deepseekcoder vs opencodeinterpreter

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href=\"https://www.kaggle.com/code/james113001/deepseekcoder-vs-opencodeinterpreter?scriptVersionId=183342349\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\nimport torch\nimport re\nimport pandas as pd\nimport random\nimport datasets\n\n\n# Load model and tokenizer\nmodel_name = '/kaggle/input/deepseek-math-7b-rl/transformers/7b-rl/1'\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\ntokenizer = AutoTokenizer.from_pretrained(model_name)\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_name,\n    torch_dtype=torch.float16 if device == 'cuda' else torch.float32,\n    device_map='auto',\n    offload_folder=\"offload\"\n)\n\ngeneration_config = GenerationConfig.from_pretrained(model_name)\ngeneration_config.pad_token_id = tokenizer.eos_token_id","metadata":{"id":"41lG2VIynrSq","outputId":"1e0d61aa-7916-4273-8bd3-f5e3a7118bd6","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"! rm -rf /opt/conda/lib/python3.10/site-packages/aiohttp-3.9.1.dist-info","metadata":{"execution":{"iopub.status.busy":"2024-06-03T20:08:18.165971Z","iopub.execute_input":"2024-06-03T20:08:18.166689Z","iopub.status.idle":"2024-06-03T20:08:19.434139Z","shell.execute_reply.started":"2024-06-03T20:08:18.166653Z","shell.execute_reply":"2024-06-03T20:08:19.433009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !pip install langchain langchain-experimental --upgrade","metadata":{"execution":{"iopub.status.busy":"2024-06-03T10:20:49.66877Z","iopub.execute_input":"2024-06-03T10:20:49.669217Z","iopub.status.idle":"2024-06-03T10:20:49.676089Z","shell.execute_reply.started":"2024-06-03T10:20:49.669186Z","shell.execute_reply":"2024-06-03T10:20:49.674119Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# !conda install -y langchain","metadata":{"execution":{"iopub.status.busy":"2024-06-03T10:20:49.678618Z","iopub.execute_input":"2024-06-03T10:20:49.679341Z","iopub.status.idle":"2024-06-03T10:20:49.682995Z","shell.execute_reply.started":"2024-06-03T10:20:49.679308Z","shell.execute_reply":"2024-06-03T10:20:49.68217Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig\n# import torch\n# from langchain_core.runnables.base import Runnable\n\n# # class DeepSeekMathRunnable(Runnable):\n# #     def __init__(self, model_name):\n# #         self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n# #         self.model = AutoModelForCausalLM.from_pretrained(\n# #             model_name,\n# #             torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n# #             device_map='auto',\n# #             offload_folder=\"offload\"\n# #         )\n# #         self.generation_config = GenerationConfig.from_pretrained(model_name)\n# #         self.generation_config.pad_token_id = self.tokenizer.eos_token_id\n\n# #     def invoke(self, input_text, stop=None, **kwargs):\n# #         inputs = self.tokenizer.encode(input_text, return_tensors=\"pt\").to(self.model.device)\n# #         outputs = self.model.generate(\n# #             inputs, \n# #             max_length=512, \n# #             pad_token_id=self.tokenizer.eos_token_id\n# #         )\n# #         response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)\n# #         return response\n\n# # Instantiate the custom runnable model\n# model_name = '/kaggle/input/deepseek-math-7b-rl/transformers/7b-rl/1'\n# runnable_model = DeepSeekMathRunnable(model_name)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-03T10:20:49.684418Z","iopub.execute_input":"2024-06-03T10:20:49.685432Z","iopub.status.idle":"2024-06-03T10:20:49.691997Z","shell.execute_reply.started":"2024-06-03T10:20:49.685356Z","shell.execute_reply":"2024-06-03T10:20:49.690314Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from langchain_core.runnables.base import Runnable\n\n# class DeepSeekMathRunnable(Runnable):\n#     def __init__(self, model):\n#         self.model = model\n\n#     def invoke(self, input_text):\n#         # Replace this with the actual way to generate responses from DeepSeekMath\n#         