### Install Hugging Face Transformers

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo.ipynb

Installs the Hugging Face Transformers library from its GitHub repository. This is often a prerequisite for NLP tasks.

```python
!pip install git+https://github.com/huggingface/transformers.git
```

--------------------------------

### Load Model and Tokenizer with Device Selection

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo.ipynb

Loads the tokenizer and causal language model, selecting the appropriate device (GPU or CPU) based on availability. Ensure PyTorch and Transformers are installed.

```python
if torch.cuda.is_available():
    device = torch.device("cuda")
    print("Using GPU")
else:
    device = torch.device("cpu")
    print("Using CPU")


model_name = "shailja/CodeGen_2B_Verilog"

tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog")

model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog").to(device)
```

--------------------------------

### Load BigQuery Extension

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Loads the BigQuery extension for interactive environments like Jupyter notebooks. Ensure you have the google-cloud-bigquery library installed.

```python
#https://stackoverflow.com/questions/36183486/importerror-no-module-named-google

%load_ext google.cloud.bigquery
```

--------------------------------

### Get Current Working Directory

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Prints the current working directory of the Python script.

```python
pwd
```

--------------------------------

### Verilog Wire Assignment Module

Source: https://context7.com/shailja-thakur/vgen/llms.txt

A minimal Verilog module that assigns its input directly to its output, acting as a wire. This is an example of a basic prompt.

```verilog
// ── BASIC: Wire Assignment ──────────────────────────────────────────────────

// prompt1_wire_assign.v  (minimal comment only)
// This is a module that assigns the output to the input
module wire_assign( input in, output out );

// answer_wire_assign.v  (reference solution)
// Create a module with one input and one output that behaves like a wire
module wire_assign( input in, output out );
    assign out = in;
endmodule
```

--------------------------------

### Save Matplotlib Plot to SVG

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Saves the current Matplotlib plot to an SVG file with a transparent background. Ensure Matplotlib is installed and imported.

```python
plt.savefig('github-language-popularity.svg',transparent=True);
```

--------------------------------

### Save Language Popularity Plot

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

This Python code snippet saves a matplotlib plot to a file named 'github-language-popularity.png'. Ensure matplotlib is installed and configured correctly.

```python
plt.savefig('github-language-popularity.png');
```

--------------------------------

### Querying Verilog Models via Fauxpilot API

Source: https://context7.com/shailja-thakur/vgen/llms.txt

This Python snippet demonstrates how to use the `openai` client to interact with a local Fauxpilot server for batched Verilog completion. Ensure Fauxpilot is running and configured.

```python
import openai

# Point the client at the local fauxpilot server
openai.api_key = "dummy"
openai.api_base = "http://127.0.0.1:5000/v1"

# Request 3 completions for a half-adder prompt
result = openai.Completion.create(
    engine="codegen",
    prompt="//module half adder",
    max_tokens=100,
    temperature=0.1,
    n=3,
    top_p=1.0,
    stop=["endmodule"]
)

for i, choice in enumerate(result["choices"]):
    print(f"--- Completion {i+1} ---")
    print(choice["text"] + "endmodule")
```

--------------------------------

### Sample Verilog Code Generation with HuggingFace

Source: https://github.com/shailja-thakur/vgen/blob/main/README.md

Use this snippet to generate Verilog code by sampling from a fine-tuned CodeGen LLM using the HuggingFace transformers library. Ensure you have a GPU available for inference.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Prompt
prompt = "//module half adder "

# Load model and tokenizer
model_name = "shailja/CodeGen_2B_Verilog"
tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog")
model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog").to(device)

# Sample
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
sample = model.generate(input_ids, max_length=128, temperature=0.5, top_p=0.9)

print(tokenizer.decode(sample[0], truncate_before_pattern=[r"endmodule"]) + "endmodule")
```

--------------------------------

### Initialize BigQuery Client

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Initializes a BigQuery client object to interact with the BigQuery API.

```python
bqclient = bigquery.Client()
```

--------------------------------

### Sample Verilog code from prompt

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo_notebook.ipynb

Generates Verilog code based on a given prompt using the loaded model. It decodes the generated token IDs into human-readable Verilog code, truncating before the 'endmodule' pattern.

