### Install ReProver Starting Retriever Model

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This command downloads the model checkpoint for ReProver's Starting Retriever from Google Drive and instructs where to place the downloaded file.

```Bash
gdown https://drive.google.com/uc?id=1aRd1jQPu_TX15Ib5htzn3wZqHYowl3ax
# Move the downloaded file to:
# {RAID_DIR}/checkpoints/
# {RAID_DIR}/{CHECKPOINT_DIR}/
```

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

### Setup LeanAgent Environment

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This snippet demonstrates how to create and activate a Conda environment for LeanAgent, install dependencies from requirements.txt, and set up the GITHUB_ACCESS_TOKEN environment variable.

```Bash
conda create -n "LeanAgent" python=3.10
conda activate LeanAgent
pip install -r requirements.txt
export GITHUB_ACCESS_TOKEN=<your_github_token>
```

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

### Download Starting FIM for Mathlib

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This command downloads the starting Fisher Information Matrix (FIM) for mathlib, which is required for Elastic Weight Consolidation (EWC) in LeanAgent.

```Bash
gdown https://drive.google.com/uc?id=1Q8yHq7XTAaHXGCiCGmhwZhTfkhHhN1cP
# Move the downloaded file to your FISHER_DIR.
```

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

### Run LeanAgent Main Script

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This command executes the main LeanAgent script after all setup and configuration steps have been completed.

```Bash
bash run_leanagent.sh
```

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

### Install ReProver Tactic Generator Model

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This sequence installs the gdown utility and downloads the model checkpoint for ReProver's Tactic Generator from Google Drive, then moves it to the specified directory.

```Bash
pip install gdown
gdown https://drive.google.com/uc?id=11DXxixg6S4-hUA-u-78geOxEB7J7rCoX
mv downloaded_file {RAID_DIR}/
```

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

### Install Latest LeanAgent Checkpoint

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This command downloads the latest LeanAgent checkpoint from Google Drive, intended for use with the framework.

```Bash
gdown https://drive.google.com/uc?id=1plkC7Y5n0OVCJ0Ad6pH8_mwbALKYY_FY
# Move the downloaded file to {RAID_DIR}/checkpoints/
```

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

### Fisher Information Matrix (FIM) Computation

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

The Fisher Information Matrix (FIM) is computed using forward passes through the model with training data. Gradients of the model's log-likelihood with respect to parameters are calculated, squared, and averaged across training examples. The FIM is saved by the global zero process to prevent redundant writes.

```Python
# Conceptual steps for FIM computation (implementation details omitted)
# 1. Perform forward passes with training data
# 2. Compute gradients of log-likelihood w.r.t. parameters
# 3. Square gradients and average across examples
# 4. Save FIM by the global zero process
```

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

### Configure EWC Fisher Computation Script

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This outlines the configuration steps for the run_compute_fisher.sh script, including setting the RAID_DIR and GITHUB_ACCESS_TOKEN environment variables for EWC computations.

```Bash
# Set the RAID_DIR variable to your desired directory path
# Update the Conda path in the source command to match your installation
# Add your GitHub personal access token as the environment variable GITHUB_ACCESS_TOKEN
```

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

### Run LeanAgent Unit Tests

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This command sequence activates the LeanAgent Conda environment and then executes the project's unit tests using pytest.

```Bash
conda activate LeanAgent
python -m pytest tests/
```

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

### Run LeanAgent with EWC

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This describes the iterative process of using Elastic Weight Consolidation (EWC) for lifelong learning by alternating between running the LeanAgent and computing the Fisher Information Matrix.

```Bash
# Run LeanAgent for one epoch (or until EWC is triggered)
bash run_leanagent.sh

# Compute the FIM for EWC
bash run_compute_fisher.sh

# Repeat for the next epoch
```

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

### LeanAgent Theorem Proving Process

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Details the steps LeanAgent takes to prove theorems marked with 'sorry' in Lean files. This includes building a knowledge base, constructing a dependency graph, retrieving relevant premises, generating tactic candidates via beam search, and applying tactics to search for a proof.

```Python
def prove_theorem(theorem_id):
    # 1. Process premise corpus to build knowledge base
    knowledge_base = build_knowledge_base(theorem_id)

    # 2. Construct dependency graph
    dependency_graph = build_dependency_graph(theorem_id)

    # 3. Proof search for each proof state
    proof_found = False
    for proof_state in get_proof_states(theorem_id):
        relevant_premises = retrieve_premises(proof_state, knowledge_base)
        filtered_premises = filter_premises(relevant_premises)

        tactic_candidates = beam_search_tactics(proof_state, filtered_premises)

        for tactic in tactic_candidates:
            next_state = apply_tactic(proof_state, tactic, dependency_graph)
            if is_proof_complete(next_state):
                save_proof(theorem_id, next_state)
                proof_found = True
                break
            add_to_search_tree(proof_state, next_state, tactic)
            select_next_tactic(tactic)

        if proof_found:
            break

    return proof_found
```