response = self.model.generate(input_text)\n#         return response\n","metadata":{"execution":{"iopub.status.busy":"2024-06-03T10:20:49.694514Z","iopub.execute_input":"2024-06-03T10:20:49.695366Z","iopub.status.idle":"2024-06-03T10:20:49.700019Z","shell.execute_reply.started":"2024-06-03T10:20:49.695304Z","shell.execute_reply":"2024-06-03T10:20:49.698989Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# DeepSeekCoder-instruct V1.5, 7B","metadata":{}},{"cell_type":"code","source":"# tokenizer.chat_template","metadata":{"execution":{"iopub.status.busy":"2024-06-10T22:43:58.705335Z","iopub.execute_input":"2024-06-10T22:43:58.705712Z","iopub.status.idle":"2024-06-10T22:43:58.713538Z","shell.execute_reply.started":"2024-06-10T22:43:58.705684Z","shell.execute_reply":"2024-06-10T22:43:58.712642Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"\"{% if not add_generation_prompt is defined %}\\n{% set add_generation_prompt = false %}\\n{% endif %}\\n{%- set ns = namespace(found=false) -%}\\n{%- for message in messages -%}\\n    {%- if message['role'] == 'system' -%}\\n        {%- set ns.found = true -%}\\n    {%- endif -%}\\n{%- endfor -%}\\n{{bos_token}}{%- if not ns.found -%}\\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\\\n'}}\\n{%- endif %}\\n{%- for message in messages %}\\n    {%- if message['role'] == 'system' %}\\n{{ message['content'] }}\\n    {%- else %}\\n        {%- if message['role'] == 'user' %}\\n{{'### Instruction:\\\\n' + message['content'] + '\\\\n'}}\\n        {%- else %}\\n{{'### Response:\\\\n' + message['content'] + '\\\\n<|EOT|>\\\\n'}}\\n        {%- endif %}\\n    {%- endif %}\\n{%- endfor %}\\n{% if add_generation_prompt %}\\n{{'### Response:'}}\\n{% endif %}\""},"metadata":{}}]},{"cell_type":"code","source":"from transformers import AutoTokenizer, AutoModelForCausalLM\nimport re\n\ntokenizer = AutoTokenizer.from_pretrained(\"deepseek-ai/deepseek-coder-7b-instruct-v1.5\", trust_remote_code=True)\nmodel = AutoModelForCausalLM.from_pretrained(\"deepseek-ai/deepseek-coder-7b-instruct-v1.5\", trust_remote_code=True)#.cuda()\n\n# latex_prompt = \"\"\"\n# You are going to take the following LaTeX mathematical reasoning step and convert it into SymPy executable code with the final variable named 'answer'. If the final variable is not 'answer', it is imperative to assign it to 'answer'. You will also return the output of the executable code after 'Sympy:'.\n\n# Prompt: {latex_input}\n# SymPy:\n# \"\"\"","metadata":{"execution":{"iopub.status.busy":"2024-06-11T22:25:53.046167Z","iopub.execute_input":"2024-06-11T22:25:53.046977Z","iopub.status.idle":"2024-06-11T22:28:24.821147Z","shell.execute_reply.started":"2024-06-11T22:25:53.046944Z","shell.execute_reply":"2024-06-11T22:28:24.818566Z"},"trusted":true},"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/1.87k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e5e0fa5762ae4668816bde9bc7690f10"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/4.61M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1fb1455e2dff4b9b9f9611d1098ea767"}},"metadata":{}},{"name":"stderr","text":"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/621 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cfecc3fa63274304892008eaa0a0e354"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json:   0%|          | 0.00/22.5k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"be02332e0457483e83e765ac7267d034"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ccae122e1c18441fbc1b95ba3ce4bb4a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00001-of-00003.safetensors:   0%|          | 0.00/4.99G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"37cd7ff0a8cd445c8a67417c266045ae"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00002-of-00003.