```python
prompt = "//a half adder module "
# prompt="// Design a 2-to-1 multiplexer.\n module mux( \n    input [4:0] a, b,\n    input sel,\n    output [4:0] out );\n // When sel=0, assign a to out. \n// When sel=1, assign b to out."
n_steps = 1
n = 5
end_pattern='endmodule'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

sample = model.generate(input_ids, max_length=128, temperature=0.5, top_p=0.9)
print(tokenizer.decode(sample[0], truncate_before_pattern=[r"endmodule"]) + end_pattern)
```

--------------------------------

### Verilog Moore State Machine - Template 2 (Some Detailed Comments)

Source: https://github.com/shailja-thakur/vgen/blob/main/prompts-and-testbenches/prompts-templates.txt

This template includes some detailed comments regarding state transitions and output logic, offering a balance between conciseness and clarity for the state machine's behavior.

```verilog
// This is a Moore state machine with two states 0 and 1, one input in, and one output out. Reset state is 0.
module simple_fsm(clk, reset, in, out);
input clk;
input reset;
input in;
output wire out;
reg present_state, next_state;
// Transition from state 0 to state 1 if in==0
// Transition from state 1 to state 0 if in==0
// out is high in state 0
```

--------------------------------

### Sample Verilog Code Generation

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo.ipynb

Generates Verilog code by sampling from the model. This method takes a prompt and generates a completion up to a specified length, truncating before a given pattern. Requires the model and tokenizer to be loaded.

```python
prompt = "//module half adder "
# prompt="// Design a 2-to-1 multiplexer.\n module mux( 
    input [4:0] a, b,
    input sel,
    output [4:0] out );
 // When sel=0, assign a to out. 
// When sel=1, assign b to out."
n_steps = 1
n = 5
end_pattern='endmodule'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
# Sample
sample = model.generate(input_ids, max_length=128, temperature=0.5, top_p=0.9)
print(tokenizer.decode(sample[0], truncate_before_pattern=[r"endmodule"]) + end_pattern)
```

--------------------------------

### Load Fine-Tuned CodeGen Model and Tokenizer

Source: https://context7.com/shailja-thakur/vgen/llms.txt

Loads a pre-trained CodeGen Verilog model and its corresponding tokenizer from HuggingFace Hub. Requires a GPU with sufficient VRAM. Select the 2B or 6B parameter model as needed.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Select device
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
print(f"Using device: {device}")

# Load 2B model (use "shailja/fine-tuned-codegen-6B-Verilog" for 6B)
model_name = "shailja/fine-tuned-codegen-2B-Verilog"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

print(f"Loaded model: {model_name}")
# Expected output:
# Using device: cuda
# Loaded model: shailja/fine-tuned-codegen-2B-Verilog
```

--------------------------------

### Query GitHub Repositories for Verilog Files

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

This query selects Verilog files from the GitHub repositories dataset, filtering for files containing 'endmodule' or 'always' and within a specific size range. It joins the files and contents tables.

```sql
SELECT f.repo_name, f.path, c.copies, c.size, c.content
 FROM `bigquery-public-data.github_repos.files` AS f 
 JOIN `bigquery-public-data.github_repos.contents` AS c 
 ON f.id = c.id 
 WHERE 
 NOT c.binary 
 AND ((f.path LIKE '%.v') 
 AND (c.content LIKE '%endmodule%' 
 OR c.content LIKE '%always%')
 AND (c.size BETWEEN 10 
 AND 1048575))
```

--------------------------------

### Clone fauxpilot for LLM Sampling

Source: https://github.com/shailja-thakur/vgen/blob/main/README.md

Clone the customized fauxpilot repository to enable sampling from fine-tuned LLMs. This method is preferred for memory and compute optimization, leading to low-latency inferencing.

```shell
git clone https://github.com/shailja-thakur/fauxpilot_changes.git
```

--------------------------------

### Import BigQuery Client

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Imports the BigQuery client library for programmatic access to BigQuery services.

```python
from google.cloud import bigquery
```

--------------------------------

### Verilog Half Adder Module

Source: https://context7.com/shailja-thakur/vgen/llms.txt

A Verilog module for a half adder, demonstrating an intermediate prompt style. It includes input ports 'a' and 'b', and output ports 'cout' and 'sum'.

```verilog
// ── INTERMEDIATE: Half Adder ─────────────────────────────────────────────────

// prompt1_half_adder.v
// This is a half adder.
module half_adder(
input a, b,
output cout, sum );
```

