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name: "Code_Flow" |
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description: "ToDO: add description" |
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model_name: "gpt-4" |
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generation_parameters: |
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n: 1 |
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max_tokens: 3000 |
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temperature: 0.3 |
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model_kwargs: |
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top_p: 0.2 |
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frequency_penalty: 0 |
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presence_penalty: 0 |
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system_message_prompt_template: |
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_target_: langchain.PromptTemplate |
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template: |2- |
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Your goal is to provide executable Python code that solves a coding interview problem. The code should correctly handle all corner cases in order to pass the hidden test cases, which are used to evaluate the correctness of the solution. |
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The user will specify the problem by providing you with: |
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- the problem statement |
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- example test cases |
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- the constraints of the problem |
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The user will provide you with a task and an output format that you will strictly follow. |
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input_variables: [] |
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template_format: jinja2 |
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human_message_prompt_template: |
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_target_: langchain.PromptTemplate |
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template: "{{query}}" |
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input_variables: |
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- "query" |
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template_format: jinja2 |
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query_message_prompt_template: |
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_target_: langchain.PromptTemplate |
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template: |2- |
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# Problem statement |
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{{problem_description}} |
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{{io_description}} |
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{{constraints}} |
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Return Python code that solves the problem. The code should extend the following stub: |
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```python |
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{{python_stub}} |
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``` |
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without changing the method signatures. |
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Reply in the following format: |
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```python |
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{{code_placeholder}} |
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``` |
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input_variables: |
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- "problem_description" |
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- "io_description" |
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- "constraints" |
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- "python_stub" |
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partial_variables: |
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code_placeholder: "{{python_code}}" |
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template_format: jinja2 |
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input_data_transformations: [] |
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input_keys: |
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- "problem_description" |
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- "io_description" |
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- "constraints" |
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- "python_stub" |
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output_data_transformations: |
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- _target_: flows.data_transformations.RegexFirstOccurrenceExtractor |
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regex: '(?<=```python)([\s\S]*?)(?=```)' |
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regex_fallback: '(?<=```)([\s\S]*?)(?=```)' |
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input_key: "raw_response" |
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output_key: "code" |
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strip: True |
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assert_unique: True |
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output_keys: |
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- "code" |
