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README.md

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----
-library_name: transformers
-tags: []
-widget:
-- text: >-
-    Hi Im pip_bot , I can document code , write sql and do other etl related work. All you have to give me are a few examples.
-  example_title: example
----
+# pip-code-to-doc
 
-# Model Card for Model ID
+[pipableAi](https://www.linkedin.com/company/pipable.ai/about/)
 
-<!-- Provide a quick summary of what the model is/does. -->
+[colab_notebook](https://colab.research.google.com/drive/17PyMU_3QN9LROy7x-jmaema0cuLRzBvc?usp=sharing)
 
+## What have we built?
 
+A 1.3 bn code documentation model that outperforms most models on documenting codes and making your in-house libs ready for LLM and RAG pipelines.
+We have also open sourced a [parsing lib](https://github.com/PipableAI/pip-library-parser) for the same, together the lib and model can turn your codebase to functional parse tree ready to be consumed by LLMs to execute complex tasks.
+This is a further trained version of pip-sql-1.3b.
 
-## Model Details
+## How we built it?
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+We used softmax cross entropy and a modified form of policy grad along with Q loss, optimized in an EM set up.
+Loss behaviour in the set up mentioned above - 
 
+## License
 
+The model is open source under apache 2.0. License
+
+## Usage
+
+
+### Library use
+```python
+!pip3 install git+https://github.com/PipableAI/pip-library-parser
+!pip3 install atlassian-python-api
+
+
+from pip_library_parser import CodeToDocGenerator
+from atlassian import Jira
+
+import torch
+torch.set_default_device("cuda")
+
+# Instantiate the CodeToDocGenerator
+generator = CodeToDocGenerator()
+
+# Generate docstrings for the module's functions and methods
+module = Jira
+module_name = "atlassian.Jira"
+
+docs = generator.generate_module_docs(module, module_name)
+print(docs)
+```
+
+```python
+from pip_library_parser import CodeToDocGenerator
+
+# Instantiate the CodeToDocGenerator
+generator = CodeToDocGenerator()
+
+code_snippet = """
+def example_function(x):
+    return x * 2
+"""
+
+docstring = generator.generate_docstring_from_pip_model(code_snippet)
+print("Generated Docstring:")
+print(docstring)
+```
+
+### Installation
+
+```bash
+pip install transformers
+```
+
+### Prompt
+```python
+prompt = f"""<function_code>{code}</function_code>
+<question>Give one line description of the python code above in natural language.</question>
+<doc>"""
+```
+
+### PyTorch
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-code-to-doc-1.3b").to(device)
+tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-code-to-doc-1.3b")
+prompt = f"""
+<function_code>
+def example_function(x):
+    return x * 2
+</function_code>
+<question>Give one line description of the python code above in natural language.</question>
+<doc>"""
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=300)
+tokenizer.decode(outputs[0], skip_special_tokens=True).split('<doc>')[-1].split('</doc>')[0]
+```
+
+
+
+## Examples
+
+### prompt
+```python
+<function_code>
+###########################
+# Generate Analytical Model
+###########################
+##################################################
+# func: get_np_array_transition_probability_matrix
+##################################################
+def get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix):
+    print('np_array_A_matrix:')
+    print(np_array_A_matrix)
+    #####################################################
+    # Perturb the adjacency matrix to avoid singularities
+    #####################################################
+    np_array_A_matrix += (np.full((int_num_states, int_num_states), float_eps) - (np.identity(int_num_states) * float_eps))
+    print('np_array_A_matrix:')
+    print(np_array_A_matrix)
+    print('np_array_D_matrix:')
+    np_array_D_matrix = np.diag(np.sum(np_array_A_matrix, axis=1))
+    print(np_array_D_matrix)
+    print('np_array_D_matrix_inv:')
+    np_array_D_matrix_inv = np.linalg.inv(np_array_D_matrix)
+    print(np_array_D_matrix_inv)
+    print('\n\n')