--------------------------------

### Debug Verilog generation with per-token sampling

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo_notebook.ipynb

This snippet allows for debugging the generation process by examining the probabilities of the next token at each step. It iteratively predicts tokens, stores the top choices, and appends the most probable token to the input sequence until 'endmodule' is detected.

```python
# max_length = 

# stop_word = 
iteration = dict()
with torch.no_grad():
    while True:
        
        iteration["Input"] = input_ids
        
        output = model(input_ids)

        # select logits of the first batch and the last token and apply softmax
        next_token_logits = output.logits[0, -1, :]
        next_token_probs = torch.softmax(next_token_logits, dim=-1)
        sorted_ids = torch.argsort(next_token_probs, dim=-1, descending=True)
        # print(sorted_ids)
        
        
        # store tokens with highest probability
        for choice_idx in range(n):
            token_id = sorted_ids[choice_idx]
            
            token_prob = next_token_probs[token_id].cpu().numpy()
            # print(token_id, token_prob)
            # print(tokenizer.decode(token_id.view(1,-1)))
            
            token_choice = (
                f"{tokenizer.decode(token_id)} ({100*token_prob:.2f}%)"
            )
            
            iteration[f"{choice_idx+1}"] = token_choice

        #Append predictions to list
        input_ids = torch.cat([input_ids, sorted_ids[None,0, None]], dim=-1)
        
        # condition checks on stop_words detected
        if 'endmodule' in tokenizer.batch_decode(input_ids)[0]: break
        
print(tokenizer.batch_decode(input_ids)[0])
```

--------------------------------

### Run Verilog RTL Code Generation Inference

Source: https://github.com/shailja-thakur/vgen/blob/main/README.md

Use this Python code within an IPython environment to set up the OpenAI API client and perform inference for Verilog RTL code generation. Ensure the API key and base URL are correctly configured for your local VGen instance.

```python
import openai

openai.api_key = 'dummy'
openai.api_base = 'http://127.0.0.1:5000/v1'

result = openai.Completion.create(engine='codegen', prompt='//module half adder', max_tokens=100, temperature=0.1, n=3,top_p=1.0, stop=["endmodule"])
```

--------------------------------

### Verilog Moore State Machine - General Template

Source: https://github.com/shailja-thakur/vgen/blob/main/prompts-and-testbenches/prompts-templates.txt

This is a general template for a Moore state machine in Verilog, including detailed comments explaining the module's functionality and state transitions.

```verilog
// This is a Moore state machine with two states 0 and 1, one input in, and one output out. Reset state is 0.
module simple_fsm(clk, reset, in, out);
input clk;
input reset;
input in;
output wire out;
reg present_state, next_state;
// In state 0, if in=1, stay in state 0. In state 0, if in=0, go to state 1
// In state 1, if in=1, stay in state 1. In state 1, if in=0, go to state 0
// out=1 in state 0 and out=0 in state 1
```

--------------------------------

### Check available device info

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo_notebook.ipynb

Determines if a CUDA-enabled GPU is available and sets the device accordingly. This is crucial for performance, as GPU acceleration is significantly faster for deep learning tasks.

```python
if torch.cuda.is_available():
    device = torch.device("cuda")
    #print("Using GPU")
else:
    device = torch.device("cpu")
    #print("Using CPU")
```

--------------------------------

### Generate Verilog Module from Natural-Language Prompt

Source: https://context7.com/shailja-thakur/vgen/llms.txt

Generates Verilog code from a natural-language prompt using a fine-tuned CodeGen model. Uses temperature sampling and stops generation at the 'endmodule' keyword. Ensure the prompt is in a comment style for optimal results.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog")
model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog").to(device)

# Natural-language prompt (comment style used during fine-tuning)
prompt = "//module half adder "

input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

# Generate up to 128 tokens
sample = model.generate(input_ids, max_length=128, temperature=0.5, top_p=0.9)

result = tokenizer.decode(sample[0], truncate_before_pattern=[r"endmodule"]) + "endmodule"
print(result)

# Expected output:
# //module half adder
#
# module half_adder(
#     input a,
#     input b,
#     output sum,
#     output carry
#     );
#
# assign sum = a ^ b;
# assign carry = a & b;
#
# endmodule
```

--------------------------------

### Query Stack Overflow for Verilog Questions

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

This SQL query retrieves questions tagged with 'Verilog' from the Stack Overflow dataset, including their titles, bodies, and associated answers.