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"70256f99e7f14679a4916704e7c6d935"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00003-of-00003.safetensors:   0%|          | 0.00/3.85G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0c93c652ca7d4e9c945a232ce3c5c492"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"849e240f23a04788ae814c5ec671cd24"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/121 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b23f6b9cbb5443c58de734aeeb07052c"}},"metadata":{}}]},{"cell_type":"code","source":"tokenizer.chat_template","metadata":{"execution":{"iopub.status.busy":"2024-06-11T22:19:27.096616Z","iopub.execute_input":"2024-06-11T22:19:27.097043Z","iopub.status.idle":"2024-06-11T22:19:27.104377Z","shell.execute_reply.started":"2024-06-11T22:19:27.097009Z","shell.execute_reply":"2024-06-11T22:19:27.103506Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"\"{% if not add_generation_prompt is defined %}\\n{% set add_generation_prompt = false %}\\n{% endif %}\\n{%- set ns = namespace(found=false) -%}\\n{%- for message in messages -%}\\n    {%- if message['role'] == 'system' -%}\\n        {%- set ns.found = true -%}\\n    {%- endif -%}\\n{%- endfor -%}\\n{{bos_token}}{%- if not ns.found -%}\\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\\\n'}}\\n{%- endif %}\\n{%- for message in messages %}\\n    {%- if message['role'] == 'system' %}\\n{{ message['content'] }}\\n    {%- else %}\\n        {%- if message['role'] == 'user' %}\\n{{'### Instruction:\\\\n' + message['content'] + '\\\\n'}}\\n        {%- else %}\\n{{'### Response:\\\\n' + message['content'] + '\\\\n<|EOT|>\\\\n'}}\\n        {%- endif %}\\n    {%- endif %}\\n{%- endfor %}\\n{% if add_generation_prompt %}\\n{{'### Response:'}}\\n{% endif %}\""},"metadata":{}}]},{"cell_type":"code","source":"question = 'Solve for the radius of the circle in the equation $x^2+y^2 - 7 = 4y-14x+3$.'\nmessages=[\n    { 'role': 'user', \n     'content': f\"You are assisting DeepSeekMath solve a problem by solving its proposal step by writing an executable SymPy python code as necessary: {question} Assign the answer to 'answer' variable. Pay close attention to detail; Accuracy is of utmost importance.\"}\n]","metadata":{"execution":{"iopub.status.busy":"2024-06-12T00:18:02.577876Z","iopub.execute_input":"2024-06-12T00:18:02.578257Z","iopub.status.idle":"2024-06-12T00:18:02.583448Z","shell.execute_reply.started":"2024-06-12T00:18:02.578224Z","shell.execute_reply":"2024-06-12T00:18:02.582398Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"def generate_sympy_code(messages):\n    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n\n    outputs = model.generate(\n        inputs, \n        max_new_tokens=600, \n        do_sample=False, \n        top_k=50, \n        top_p=0.95, \n        repetition_penalty = 1,\n        temperature = 0.1,\n        num_return_sequences=1, \n        pad_token_id=tokenizer.eos_token_id, # add this line <=====\n        eos_token_id=tokenizer.eos_token_id)\n    generated_text = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n    print(generated_text)\n    # Extract the SymPy code from the generated text\n    sympy_code_match = re.search(r'```python\\n(.*?)```', generated_text, re.DOTALL)\n    \n    if sympy_code_match:\n        sympy_code = sympy_code_match.group(1).strip()\n        if sympy_code:\n            lines = sympy_code.split('\\n')\n            last_line = lines[-1].strip()\n            if 'answer' not in last_line:\n                sympy_code += f'\\nanswer = {last_line}'\n        return sympy_code\n    return \"\", \"\"\n\ndef execute_sympy_code(sympy_code):\n    exec_globals = {}\n    exec(sympy_code, exec_globals)\n    result = exec_globals.get('answer', 'No result variable found')\n    return result","metadata":{"execution":{"iopub.status.busy":"2024-06-11T23:25:10.19979Z","iopub.execute_input":"2024-06-11T23:25:10.200237Z","iopub.status.idle":"2024-06-11T23:25:10.211973Z","shell.execute_reply.started":"2024-06-11T23:25:10.200196Z","shell.execute_reply":"2024-06-11T23:25:10.210695Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"sympy_code = generate_sympy_code(messages)\nprint(\"Generated SymPy Code:\")\nprint(sympy_code)","metadata":{"execution":{"iopub.status.busy":"2024-06-12T00:18:09.7705Z","iopub.execute_input":"2024-06-12T00:18:09.770855Z","iopub.status.idle":"2024-06-12T00:24:15.186619Z","shell.execute_reply.started":"2024-06-12T00:18:09.770825Z","shell.execute_reply":"2024-06-12T00:24:15.185504Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"The given equation is a circle equation in the form of $(x-a)^2 + (y-b)^2 = r^2$. \n\nWe can rearrange the equation to this form to find the center (a, b) and the radius r.