+    print('np_array_P_matrix:')
+    np_array_P_matrix = np.dot(np_array_D_matrix_inv, np_array_A_matrix)
+    print(np_array_P_matrix)
+    print('np.sum(np_array_P_matrix, axis=1):')
+    print(np.sum(np_array_P_matrix, axis=1))
+    print('\n\n')
+    return np_array_P_matrix
+##################################################
+# func: get_np_array_perron_frobenius_eigen_vector
+##################################################
+def get_np_array_perron_frobenius_matrix(int_num_states, np_array_P_matrix):
+    np_array_perron_frobenius_matrix = np.linalg.matrix_power(np_array_P_matrix,1000)
+    np_array_perron_frobenius_vector = np_array_perron_frobenius_matrix[0,:]
+    print('np_array_perron_frobenius_matrix:')
+    print(np_array_perron_frobenius_matrix)
+    print('np.sum(np_array_perron_frobenius_matrix, axis=1):')
+    print(np.sum(np_array_perron_frobenius_matrix, axis=1))
+    print('np.sum(np_array_perron_frobenius_matrix, axis=0):')
+    print(np.sum(np_array_perron_frobenius_matrix, axis=0))
+    print('np.sum(np_array_perron_frobenius_matrix, axis=0)/int_num_states:')
+    print(np.sum(np_array_perron_frobenius_matrix, axis=0)/int_num_states)
+    print('np.dot(np_array_perron_frobenius_vector, np_array_P_matrix):')
+    print(np.dot(np_array_perron_frobenius_vector, np_array_P_matrix))
+    print('np_array_perron_frobenius_vector:')
+    print(np_array_perron_frobenius_vector)
+    print('\n\n')
+    return np_array_perron_frobenius_vector, np_array_perron_frobenius_matrix
+#############################
+# func: get_np_array_Z_matrix
+#############################
+def get_np_array_Z_matrix(int_num_states, np_array_P_matrix, np_array_perron_frobenius_matrix):
+    np_array_Z_matrix = np.linalg.inv(np.identity(int_num_states) - np_array_P_matrix + np_array_perron_frobenius_matrix)
+    print('np_array_Z_matrix:')
+    print(np_array_Z_matrix)
+    print('\n\n')
+    return(np_array_Z_matrix)
+#############################
+# func: get_np_array_H_matrix
+#############################
+def get_np_array_H_matrix(int_num_states, np_array_Z_matrix, np_array_perron_frobenius_vector):
+    np_array_H_matrix = np.zeros([int_num_states, int_num_states])
+    for i in range(int_num_states):
+        for j in range(int_num_states):
+            np_array_H_matrix[i][j] = (np_array_Z_matrix[j][j] - np_array_Z_matrix[i][j])/np_array_perron_frobenius_vector[j]
+    print('np_array_H_matrix:')
+    print(np_array_H_matrix)
+    print('\n\n')
+    return np_array_H_matrix
+###########
+# func: run
+###########
+def run(np_array_A_matrix):
+    int_num_states = len(np_array_A_matrix)
+    np_array_P_matrix = get_np_array_transition_probability_matrix(int_num_states, np_array_A_matrix)
+    np_array_perron_frobenius_vector, np_array_perron_frobenius_matrix = get_np_array_perron_frobenius_matrix(int_num_states, np_array_P_matrix)
+    np_array_Z_matrix = get_np_array_Z_matrix(int_num_states, np_array_P_matrix, np_array_perron_frobenius_matrix)
+    np_array_H_matrix = get_np_array_H_matrix(int_num_states, np_array_Z_matrix, np_array_perron_frobenius_vector)
+    return(np_array_H_matrix)
+</function_code>
+<question>Give one line description of the python code above in natural language.</question>
+<doc>
+```
+
+### Response
+```txt
+The given python code is a function that calculates the transition probability matrix, P, for a given adjacency matrix A, and then uses these matrices to calculate the Perron-Frobenius eigenvector and its inverse matrix Z, and finally, the H matrix which is the inverse of the Z matrix. The H matrix is then returned as the output of the function. The adjacency matrix A is a square matrix where each element at position (i, j) represents the probability of transitioning from state i to state j. The function first perturbs the adjacency matrix to avoid singularities, then calculates the transition probability matrix P, the Perron-Frobenius eigenvector and its inverse matrix Z, and finally, the H matrix. The H matrix is then returned as the output of the function.
+```
+
+### Team
+Avi Kothari, Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Soham Acharya