```sql
select q.tags question_tag, q.id question_id, q.title question_title, q.body question_body, a.id answer_id, a.body from `bigquery-public-data.stackoverflow.posts_questions` as q
left join
`bigquery-public-data.stackoverflow.posts_answers` as a
on a.parent_id = q.id
where regexp_contains(q.tags, 'Verilog');
```

--------------------------------

### Load model and tokenizer

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo_notebook.ipynb

Loads a pre-trained CodeGen model and its corresponding tokenizer from Hugging Face. The model is then moved to the determined device (GPU or CPU).

```python
model_name = "shailja/CodeGen_6B_Verilog"

tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-6B-Verilog")

model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-6B-Verilog").to(device)
```

--------------------------------

### Set Google Cloud Credentials

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Sets the environment variable for Google Cloud credentials. Replace 'my-project-348716-29f69b900233.json' with your actual service account key file path.

```python
#Your credentials to google cloud
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]=r"my-project-348716-29f69b900233.json"
# git token
TOKEN = ""  # Replace with your token
```

--------------------------------

### Plot GitHub Language Popularity (Verilog Focus)

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Generates a bar chart showing the popularity of programming languages from GitHub data, with a specific emphasis and annotation for Verilog. Requires pre-existing sorted_Languages_counts and total_count variables.

```python
total_count = sum(count for _, count in sorted_Languages_counts)

# Find the index of Verilog
verilog_index = next(i for i, v in enumerate(sorted_Languages_counts) if v[0] == 'Verilog')

# Number of initial languages to display
initial_display_count = 5

# Create a new list with the first few languages, placeholder, and Verilog
selected_languages_counts = sorted_Languages_counts[:initial_display_count] + [('...', 0), sorted_Languages_counts[verilog_index]]

# Unpack languages and counts
language, count = zip(*selected_languages_counts)
x_pos = np.arange(len(language))

# Plot
plt.figure(figsize=(7,5))
bars = plt.bar(x_pos, count, align='center', color=sns.color_palette("gist_rainbow", len(x_pos)))
plt.xticks(x_pos, language, rotation=15, fontsize=25)

# Annotate bars with percentage
for idx, bar in enumerate(bars):
    height = bar.get_height()
    percentage = (height / total_count) * 100
    if language[idx] == 'Verilog':  # Highlight Verilog
        plt.text(bar.get_x() + bar.get_width() / 2, height, f'{percentage:.2f}%', ha='center', va='bottom', fontsize=20, fontweight='bold', color='red')
    elif language[idx] != '...':  # Avoid annotating '...' 
        plt.text(bar.get_x() + bar.get_width() / 2, height, f'{percentage:.2f}%', ha='center', va='bottom', fontsize=15)

plt.show()
```

--------------------------------

### Import necessary libraries

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo_notebook.ipynb

Imports the PyTorch library for tensor operations and AutoTokenizer and AutoModelForCausalLM from Hugging Face Transformers for model and tokenizer handling.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
```

--------------------------------

### Per-Token Greedy Decoding for Verilog Generation

Source: https://context7.com/shailja-thakur/vgen/llms.txt

This Python snippet performs greedy decoding one token at a time, recording top candidate tokens and their probabilities. Generation stops when 'endmodule' is encountered. Requires `transformers` and `torch` libraries.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-6B-Verilog")
model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-6B-Verilog").to(device)

prompt = "//a half adder module "
n = 5  # number of top token candidates to track
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

iteration = dict()
with torch.no_grad():
    while True:
        output = model(input_ids)
        next_token_logits = output.logits[0, -1, :]
        next_token_probs = torch.softmax(next_token_logits, dim=-1)
        sorted_ids = torch.argsort(next_token_probs, dim=-1, descending=True)

        for choice_idx in range(n):
            token_id = sorted_ids[choice_idx]
            token_prob = next_token_probs[token_id].cpu().numpy()
            iteration[f"{choice_idx+1}"] = (
                f"{tokenizer.decode(token_id)} ({100 * token_prob:.2f}%)"
            )

        # Append the greedy best token
        input_ids = torch.cat([input_ids, sorted_ids[None, 0, None]], dim=-1)

        if "endmodule" in tokenizer.batch_decode(input_ids)[0]:
            break

print(tokenizer.batch_decode(input_ids)[0])
```

--------------------------------

### Prepare Data for Plotting

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Prepares language and count data for plotting the top 10 programming languages. Requires numpy, matplotlib, and seaborn libraries.