\n\nHere is the Python code to solve for the radius:\n\n```python\nimport math\n\n# Given equation\nx2_y2_minus_7_4y_minus_14x_plus_3 = 0\n\n# Rearrange the equation to the form of (x-a)^2 + (y-b)^2 = r^2\n# (x-(-4/2))^2 + (y-(7/2))^2 = r^2\n# (x+2)^2 + (y-3.5)^2 = r^2\n\n# The center of the circle is at (-4/2, 7/2) = (-2, 3.5)\na = -2\nb = 3.5\n\n# The radius is the distance from the center to any point on the circle\n# Here, we can use the point (0, 0)\nr = math.sqrt((0-a)**2 + (0-b)**2)\n\n# Assign the answer to 'answer' variable\nanswer = r\n\nprint(answer)\n```\n\nThis code will output the radius of the circle.\n\nGenerated SymPy Code:\nimport math\n\n# Given equation\nx2_y2_minus_7_4y_minus_14x_plus_3 = 0\n\n# Rearrange the equation to the form of (x-a)^2 + (y-b)^2 = r^2\n# (x-(-4/2))^2 + (y-(7/2))^2 = r^2\n# (x+2)^2 + (y-3.5)^2 = r^2\n\n# The center of the circle is at (-4/2, 7/2) = (-2, 3.5)\na = -2\nb = 3.5\n\n# The radius is the distance from the center to any point on the circle\n# Here, we can use the point (0, 0)\nr = math.sqrt((0-a)**2 + (0-b)**2)\n\n# Assign the answer to 'answer' variable\nanswer = r\n\nprint(answer)\n","output_type":"stream"}]},{"cell_type":"code","source":"execution_result = execute_sympy_code(sympy_code)\nprint(\"\\nExecution Result:\")\nprint(execution_result)","metadata":{"execution":{"iopub.status.busy":"2024-06-11T23:36:23.575215Z","iopub.execute_input":"2024-06-11T23:36:23.576019Z","iopub.status.idle":"2024-06-11T23:36:23.580921Z","shell.execute_reply.started":"2024-06-11T23:36:23.575991Z","shell.execute_reply":"2024-06-11T23:36:23.580009Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"\nExecution Result:\n0.2879377767249482\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# OpenCodeInterpreter-DS-6.7B","metadata":{}},{"cell_type":"code","source":"# from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain\n# from langchain.llms.huggingface_pipeline import HuggingFacePipeline\n\n\n# from transformers import GPTNeoForCausalLM, GPT2Tokenizer\n# model_name = \"EleutherAI/gpt-neo-2.7B\"\n# model = GPTNeoForCausalLM.from_pretrained(model_name)\n# tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n\nimport torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\nmodel_path=\"/kaggle/input/opencodeinterpreterds6.7b/tensorflow2/asis/1/OpenCodeInterpreter-DS-6.7B\"\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\ntokenizer = AutoTokenizer.from_pretrained(model_path)\nmodel = AutoModelForCausalLM.from_pretrained(\n    model_path,\n    torch_dtype=torch.bfloat16 if device == 'cuda' else torch.float32,\n    device_map=\"auto\",\n)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from transformers import pipeline\n# pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=10)\n# llm = HuggingFacePipeline(pipeline=pipe)\n# symbolic_math_chain = LLMSymbolicMathChain.from_llm(llm)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T10:22:01.826923Z","iopub.status.idle":"2024-06-03T10:22:01.827433Z","shell.execute_reply.started":"2024-06-03T10:22:01.827176Z","shell.execute_reply":"2024-06-03T10:22:01.827197Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain\n# from langchain.chains import LLMChain\n# from langchain.prompts import PromptTemplate\n# from langchain_community.llms import Ollama\n# ollama = Ollama(model=\"llama2\")\n\n# # Create a prompt template\n# prompt_template = PromptTemplate(template=\"Convert the following command to SymPy code: {command}\", input_variables=[\"command\"])\n\n# # Create the LLMChain\n# llm_chain = LLMChain(llm=ollama, prompt=prompt_template)\n\n# # Create the LLMSymbolicMathChain\n# symbolic_math_chain = LLMSymbolicMathChain.from_llm(llm_chain)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-03T10:22:01.828911Z","iopub.status.idle":"2024-06-03T10:22:01.829434Z","shell.execute_reply.started":"2024-06-03T10:22:01.82917Z","shell.execute_reply":"2024-06-03T10:22:01.829192Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# command = 'Solve for $\\frac{ar^4}{ar^7}=\\frac{7!