operators: 
- id: load_math
  config:
    type: hf_source
    dataset: open-web-math/open-web-math
    split: train
    trust_remote_code: True
- id: sample_dataset
  config:
    type: function
    function: data_strategies.commons.uniform_sample_fixed
    function_config:
      num_samples: 50_000  # 1.25% of 10000 target from stage 1
  input_ids:
  - load_math
- id: generate_instructions
  config:
    type: completions
    map: chat
    map_config:
      system_message: "You are a helpful assistant."
      user_message: |
        You are to reform the following math text snippet into a question with a quantitative or verifiable answer such as one that would be 
        included in the USAMO or the Putnam Exam. 

        Text: {{text}}
        
        Include only the new question or task. Do not include anything like "Here is the instruction". You can either extract a 
        question from the text or form a new one based on the text. Make the question sound like what a human would ask a language model. 
      output_column: instruction_seed
    model: gpt-4o-mini
    temperature: 1.0
    require_all_responses: False
    batch: True
  input_ids:
    - sample_dataset
- id: add_constant_column
  config:
    type: function
    function: data_strategies.commons.add_constant_columns
    function_config:
      response_seed: ""
  input_ids:
  - generate_instructions
- id: drop_columns
  config:
    type: function
    function: data_strategies.commons.keep_only_columns
    function_config:
      columns_to_keep:
      - instruction_seed
      - response_seed
  input_ids:
  - add_constant_column
- id: decontaminate
  config:
    type: cpu_function
    sharded: false
    num_cpus: 32
    function:  data_strategies._A1_Source.utils.decontaminate_fuzzy_w_ngram
    function_config:
      column: instruction_seed
      eval_datasets:
        - HuggingFaceH4/MATH-500
        - Maxwell-Jia/AIME_2024
        - AI-MO/aimo-validation-amc
        - livecodebench/code_generation_lite
        - mlfoundations-dev/AIME2025_combined
        - cais/hle
        - open-r1/codeforces
        - Idavidrein/gpqa
        - daman1209arora/jeebench
        - mlfoundations-dev/mmlu_pro_eval_full
        - Qwen/CodeElo
        - open-r1/ioi
      eval_columns:
        - problem
        - Problem
        - problem
        - question_content
        - question
        - question
        - description
        - Question
        - question
        - prompt
        - description
        - statement
      eval_splits:
        - test
        - train
        - train
        - test
        - train
        - test
        - test
        - train
        - test
        - test
        - test
        - test
      eval_subsets:
        Idavidrein/gpqa: gpqa_diamond
      similarity_threshold: 80.0
      ngram_size: 13
  input_ids:
  - drop_columns
- id: sample_dataset_final
  config:
    type: function
    function: data_strategies.commons.uniform_sample_fixed
    function_config:
      num_samples: 31_600  # 1.25% of 10000 target from stage 1
  input_ids:
  - decontaminate

# - id: annotate_r1
#   config:
#     type: completions
#     map: deepseek_reasoner
#     map_config:
#       input_problem_column: instruction_seed
#     model: deepseek-reasoner
#     batch: false
#     temperature: 1.0
#     require_all_responses: false
#     backend: openai
#     backend_params:
#       max_requests_per_minute: 500
#       max_tokens_per_minute: 1_000_000_000
#       base_url: "https://api.deepseek.com/"
#       invalid_finish_reasons: ['content_filter']
#   input_ids:
#     - sample_dataset_final
# - id: convert_reasoning_trace_to_final
#   config:
#     type: function
#     function: data_strategies.commons.convert_reasoning_trace_to_final
#     function_config:
#       reasoning_column: reasoning
#       solution_column: deepseek_solution
#       output_column: final_reasoning_trace
#   input_ids:
#   - annotate_r1
# - id: convert_to_sharegpt
#   config:
#     type: function
#     function: data_strategies.commons.convert_instruction_response_to_sharegpt
#     function_config:
#       input_instruction_column: instruction_seed
#       input_response_column: final_reasoning_trace
#       output_sharegpt_column: conversations
#   input_ids:
#     - convert_reasoning_trace_to_final