```python
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
language = list(zip(*sorted_Languages_counts[:10]))[0]
count = list(zip(*sorted_Languages_counts[:10]))[1]
x_pos = np.arange(len(language))
```

--------------------------------

### SQL Query for Verilog Answers

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Constructs a SQL query to retrieve answers containing 'Verilog' from the Stack Overflow dataset. This query joins questions and answers tables and filters by answer body content.

```sql
query_string = """
SELECT
p.id answer_id, p.body FROM `bigquery-public-data.stackoverflow.posts_answers` as p
WHERE p.body LIKE '%Verilog%'
"""
# where regexp_contains(q.tags, 'Verilog')
```

--------------------------------

### BigQuery SQL Query for Stack Overflow Verilog Questions

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Constructs a SQL query to retrieve URLs and view counts for Stack Overflow questions tagged with 'verilog' or containing specific Verilog keywords in their tags. This query is intended for use with Google BigQuery.

```sql
SELECT
    CONCAT(
        'https://stackoverflow.com/questions/',
        CAST(id as STRING))
    as url,
    view_count
FROM `bigquery-public-data.stackoverflow.posts_questions`
WHERE ((tags like '%verilog%')
or (tags like '%beginmodule%' and tags like '%endmodule%' and tags like '%always%')
)
ORDER BY view_count DESC
```

--------------------------------

### Verilog Signed Addition with Overflow Detection Module

Source: https://context7.com/shailja-thakur/vgen/llms.txt

An advanced Verilog module for signed addition of two 8-bit 2's complement numbers, including overflow detection. This uses an advanced prompt style.

```verilog
// ── ADVANCED: Signed Addition with Overflow Detection ───────────────────────

// prompt1_signed-addition-overflow.v
// This is a signed adder that adds two 8-bit 2's complement numbers.
// It also captures a signed overflow.
module signed_adder(input [7:0] a, input [7:0] b, output [7:0] s, output overflow );
```

--------------------------------

### Query GitHub Languages

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

This query selects the 'language' field from the GitHub repositories dataset. The 'language' field is a list of dictionaries, each containing a language name and its byte count.

```sql
SELECT language
            FROM `bigquery-public-data.github_repos.languages`
            
        
```

--------------------------------

### Import Libraries

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Imports necessary libraries for data manipulation and interacting with the operating system.

```python
import os
import pandas as pd
```

--------------------------------

### Token-by-Token Verilog Code Generation

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo.ipynb

Generates Verilog code one token at a time, selecting the most probable next token. This method allows for more control and inspection of the generation process. It requires the model and tokenizer to be loaded and operates within a `torch.no_grad()` context.

```python
iteration = dict()
with torch.no_grad():
    while True:
        
        iteration["Input"] = input_ids
        
        output = model(input_ids)

        # select logits of the first batch and the last token and apply softmax
        next_token_logits = output.logits[0, -1, :]
        next_token_probs = torch.softmax(next_token_logits, dim=-1)
        sorted_ids = torch.argsort(next_token_probs, dim=-1, descending=True)
        # print(sorted_ids)
        
        
        # store tokens with highest probability
        for choice_idx in range(n):
            token_id = sorted_ids[choice_idx]
            
            token_prob = next_token_probs[token_id].cpu().numpy()
            # print(token_id, token_prob)
            # print(tokenizer.decode(token_id.view(1,-1)))
            
            token_choice = (
                f"{tokenizer.decode(token_id)} ({100*token_prob:.2f}%)"
            )
            
            iteration[f"{choice_idx+1}"] = token_choice

        #Append predictions to list
        input_ids = torch.cat([input_ids, sorted_ids[None,0, None]], dim=-1)
        
        # condition checks on stop_words detected
        if 'endmodule' in tokenizer.batch_decode(input_ids)[0]: break
        
print(tokenizer.batch_decode(input_ids)[0])
```

--------------------------------

### Check GPU Availability

Source: https://github.com/shailja-thakur/vgen/blob/main/VGen_Demo.ipynb

Checks if a GPU is connected and available for use. It prints the GPU information if connected, otherwise indicates that no GPU is found.