}{8!}$'\n# symbolic_math_chain.invoke(command)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T10:22:01.831678Z","iopub.status.idle":"2024-06-03T10:22:01.832045Z","shell.execute_reply.started":"2024-06-03T10:22:01.831872Z","shell.execute_reply":"2024-06-03T10:22:01.831887Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# run for opencode interpreter\ntorch.backends.cuda.enable_mem_efficient_sdp(False)\ntorch.backends.cuda.enable_flash_sdp(False)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T21:16:17.822351Z","iopub.execute_input":"2024-06-10T21:16:17.822861Z","iopub.status.idle":"2024-06-10T21:16:17.82752Z","shell.execute_reply.started":"2024-06-10T21:16:17.822833Z","shell.execute_reply":"2024-06-10T21:16:17.826619Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"import sympy as sp\nimport re\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n# Ensure you have set up your OpenAI API key\n\n# Define the prompt for the GPT-Neo model\nlatex_prompt = \"\"\"\nYou are going to take the following LaTeX mathematical statement and, if needed, convert it into SymPy executable code with the final variable named 'answer'. Not all statements involve solving for an equation or using SymPy. Do not output anything more than the sympy code or the answer. Ensure anything used in the solve function is also defined in the symbol function.\n\nPrompt: {latex_input}\nSymPy:\n\"\"\"\n\n# 'You are going to solve math problems that have a positive integer solution.'\n#         'It is imperative that the final answer is in the brackets of \\\\boxed{}. I will explain how to solve the problem.'\n#         f\"Here is a math problem you are to solve (positive numerical answer): {question}\\n\"\n#         f\"This particular question is a {labelType} question.\\n\"\n#         \"To solve it, first determine a series of logical steps for solving the problem and then follow these steps. \\n\\n\"\n#         \"The final answer should be a positive integer and not an algebraic expression.\\n\"\n#         f\"Here is a gold-standard example of how to solve similar {labelType} problems step-by-step:\\n\\n\"\n#         f\"{exemplar_texts}\\n\\n\"\n#         \"Answer:\"\n\ndef OCgenerate_sympy_code(latex_input):\n    prompt = latex_prompt.format(latex_input=latex_input)\n    inputs = tokenizer(prompt, return_tensors='pt').to(device).input_ids\n    outputs = model.generate(inputs, max_new_tokens=600)\n    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n    # Find the position of the SymPy output to ignore the initial prompt\n    sympy_start_idx = generated_text.find(\"SymPy:\")\n#     sympy_start_idx = re.findall('SymPy:', generated_text, re.DOTALL)[-1]\n\n    if sympy_start_idx != -1:\n        generated_text = generated_text[sympy_start_idx:]\n#     return generated_text\n#     # Extract the SymPy code from the generated text\n    sympy_code_match = re.search(r'```python\\n(.*?)```', generated_text, re.DOTALL)\n#     result_match = re.search(r'```output\\n(.*?)```', generated_text, re.DOTALL)\n    \n    if sympy_code_match:\n        sympy_code = sympy_code_match.group(1).strip()\n#         result = result_match.group(1).strip()\n        if sympy_code:\n            lines = sympy_code.split('\\n')\n            last_line = lines[-1].strip()\n            if 'answer' not in last_line:\n                sympy_code += f'\\nanswer = {last_line}'\n        return sympy_code\n    return \"\", \"\"\n\n# # # Function to execute the generated SymPy code\n# def execute_sympy_code(sympy_code):\n#     exec_globals = {}\n#     exec(sympy_code, exec_globals)\n#     result = exec_globals.get('answer', 'No result variable found')\n#     return