color: #333; /* Dark Grey */
padding: 10px;
border-radius: 8px;
box-shadow: 0px 4px 8px rgba(0,0,0,0.2);
}
img {
width: 100%;
max-width: 200px; /* Limiting image width */
height: auto;
border: 1px solid #444; /* Darker Grey */
border-radius: 4px;
margin-top: 10px;
box-shadow: 0px 4px 8px rgba(0,0,0,0.05);
}
.select-all-container {
margin-top: 20px;
text-align: center;
}
.select-all-button {
background-color: #4CAF50; /* Green */
color: white;
padding: 10px 20px;
border: none;
border-radius: 4px;
cursor: pointer;
transition: background-color 0.3s ease-in-out;
}
.select-all-button:hover {
background-color: #45a049; /* Darker Green */
}
.process-button {
background-color: #4CAF50; /* Green */
color: white;
padding: 10px 20px;
border: none;
border-radius: 4px;
cursor: pointer;
transition: background-color 0.3s ease-in-out;
}
.process-button:hover {
background-color: #45a049; /* Darker Green */
}
</style>
</head>
<body>
<div class="container">
<h2>Available Images</h2>
<form action="{{ url_for('process_images') }}" method="post" enctype="multipart/form-data" id="imageForm">
<div class="result-container">
{# Loop through the images dictionary available in the template context #}
{% for image_name, image_data in images.items() %}
<div class="image-container">
<input type="checkbox" id="image-{{ image_name }}" name="selected_images" value="{{ image_name }}">
<label for="image-{{ image_name }}">
<img src="data:image/jpeg;base64,{{ image_data }}" alt="Thumbnail of {{ image_name }}">
</label>
</div>
{% endfor %}
</div>
<div class="select-all-container">
<button type="button" class="select-all-button" onclick="selectAll()">Select All</button>
<button type="button" class="process-button" onclick="processImages()">Process Selected Images</button>
</div>
</form>
</div>
<script>
function selectAll() {
var checkboxes = document.querySelectorAll('input[name="selected_images"]');
checkboxes.forEach(function(checkbox) {
checkbox.checked = true;
});
}
function processImages() {
var selectedImages = [];
var checkboxes = document.querySelectorAll('input[name="selected_images"]:checked');
checkboxes.forEach(function(checkbox) {
selectedImages.push(checkbox.value);
});
fetch("/process_images", {
method: "POST",
headers: {
"Content-Type": "application/json"
},
body: JSON.stringify(selectedImages)
})
.then(response => {
if (!response.ok) {
throw new Error("Network response was not ok");
}
return response.json();
})
.then(data => {
// Handle the response data
console.log(data);
})
.catch(error => {
console.error("There was a problem with the fetch operation:", error);
});
}
</script>
</body>
</html>
|
5b56a44f8fafd736fdf75b67e7eae98b
|
{
"intermediate": 0.35560011863708496,
"beginner": 0.44069892168045044,
"expert": 0.20370091497898102
}
|
39,346
|
hello
|
bb77a5498e5ec902a6826e3632bb9b43
|
{
"intermediate": 0.32064199447631836,
"beginner": 0.28176039457321167,
"expert": 0.39759764075279236
}
|
39,347
|
How to implement conditional wait in posix thread
|
41b8c0f7d87939a83433b96052923caa
|
{
"intermediate": 0.3002641201019287,
"beginner": 0.11750547587871552,
"expert": 0.5822303891181946
}
|
39,348
|
how to print defined macros in c
|
81cde6042932cb921d73da67baeb459e
|
{
"intermediate": 0.39361608028411865,
"beginner": 0.3282512426376343,
"expert": 0.2781326174736023
}
|
39,349
|
If the Digimon Lopmon was a creature in a hypothetical D&D 5e setting/homebrew, what would its statblock look like?
|
f4d95148209c3af2ef8a12ecd3d5abad
|
{
"intermediate": 0.36741408705711365,
"beginner": 0.3219277560710907,
"expert": 0.3106580674648285
}
|
39,350
|
В каком случае последовательность действий попадает в ветку else в данном коде?
for i, lvl_threshold in enumerate(levels[1:], start=1): #тут важно начинать со второго элемента в списке, так как у первого уровня трешхолд 0
if orders < df.loc[(df['sprint_id'] == sprint), lvl_threshold].iloc[0]:
level = i
break
else:
if not np.isnan(sprint_order_count):
level = 4
else:
level = np.nan
|
90385cba9579b99c47e1ea5bcab248b4
|
{
"intermediate": 0.3184046447277069,
"beginner": 0.42776671051979065,
"expert": 0.25382864475250244
}
|
39,351
|
launch doker-compose_local.yml'
|
283e492b667302582dc50adc31f01b3b
|
{
"intermediate": 0.37639519572257996,
"beginner": 0.29483503103256226,
"expert": 0.328769713640213
}
|
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