```python
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
  print('Not connected to a GPU')
else:
  print(gpu_info)
```

--------------------------------

### Generate Plagiarism Pie Chart

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Creates a pie chart to visualize the proportion of 'Unique' and 'Plagiarised' content, with 'Plagiarised' highlighted. The chart is saved as a PNG file. Note: The `plt.show()` call is commented out.

```python
import matplotlib.pyplot as plt
explode = (0, 0.1)  # only "explode" the 2nd slice (i.e. 'Hogs')
labels = 'Unique', 'Plagiarised'
sizes = [15, 30]
fig = plt.gcf()
fig, ax = plt.subplots()
plt.figure(figsize=(50,50))

ax.pie(sizes, explode=explode, labels=labels,colors=['green', 'red'], autopct='%1.1f%%',
        shadow=True, startangle=90,wedgeprops={'alpha':0.6})
# plt.savefig('pie_plagiarism.svg',transparent=True,bbox_inches='tight', format="svg")
plt.savefig('pie_plagiarism.png',transparent=True,bbox_inches='tight')

# plt.show()
```

--------------------------------

### Verilog Moore State Machine - Template 1 (Least Detailed)

Source: https://github.com/shailja-thakur/vgen/blob/main/prompts-and-testbenches/prompts-templates.txt

This template represents a Moore state machine with minimal comments after the internal signal declarations. It is suitable when detailed inline explanations are not required.

```verilog
// This is a Moore state machine with two states 0 and 1, one input in, and one output out. Reset state is 0.
module simple_fsm(clk, reset, in, out);
input clk;
input reset;
input in;
output wire out;
reg present_state, next_state;
```

--------------------------------

### Update and Plot GitHub Language Popularity (JS Alias)

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Updates language names (e.g., 'JavaScript' to 'JS') and then plots language popularity, highlighting Verilog and saving the plot to SVG and PDF. Assumes sorted_Languages_counts and total_count are defined.

```python
# Updating the data to reflect the change for JavaScript to JS
updated_sorted_Languages_counts = [(('JS' if lang == 'JavaScript' else lang), count) for lang, count in sorted_Languages_counts]

# Find the index of Verilog in the updated list
verilog_index = next(i for i, v in enumerate(updated_sorted_Languages_counts) if v[0] == 'Verilog')

# Number of initial languages to display
initial_display_count = 5

# Create a new list with the first few languages, placeholder, and Verilog
selected_languages_counts = updated_sorted_Languages_counts[:initial_display_count] + [('...', 0), updated_sorted_Languages_counts[verilog_index]]

# Unpack languages and counts
language, count = zip(*selected_languages_counts)
x_pos = np.arange(len(language))

# Plot
plt.figure(figsize=(6,3))
bars = plt.bar(x_pos, count, align='center', color=sns.color_palette("gist_rainbow", len(x_pos)))

# Annotate bars with percentage
for idx, bar in enumerate(bars):
    height = bar.get_height()
    percentage = (height / total_count) * 100
    if language[idx] == 'Verilog':  # Highlight Verilog
        plt.text(bar.get_x() + bar.get_width() / 2, height, f'{percentage:.2f}%', ha='center', va='bottom', fontsize=20, fontweight='bold', color='red')
    elif language[idx] != '...':  # Avoid annotating '...' 
        plt.text(bar.get_x() + bar.get_width() / 2, height, f'{percentage:.2f}%', ha='center', va='bottom', fontsize=13)

plt.yticks( fontsize=15)

plt.xticks(x_pos, language, rotation=30, fontsize=15)
plt.savefig('github-language-popularity.svg', bbox_inches='tight');
plt.savefig('github-language-popularity.pdf');

plt.show()
```

--------------------------------

### Verilog Testbench for Advanced FSM (Pattern 101 Detector)

Source: https://context7.com/shailja-thakur/vgen/llms.txt

A Verilog testbench (`tb_adv_fsm.v`) for evaluating an advanced Finite State Machine (FSM) that detects the pattern '101'. It includes clock generation, reset sequence, and stimulus application to verify state transitions and output.