result","metadata":{"execution":{"iopub.status.busy":"2024-06-10T22:09:25.993875Z","iopub.execute_input":"2024-06-10T22:09:25.994553Z","iopub.status.idle":"2024-06-10T22:09:26.004524Z","shell.execute_reply.started":"2024-06-10T22:09:25.994517Z","shell.execute_reply":"2024-06-10T22:09:26.003562Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"# !pip install pyautogen","metadata":{"execution":{"iopub.status.busy":"2024-06-03T20:10:16.536054Z","iopub.execute_input":"2024-06-03T20:10:16.536784Z","iopub.status.idle":"2024-06-03T20:10:32.168217Z","shell.execute_reply.started":"2024-06-03T20:10:16.536753Z","shell.execute_reply":"2024-06-03T20:10:32.167133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from autogen import Workflow, Task","metadata":{"execution":{"iopub.status.busy":"2024-06-03T20:19:26.457846Z","iopub.execute_input":"2024-06-03T20:19:26.458784Z","iopub.status.idle":"2024-06-03T20:19:26.502171Z","shell.execute_reply.started":"2024-06-03T20:19:26.45873Z","shell.execute_reply":"2024-06-03T20:19:26.501064Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import torch\n# from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig\n\n\n# class Task:\n#     def __init__(self, func, args):\n#         self.func = func\n#         self.args = args\n\n#     def run(self):\n#         return self.func(*self.args)\n\n# # Define a simple Workflow class\n# class Workflow:\n#     def __init__(self, tasks):\n#         self.tasks = tasks\n\n#     def run(self):\n#         for task in self.tasks:\n#             result = task.run()\n#             print(f\"Task result: {result}\")\n#             # Update args for the next task to include the result if necessary\n#             task.args = task.args[:-1] + (result,)\n            \n\n# def execute_sympy_code(sympy_code):\n#     exec_globals = {}\n#     exec(sympy_code, exec_globals)\n#     result = exec_globals.get('answer', 'No result variable found')\n#     return result\n\n# # Define the DeepSeekMathRL agent\n# class DeepSeekMathRL:\n#     def __init__(self, model_path, device):\n#         self.tokenizer = AutoTokenizer.from_pretrained(model_path)\n#         self.model = AutoModelForCausalLM.from_pretrained(\n#             model_path,\n#             torch_dtype=torch.float16 if device == 'cuda' else torch.float32,\n#             device_map='auto'\n#         )\n#         self.device = device\n\n#     def ask(self, prompt):\n#         inputs = self.tokenizer(prompt, return_tensors='pt').to(self.device).input_ids\n#         outputs = self.model.generate(inputs, max_new_tokens=600)\n#         response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)\n#         return response\n\n#     def solve_step(self, problem, step, input_data=None):\n#         prompt = f\"You are DeepSeekMath-RL, an expert in solving complex mathematical problems. Solve the following step of the problem: {step}\\nProblem: {problem}\\nInput Data: {input_data}\"\n#         response = self.ask(prompt)\n#         return response\n\n# # Define the OpenCodeInterpreter agent\n# class OpenCodeInterpreter:\n#     def __init__(self, model_path, device):\n#         self.tokenizer = AutoTokenizer.from_pretrained(model_path)\n#         self.model = AutoModelForCausalLM.from_pretrained(\n#             model_path,\n#             torch_dtype=torch.float16 if device == 'cuda' else torch.float32,\n#             device_map='auto'\n#         )\n#         self.device = device\n\n#     def ask(self, prompt):\n#         inputs = self.tokenizer(prompt, return_tensors='pt').to(self.device).input_ids\n#         outputs = self.model.generate(inputs, max_new_tokens=600)\n#         response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)\n#         return response\n\n#     def verify_step(self, deepseek_output, step):\n#         prompt = f\"You are OpenCodeInterpreter, a specialized LLM that verifies and generates Sympy code for mathematical problems. Verify and proceed with the following step: {step}\\nDeepSeekMath-RL Output: {deepseek_output}\"\n#         response = self.ask(prompt)\n\n#         # Extract SymPy code from the response\n#         sympy_code_match = re.search(r'SymPy:\\n```python\\n(.