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

### Extracting Theorem and Premise Data with LeanDojo Trace

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This snippet describes the process of using LeanDojo's `trace` function to extract theorem definitions, proofs, premises, and their dependencies from Lean repositories. It involves parsing Lean files, identifying theorems and proofs, and building dependency graphs.

```Python
from leandojo.trace import trace

# Assuming 'repo_path' is the path to a cloned Lean repository
# and 'output_dir' is where the traced data will be saved
trace(repo_path, output_dir)
```

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

### Configure LeanAgent Python Script

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This describes the necessary global variable modifications within the leanagent.py script to configure data directories, checkpoint paths, and evaluation file locations. It also mentions adjusting options for different training configurations.

```Python
# Set the following global variables:
repo_dir = "/path/to/your/repository"
DATA_DIR = "/path/to/data"
CHECKPOINT_DIR = "/path/to/checkpoints"
EVAL_RESULTS_FILE_PATH = "/path/to/evaluation_results.txt"
DB_FILE_NAME = "leanagent.db"
PROOF_LOG_FILE_NAME = "proof_log.txt"
ENCOUNTERED_THEOREMS_FILE = "encountered_theorems.txt"
# Optional: FISHER_DIR = "/path/to/fisher_matrices"
```

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

### LeanAgent Repository Contribution Workflow

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Outlines the process for integrating a successfully generated proof into a Lean repository. This involves creating a new branch, replacing 'sorry' keywords with the proof text, committing the changes, and submitting a pull request.

```Python
def contribute_proof(repository_path, theorem_id, proof_text):
    # 1. Create a temporary branch
    create_branch(repository_path, f"leanagent-proof-{theorem_id}")

    # 2. Locate and replace 'sorry' keywords
    replace_sorry_with_proof(repository_path, theorem_id, proof_text)

    # 3. Commit and push changes
    commit_changes(repository_path, f"feat: Add LeanAgent proof for theorem {theorem_id}")
    push_branch(repository_path, f"leanagent-proof-{theorem_id}")

    # 4. Create a pull request
    create_pull_request(
        repository_path,
        f"LeanAgent Proof for Theorem {theorem_id}",
        f"This PR adds a proof for theorem {theorem_id} generated by LeanAgent."
    )

```

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

### LeanAgent Database Persistence (Python)

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Demonstrates how LeanAgent's database components are serialized to and deserialized from a JSON format using `to_dict` and `from_dict` methods. This enables persistence between runs and distribution across computing resources.

```Python
class LeanObject:
    def to_dict(self):
        # Implementation to convert object to dictionary
        pass

    @classmethod
    def from_dict(cls, data):
        # Implementation to create object from dictionary
        pass

# Example usage with a Theorem object
# theorem_data = theorem_instance.to_dict()
# new_theorem = Theorem.from_dict(theorem_data)
```

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

### Lean Repository Compatibility Check

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This snippet outlines the logic for checking Lean repository compatibility by examining the `lean-toolchain` file for supported Lean versions, ranging from `4.3.0-rc2` to `4.8.0-rc1`.

```Lean
-- Example of checking lean-toolchain content (conceptual)
-- In a real scenario, this would involve file I/O and version parsing in Python.
-- For demonstration, imagine a function that reads the toolchain file:
-- check_lean_version(repo_path) -> bool
```

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

### LeanAgent Curriculum Complexity Calculation (Conceptual)

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Illustrates the conceptual calculation of theorem complexity using an exponential function of proof steps ($e^S$) for LeanAgent's curriculum learning strategy.

```Python
# Conceptual representation of complexity calculation
def calculate_complexity(num_proof_steps):
    if num_proof_steps == float('inf'): # For 'sorry' theorems
        return float('inf')
    return math.exp(num_proof_steps)

# Example usage:
# complexity = calculate_complexity(theorem.num_proof_steps)
```