```verilog
// tb_adv_fsm.v — testbench for the advanced FSM (pattern 101 detector)
`timescale 1 ns/10 ps

module tb_adv_fsm;
    reg clk, reset, x;
    wire z;
    localparam period = 20;

    adv_fsm UUT (.clk(clk), .reset(reset), .x(x), .z(z));

    initial begin clk = 0; forever #(period/2) clk = ~clk; end

    initial begin
        #2;
        reset = 1; x = 0; #period;
        if (z !== 0) begin $display("test 1 failed"); $finish; end

        reset = 0; x = 0; #period;   // IDLE
        if (z !== 0) begin $display("test 2 failed"); $finish; end

        x = 1; #period;               // → S1
        if (z !== 0) begin $display("test 3 failed"); $finish; end

        x = 0; #period;               // → S2
        if (z !== 0) begin $display("test 4 failed"); $finish; end

        x = 1; #period;               // → S3, output = 1
        if (z !== 1) begin $display("test 5 failed"); $finish; end

        #period;                      // → back to S1
        if (z !== 0) begin $display("test 6 failed"); $finish; end

        $display("all tests passed");
        $finish;
    end
endmodule
```

--------------------------------

### Prepare Data for Plotting

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Extracts language names and their counts from a sorted list of tuples, preparing them for visualization. Requires numpy and matplotlib.

```python
language = list(zip(*sorted_Languages_counts[:155]))[0]
count = list(zip(*sorted_Languages_counts[:155]))[1]
x_pos = np.arange(len(language))

plt.figure(figsize=(12,8))
```

--------------------------------

### Python PDF Extraction Utilities

Source: https://context7.com/shailja-thakur/vgen/llms.txt

Python code snippet demonstrating the use of `pdf_extractor` library functions (`readPDF`, `getTotalPages`, `getPages`) to extract text from Verilog PDF textbooks. This is useful for building training corpora.

```python
from pdf_extractor import readPDF, getTotalPages, getPages, preprocess

# Open a Verilog textbook PDF
pdf = readPDF("verilog_textbook.pdf")
# Expected output:
# Document Metadata
# {'/Title': 'Digital Design with Verilog', '/Author': '...'}

total = getTotalPages(pdf)
# Expected output:
# Total pages: 412

# Extract all text
all_text = getPages(pdf, total)
print(all_text[:200])
# Expected output (raw extracted text):
```

--------------------------------

### Plotting Language Popularity with Matplotlib

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Generates a bar chart to visualize language popularity scores. Customize x-axis ticks for better readability.

```python
plt.bar(x_pos, count,align='center',color=sns.color_palette("gist_rainbow",len(x_pos)))
plt.xticks(x_pos, language,rotation=30, fontsize=25)
```

--------------------------------

### Verilog 5-bit Galois LFSR Module

Source: https://context7.com/shailja-thakur/vgen/llms.txt

A Verilog module for a 5-bit maximal-length Galois Linear Feedback Shift Register (LFSR) with specified taps. This represents an intermediate prompt.

```verilog
// ── INTERMEDIATE: 5-bit Galois LFSR ─────────────────────────────────────────

// prompt1_lfsr.v
// This is a 5-bit maximal-length Galois LFSR with taps at bit positions 5 and 3
module lfsr(
input clk,
input reset,
output [4:0] q
);
reg [4:0] r_reg;
wire [4:0] r_next;
wire feedback_value;
```

--------------------------------

### Verilog Testbench for Wire Assignment

Source: https://context7.com/shailja-thakur/vgen/llms.txt

A Verilog testbench (`tb_wire_assign.v`) designed to functionally evaluate the `wire_assign` module. It applies input stimuli and checks the output against expected values, reporting pass/fail messages.

```verilog
// tb_wire_assign.v — testbench for the wire_assign module
`timescale 1 ns/10 ps

module tb_wire_assign;
    reg  in;
    wire out;
    localparam period = 2;

    wire_assign UUT (.in(in), .out(out));

    initial begin
        in = 0; #period;
        if (out !== 0) begin $display("test 1 failed"); $finish; end
        else $display("in = %b , out = %b", in, out);

        in = 1; #period;
        if (out !== 1) begin $display("test 2 failed"); $finish; end
        else $display("in = %b , out = %b", in, out);

        in = 0; #period;
        if (out !== 0) begin $display("test 3 failed"); $finish; end
        else $display("in = %b , out = %b", in, out);

        $display("all tests passed");
        $finish;
    end
endmodule
```

--------------------------------

### Verilog Moore FSM (Pattern 101 Detector) Module

Source: https://context7.com/shailja-thakur/vgen/llms.txt

An advanced Verilog module implementing a Moore state machine that detects the pattern '101'. It includes clock, reset, input, and output signals, with state registers.