*?)```', response, re.DOTALL)\n#         if sympy_code_match:\n#             sympy_code = sympy_code_match.group(1).strip()\n#             if sympy_code:\n#                 sympy_code += \"\\nanswer = result\"  # Ensure 'answer' variable is set in the code\n#                 result = execute_sympy_code(sympy_code)\n#                 return result\n#         return \"SymPy code not found in the response\"\n\n# # Define the problem\n# problem = \"A function f: N -> N satisfies f(f(f(n))) = 8n - 7 and f(2n) = 2f(n) + 1. Calculate f(100).\"\n\n# # Initialize agents\n# device = 'cuda' if torch.cuda.is_available() else 'cpu'\n# model_path_rl = \"/kaggle/input/deepseek-math-7b-rl/transformers/7b-rl/1\"\n# model_path_ci = \"m-a-p/OpenCodeInterpreter-DS-6.7B\"\n\n# deepseek_math_rl = DeepSeekMathRL(model_path_rl, device)\n# open_code_interpreter = OpenCodeInterpreter(model_path_ci, device)\n\n# # Define the workflow tasks\n# tasks = [\n#     Task(deepseek_math_rl.solve_step, (problem, \"Step 1: Understanding the Problem\", None)),\n#     Task(open_code_interpreter.verify_step, (\"DeepSeekMath-RL Output for Step 1\", \"Step 1 Verification\")),\n#     Task(deepseek_math_rl.solve_step, (problem, \"Step 2: Formulating the Equations\", None)),\n#     Task(open_code_interpreter.verify_step, (\"DeepSeekMath-RL Output for Step 2\", \"Step 2 Verification\")),\n#     Task(deepseek_math_rl.solve_step, (problem, \"Step 3: Initial Simplifications\", None)),\n#     Task(open_code_interpreter.verify_step, (\"DeepSeekMath-RL Output for Step 3\", \"Step 3 Verification\")),\n#     Task(deepseek_math_rl.solve_step, (problem, \"Step 4: Strategy Selection\", None)),\n#     Task(open_code_interpreter.verify_step, (\"DeepSeekMath-RL Output for Step 4\", \"Step 4 Verification\")),\n#     Task(deepseek_math_rl.solve_step, (problem, \"Step 5: Detailed Solution Steps\", None)),\n#     Task(open_code_interpreter.verify_step, (\"DeepSeekMath-RL Output for Step 5\", \"Step 5 Verification and Final Calculation\"))\n# ]\n\n# # Create and run the workflow\n# workflow = Workflow(tasks)\n# workflow.run()","metadata":{"execution":{"iopub.status.busy":"2024-06-03T20:38:29.817126Z","iopub.execute_input":"2024-06-03T20:38:29.817738Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Example usage\n# latex_input = \"If $x$ is a real number and $x^2 = 16$, what is the sum of all possible values of $x$?\"\n# latex_input = 'Every morning Aya goes for a $9$-kilometer-long walk and stops at a coffee shop afterwards. When she walks at a constant speed of $s$ kilometers per hour, the walk takes her 4 hours, including $t$ minutes spent in the coffee shop. When she walks $s+2$ kilometers per hour, the walk takes her 2 hours and 24 minutes, including $t$ minutes spent in the coffee shop. Suppose Aya walks at $s+\\frac{1}{2}$ kilometers per hour. Find the number of minutes the walk takes her, including the $t$ minutes spent in the coffee shop.'\nlatex_input = 'Solve for the radius of the circle described by the equation $x^2+y^2 - 7 = 4y-14x+3$.'\nsympy_code = OCgenerate_sympy_code(latex_input)\nprint(\"Generated SymPy Code:\")\nprint(sympy_code)\n\n\nexecution_result = execute_sympy_code(sympy_code)\nprint(\"\\nExecution Result:\")\nprint(execution_result)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T22:06:32.948607Z","iopub.execute_input":"2024-06-10T22:06:32.949311Z","iopub.status.idle":"2024-06-10T22:06:43.010791Z","shell.execute_reply.started":"2024-06-10T22:06:32.949279Z","shell.execute_reply":"2024-06-10T22:06:43.009853Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stderr","text":"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\nSetting `pad_token_id` to `eos_token_id`:32021 for open-end generation.\n","output_type":"stream"},{"name":"stdout","text":"Generated SymPy Code:\nfrom sympy import symbols, Eq, solve\n\nx, y = symbols('x y')\neq = Eq(x**2 + y**2 - 7 + 4*y - 14*x + 3, 0)\nanswer = solve((eq,), (x, y))\n\nExecution Result:\n[(7 - sqrt(-y**2 - 4*y + 53), y), (sqrt(-y**2 - 4*y + 53) + 7, y)]\n","output_type":"stream"}]},{"cell_type":"code","source":"latex_input = \"Express $(5-4i)-2(3+6i)$ as a complex number.