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

### Exporting Theorem Proofs and Metadata

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This snippet details the structure of exported theorem data, including the theorem statement, proof tactics, annotated tactics, and proof states. It also mentions the export of premise definitions in a topologically sorted order.

```Python
# Conceptual representation of exported theorem data structure
exported_theorem = {
    "statement": "...",
    "proof_tactics": ["tactic1", "tactic2"],
    "annotated_tactics": [{"tactic": "tactic1", "premises_used": ["premise1"] }],
    "proof_states": [{"before": "state1", "after": "state2"}]
}

# Conceptual representation of exported premise data structure
exported_premise = {
    "full_name": "...",
    "code": "...",
    "source_position": {...},
    "kind": "theorem" | "lemma" | "definition"
}

# Conceptual representation of dataset metadata
metadata = {
    "creation_timestamp": "...",
    "source_repository": {
        "url": "...",
        "commit": "..."
    },
    "leandojo_version": "..."
}
```

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

### Lean Premise File Representation

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Defines the structure for representing a Lean source file containing premises. It includes the file path, imported modules, and a list of premises.

```Lean
structure PremiseFile := (
  filePath : String,         -- Path to the Lean source file
  imports : List String,     -- List of imported modules
  premises : List Premise    -- List of premises defined in the file
)
```

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

### Lean Repository Representation

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Defines the structure for representing a Lean GitHub repository. It includes metadata, categorized theorems, and premise files.

```Lean
structure Repository := (
  url : String,              -- URL of the GitHub repository
  name : String,             -- Name of the repository
  commit : String,           -- Current commit SHA
  leanVersion : String,      -- Lean version used
  provenTheorems : List Theorem, -- Theorems with complete proofs
  sorryTheorems : List Theorem, -- Theorems marked with 'sorry'
  unprovenTheorems : List Theorem, -- Theorems previously unproven, now proven by LeanAgent
  premiseFiles : List PremiseFile -- List of premise files in the repository
)
```

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

### Adjust Lean Toolchain Paths

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This Python code snippet shows how to dynamically set Lean toolchain paths, which is crucial for ensuring the correct version of Lean is used by the agent.

```Python
lean_dir2 = f"/.elan/toolchains/leanprover--lean4--{v}"
lean_dir3 = f"~/.elan/toolchains/leanprover--lean4--{v}"
```

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

### Update File Paths with Bash

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

This command executes a bash script to update file paths, likely for configuring project directories.

```Bash
bash replace_files.sh
```

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

### Distributed Training with PyTorch Lightning DDP

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

LeanAgent utilizes PyTorch Lightning's Distributed Data Parallel (DDP) strategy for efficient multi-GPU training. This approach ensures synchronized gradient updates across devices, maintaining model consistency during progressive learning. Custom timeout settings are employed to manage lengthy operations.

```Python
from pytorch_lightning.strategies import DDPStrategy

# Example configuration within a PyTorch Lightning Trainer
trainer = Trainer(
    accelerator="gpu",
    devices=4,  # Example: 4 NVIDIA A100s
    strategy=DDPStrategy(timeout=timedelta(seconds=3600)) # Custom timeout
)
```

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

### Lean Premise Representation

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Defines the structure for representing a mathematical premise (fact) in Lean. It includes its name, code, source positions, and type.

```Lean
structure Premise := (
  name : String,             -- Fully qualified name of the premise
  code : String,             -- The Lean code defining the premise
  startPos : SourcePos,      -- Start source code position
  endPos : SourcePos,        -- End source code position
  premiseType : String       -- Type of premise (e.g., theorem, definition, axiom)
)
```

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

### Lean Theorem Representation

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Defines the structure for representing a Lean theorem within the LeanAgent system. It includes metadata, the theorem statement, and traced tactics, along with a difficulty rating for curriculum learning.

```Lean
structure Theorem := (
  name : String,             -- Fully qualified name of the theorem
  filePath : String,         -- Path to the file defining the theorem
  sourcePos : SourcePos,     -- Source code positions of the theorem definition
  url : String,              -- URL of the repository
  commit : String,           -- Commit SHA of the repository
  statement : String,        -- The theorem statement
  tracedTactics : List Tactic, -- Sequence of tactics proving the theorem
  difficulty : Float         -- Difficulty rating for curriculum learning
)
```

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

### LeanAgent Citation

Source: https://github.com/lean-dojo/leanagent/blob/main/README.md

Citation details for the LeanAgent research paper, including title, authors, and conference information.

```BibTeX
@inproceedings{kumarappan2025leanagent,
  title={{LeanAgent}: Lifelong Learning for Formal Theorem Proving},
  author={Kumarappan, Adarsh and Tiwari, Mo and Song, Peiyang and George, Robert Joseph and Xiao, Chaowei and Anandkumar, Anima},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2025}
}
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.