```verilog
// ── ADVANCED: Moore FSM (pattern 101 detector) ───────────────────────────────

// prompt1_simple-fsm.v
// This is a Moore state machine with two states 0 and 1, one input in, and one output out.
// Reset state is 0. Output is high in state 0. If in is low, state changes.
module simple_fsm(input clk, input reset, input in, output out);
reg present_state, next_state;
```

--------------------------------

### Print Sorted Language Counts

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

This Python code prints a list of tuples, where each tuple contains a programming language and its corresponding count. This is useful for displaying the results of language popularity analysis.

```python
print(sorted_Languages_counts)
```

--------------------------------

### Sort and Display Language Counts

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

This Python code sorts the programming language counts in descending order and displays the top 200. It uses the 'operator' module for sorting.

```python
import operator
sorted_Languages_counts = sorted(Languages_count.items(), key=operator.itemgetter(1),reverse=True)
sorted_Languages_counts[:200]
```

--------------------------------

### Process and Count Programming Languages

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

This Python code iterates through a list of language data, flattens the nested structure, and counts the occurrences of each programming language. It's useful for analyzing the distribution of languages in a dataset.

```python
languagesList=[]
for lang in dataframe.language:
    languagesList.extend(lang)

```

```python
Languages_count={}
for lang in languagesList:
    if lang["name"] not in Languages_count:
        Languages_count[lang["name"]]=0
    Languages_count[lang["name"]]+=1
#Languages_count
```

--------------------------------

### Save DataFrame to CSV

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Saves the current DataFrame to a CSV file. Ensure the file path is correctly specified for your environment.

```python
dataframe.to_csv( '/Volumes/GoogleDrive/My Drive/sg0_workspace/datasets/verilog_github_contains_always_module_multicopies.csv',index=False)
```

```python
dataframe.to_csv( '/Volumes/GoogleDrive/My Drive/sg0_workspace/datasets/verilog_github_contains_always_module.csv',index=False)
```

```python
dataframe.to_csv( '/Volumes/GoogleDrive/My Drive/sg0_workspace/datasets/verilog_github_contains_always_or_endmodule.csv',index=False)
```

--------------------------------

### Calculate Trend Line and Plot Language Popularity

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Calculates the linear trend line for language popularity scores and generates a horizontal bar chart. Requires numpy, matplotlib, and seaborn libraries.

```python
slope, intercept = np.polyfit(x_pos, count, 1)
trendline = intercept + (slope * x_pos)
plt.figure(figsize=(10,30))
# plt.plot(x_pos, trendline, color='black', linestyle='--')    
plt.barh(x_pos, count,align='center',color=sns.color_palette("gist_rainbow",len(x_pos)))
plt.xticks(x_pos, language,rotation=45)
```

--------------------------------

### Define and Execute BigQuery Query Function

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

A Python function to execute a BigQuery SQL query and convert the results into a pandas DataFrame. It optionally uses the BigQuery Storage API for faster data retrieval.

```python
def query(query_string):
    
    dataframe = (
        bqclient.query(query_string)
        .result()
        .to_dataframe(
            # Optionally, explicitly request to use the BigQuery Storage API. As of
            # google-cloud-bigquery version 1.26.0 and above, the BigQuery Storage
            # API is used by default.
            create_bqstorage_client=True,
        )
    )
    print(dataframe.head())
    
    return dataframe
```

--------------------------------

### Text Labels for Plot

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Defines text labels for various programming languages to be used in plots. This is typically part of a larger plotting script.

```python
Text(131, 0, 'HLSL'),
  Text(132, 0, 'Rebol'),
  Text(133, 0, 'AutoHotkey'),
  Text(134, 0, 'SAS'),
  Text(135, 0, 'LLVM'),
  Text(136, 0, 'Module Management System'),
  Text(137, 0, 'Procfile'),
  Text(138, 0, 'Starlark'),
  Text(139, 0, 'ShaderLab'),
  Text(140, 0, 'Crystal'),
  Text(141, 0, 'Meson'),
  Text(142, 0, 'XQuery'),
  Text(143, 0, 'Jinja'),
  Text(144, 0, 'KiCad')
```

--------------------------------

### Plot Result Display

Source: https://github.com/shailja-thakur/vgen/blob/main/verilog-fetch-big-query.ipynb

Represents the output of a plotting function, indicating the figure size and axes. This is a representation of a Matplotlib figure object.

```python
<Figure size 864x576 with 1 Axes>
```