216 lines
6.7 KiB
Lua
216 lines
6.7 KiB
Lua
local Utils = require("avante.utils")
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local Providers = require("avante.providers")
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local Clipboard = require("avante.clipboard")
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local OpenAI = require("avante.providers").openai
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---@class AvanteProviderFunctor
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local M = {}
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M.api_key_name = "GEMINI_API_KEY"
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M.role_map = {
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user = "user",
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assistant = "model",
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}
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function M:is_disable_stream() return false end
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---@param tool AvanteLLMTool
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function M:transform_to_function_declaration(tool)
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local input_schema_properties, required = Utils.llm_tool_param_fields_to_json_schema(tool.param.fields)
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local parameters = nil
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if not vim.tbl_isempty(input_schema_properties) then
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parameters = {
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type = "object",
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properties = input_schema_properties,
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required = required,
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}
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end
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return {
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name = tool.name,
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description = tool.get_description and tool.get_description() or tool.description,
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parameters = parameters,
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}
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end
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function M:parse_messages(opts)
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local contents = {}
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local prev_role = nil
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local tool_id_to_name = {}
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vim.iter(opts.messages):each(function(message)
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local role = message.role
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if role == prev_role then
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if role == M.role_map["user"] then
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table.insert(
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contents,
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{ role = M.role_map["assistant"], parts = {
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{ text = "Ok, I understand." },
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} }
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)
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else
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table.insert(contents, { role = M.role_map["user"], parts = {
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{ text = "Ok" },
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} })
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end
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end
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prev_role = role
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local parts = {}
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local content_items = message.content
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if type(content_items) == "string" then
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table.insert(parts, { text = content_items })
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elseif type(content_items) == "table" then
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---@cast content_items AvanteLLMMessageContentItem[]
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for _, item in ipairs(content_items) do
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if type(item) == "string" then
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table.insert(parts, { text = item })
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elseif type(item) == "table" and item.type == "text" then
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table.insert(parts, { text = item.text })
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elseif type(item) == "table" and item.type == "image" then
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table.insert(parts, {
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inline_data = {
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mime_type = "image/png",
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data = item.source.data,
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},
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})
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elseif type(item) == "table" and item.type == "tool_use" then
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tool_id_to_name[item.id] = item.name
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role = "model"
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table.insert(parts, {
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functionCall = {
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name = item.name,
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args = item.input,
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},
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})
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elseif type(item) == "table" and item.type == "tool_result" then
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role = "function"
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local ok, content = pcall(vim.json.decode, item.content)
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if not ok then content = item.content end
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table.insert(parts, {
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functionResponse = {
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name = tool_id_to_name[item.tool_use_id],
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response = {
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name = tool_id_to_name[item.tool_use_id],
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content = content,
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},
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},
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})
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elseif type(item) == "table" and item.type == "thinking" then
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table.insert(parts, { text = item.thinking })
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elseif type(item) == "table" and item.type == "redacted_thinking" then
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table.insert(parts, { text = item.data })
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end
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end
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end
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table.insert(contents, { role = M.role_map[role] or role, parts = parts })
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end)
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if Clipboard.support_paste_image() and opts.image_paths then
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for _, image_path in ipairs(opts.image_paths) do
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local image_data = {
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inline_data = {
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mime_type = "image/png",
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data = Clipboard.get_base64_content(image_path),
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},
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}
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table.insert(contents[#contents].parts, image_data)
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end
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end
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return {
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systemInstruction = {
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role = "user",
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parts = {
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{
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text = opts.system_prompt,
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},
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},
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},
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contents = contents,
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}
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end
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function M:parse_response(ctx, data_stream, _, opts)
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local ok, json = pcall(vim.json.decode, data_stream)
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if not ok then opts.on_stop({ reason = "error", error = json }) end
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if json.candidates and #json.candidates > 0 then
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local candidate = json.candidates[1]
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---@type AvanteLLMToolUse[]
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local tool_use_list = {}
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if candidate.content.parts ~= nil then
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for _, part in ipairs(candidate.content.parts) do
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if part.text then
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if opts.on_chunk then opts.on_chunk(part.text) end
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OpenAI:add_text_message(ctx, part.text, "generating", opts)
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elseif part.functionCall then
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if not ctx.function_call_id then ctx.function_call_id = 0 end
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ctx.function_call_id = ctx.function_call_id + 1
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local tool_use = {
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id = ctx.session_id .. "-" .. tostring(ctx.function_call_id),
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name = part.functionCall.name,
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input_json = vim.json.encode(part.functionCall.args),
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}
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table.insert(tool_use_list, tool_use)
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OpenAI:add_tool_use_message(tool_use, "generated", opts)
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end
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end
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end
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if candidate.finishReason and candidate.finishReason == "STOP" then
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OpenAI:finish_pending_messages(ctx, opts)
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if #tool_use_list > 0 then
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opts.on_stop({ reason = "tool_use", tool_use_list = tool_use_list })
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else
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opts.on_stop({ reason = "complete" })
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end
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end
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else
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OpenAI:finish_pending_messages(ctx, opts)
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opts.on_stop({ reason = "complete" })
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end
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end
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function M:parse_curl_args(prompt_opts)
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local provider_conf, request_body = Providers.parse_config(self)
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local disable_tools = provider_conf.disable_tools or false
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request_body = vim.tbl_deep_extend("force", request_body, {
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generationConfig = {
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temperature = request_body.temperature,
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maxOutputTokens = request_body.max_tokens,
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},
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})
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request_body.temperature = nil
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request_body.max_tokens = nil
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local api_key = self.parse_api_key()
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if api_key == nil then error("Cannot get the gemini api key!") end
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local function_declarations = {}
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if not disable_tools and prompt_opts.tools then
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for _, tool in ipairs(prompt_opts.tools) do
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table.insert(function_declarations, self:transform_to_function_declaration(tool))
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end
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end
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if #function_declarations > 0 then
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request_body.tools = {
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{
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functionDeclarations = function_declarations,
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},
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}
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end
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return {
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url = Utils.url_join(
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provider_conf.endpoint,
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provider_conf.model .. ":streamGenerateContent?alt=sse&key=" .. api_key
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),
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proxy = provider_conf.proxy,
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insecure = provider_conf.allow_insecure,
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headers = { ["Content-Type"] = "application/json" },
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body = vim.tbl_deep_extend("force", {}, self:parse_messages(prompt_opts), request_body),
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}
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end
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return M
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