\"\nsympy_code = OCgenerate_sympy_code(latex_input)\nprint(\"Generated SymPy Code:\")\nprint(sympy_code)\n\n\nexecution_result = execute_sympy_code(sympy_code)\nprint(\"\\nExecution Result:\")\nprint(execution_result)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T18:59:21.088896Z","iopub.execute_input":"2024-06-10T18:59:21.089828Z","iopub.status.idle":"2024-06-10T18:59:36.601513Z","shell.execute_reply.started":"2024-06-10T18:59:21.089795Z","shell.execute_reply":"2024-06-10T18:59:36.600564Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\nSetting `pad_token_id` to `eos_token_id`:32021 for open-end generation.\n","output_type":"stream"},{"name":"stdout","text":"Generated SymPy Code:\nfrom sympy import *\n\n# define the variables\ni = symbols('i')\n\n# define the expression\nexpr = (5 - 4*i) - 2*(3 + 6*i)\n\n# simplify the expression\nsimplified_expr = simplify(expr)\n\n# assign the simplified expression to the variable 'answer'\nanswer = simplified_expr\n\n# print the answer\nprint(answer)\nanswer = print(answer)\n-16*i - 1\n-16*i - 1\n\nExecution Result:\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"latex_input = \"Let $k, l > 0$ be parameters. The parabola $y = kx^2 - 2kx + l$ intersects the line $y = 4$ at two points $A$ and $B$. These points are distance 6 apart. What is the sum of the squares of the distances from $A$ and $B$ to the origin?\"\nsympy_code = OCgenerate_sympy_code(latex_input)\nprint(\"Generated SymPy Code:\")\nprint(sympy_code)\n\n\nexecution_result = execute_sympy_code(sympy_code)\nprint(\"\\nExecution Result:\")\nprint(execution_result)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T22:09:31.119288Z","iopub.execute_input":"2024-06-10T22:09:31.119655Z","iopub.status.idle":"2024-06-10T22:10:09.721821Z","shell.execute_reply.started":"2024-06-10T22:09:31.119624Z","shell.execute_reply":"2024-06-10T22:10:09.720997Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stderr","text":"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\nSetting `pad_token_id` to `eos_token_id`:32021 for open-end generation.\n","output_type":"stream"},{"name":"stdout","text":"Generated SymPy Code:\nfrom sympy import symbols, Eq, solve\n\nk, l = symbols('k l')\nequation = Eq(k*6**2 - 2*k*6 + 4, l)\nsolution = solve(equation, k, l)\nanswer = solution[0][0]**2 + solution[0][1]**2\n\nExecution Result:\nl**2 + (l/24 - 1/6)**2\n","output_type":"stream"}]},{"cell_type":"code","source":"# latex_input = \"Each of the three-digits numbers $111$ to $999$ is coloured blue or yellow in such a way that the sum of any two (not necessarily different) yellow numbers is equal to a blue number. What is the maximum possible number of yellow numbers there can be?\"\n# sympy_code = OCgenerate_sympy_code(latex_input)\n# print(\"Generated SymPy Code:\")\n# print(sympy_code)\n\n\n# execution_result = execute_sympy_code(sympy_code)\n# print(\"\\nExecution Result:\")\n# print(execution_result)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T11:23:06.69453Z","iopub.execute_input":"2024-06-03T11:23:06.694928Z","iopub.status.idle":"2024-06-03T11:23:24.526895Z","shell.execute_reply.started":"2024-06-03T11:23:06.694897Z","shell.execute_reply":"2024-06-03T11:23:24.524455Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# latex_input = 'What is the intersection of the lines given by $2y=-x+3$ and $-y=5x+1$?  Enter the answer as an ordered pair.'\n# sympy_code = OCgenerate_sympy_code(latex_input)\n# print(\"Generated SymPy Code:\")\n# print(sympy_code)\n\n\n# execution_result = execute_sympy_code(sympy_code)\n# print(\"\\nExecution Result:\")\n# print(execution_result)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T12:07:32.942947Z","iopub.execute_input":"2024-06-03T12:07:32.943306Z","iopub.status.idle":"2024-06-03T12:07:47.825715Z","shell.execute_reply.started":"2024-06-03T12:07:32.943281Z","shell.execute_reply":"2024-06-03T12:07:47.824781Z"},"trusted":true},"execution_count":null,"outputs":[]}]}