# frozen_string_literal: true

require_relative "endpoint_compliance"

class OpenAiMock < EndpointMock
  def response(content, tool_call: false)
    message_content =
      if tool_call
        { tool_calls: [content] }
      else
        { content: content }
      end

    {
      id: "chatcmpl-6sZfAb30Rnv9Q7ufzFwvQsMpjZh8S",
      object: "chat.completion",
      created: 1_678_464_820,
      model: "gpt-3.5-turbo-0301",
      usage: {
        prompt_tokens: 337,
        completion_tokens: 162,
        total_tokens: 499,
      },
      choices: [
        { message: { role: "assistant" }.merge(message_content), finish_reason: "stop", index: 0 },
      ],
    }
  end

  def stub_response(prompt, response_text, tool_call: false)
    WebMock
      .stub_request(:post, "https://api.openai.com/v1/chat/completions")
      .with(body: request_body(prompt, tool_call: tool_call))
      .to_return(status: 200, body: JSON.dump(response(response_text, tool_call: tool_call)))
  end

  def stream_line(delta, finish_reason: nil, tool_call: false)
    message_content =
      if tool_call
        { tool_calls: [delta] }
      else
        { content: delta }
      end

    +"data: " << {
      id: "chatcmpl-#{SecureRandom.hex}",
      object: "chat.completion.chunk",
      created: 1_681_283_881,
      model: "gpt-3.5-turbo-0301",
      choices: [{ delta: message_content }],
      finish_reason: finish_reason,
      index: 0,
    }.to_json
  end

  def stub_streamed_response(prompt, deltas, tool_call: false)
    chunks =
      deltas.each_with_index.map do |_, index|
        if index == (deltas.length - 1)
          stream_line(deltas[index], finish_reason: "stop_sequence", tool_call: tool_call)
        else
          stream_line(deltas[index], tool_call: tool_call)
        end
      end

    chunks = (chunks.join("\n\n") << "data: [DONE]").split("")

    WebMock
      .stub_request(:post, "https://api.openai.com/v1/chat/completions")
      .with(body: request_body(prompt, stream: true, tool_call: tool_call))
      .to_return(status: 200, body: chunks)
  end

  def tool_deltas
    [
      { id: tool_id, function: {} },
      { id: tool_id, function: { name: "get_weather", arguments: "" } },
      { id: tool_id, function: { name: "get_weather", arguments: "" } },
      { id: tool_id, function: { name: "get_weather", arguments: "{" } },
      { id: tool_id, function: { name: "get_weather", arguments: " \"location\": \"Sydney\"" } },
      { id: tool_id, function: { name: "get_weather", arguments: " ,\"unit\": \"c\" }" } },
    ]
  end

  def tool_response
    {
      id: tool_id,
      function: {
        name: "get_weather",
        arguments: { location: "Sydney", unit: "c" }.to_json,
      },
    }
  end

  def tool_id
    "eujbuebfe"
  end

  def tool_payload
    {
      type: "function",
      function: {
        name: "get_weather",
        description: "Get the weather in a city",
        parameters: {
          type: "object",
          properties: {
            location: {
              type: "string",
              description: "the city name",
            },
            unit: {
              type: "string",
              description: "the unit of measurement celcius c or fahrenheit f",
              enum: %w[c f],
            },
          },
          required: %w[location unit],
        },
      },
    }
  end

  def request_body(prompt, stream: false, tool_call: false)
    model
      .default_options
      .merge(messages: prompt)
      .tap do |b|
        b[:stream] = true if stream
        b[:tools] = [tool_payload] if tool_call
      end
      .to_json
  end
end

RSpec.describe DiscourseAi::Completions::Endpoints::OpenAi do
  subject(:endpoint) do
    described_class.new("gpt-3.5-turbo", DiscourseAi::Tokenizer::OpenAiTokenizer)
  end

  fab!(:user) { Fabricate(:user) }

  let(:open_ai_mock) { OpenAiMock.new(endpoint) }

  let(:compliance) do
    EndpointsCompliance.new(self, endpoint, DiscourseAi::Completions::Dialects::ChatGpt, user)
  end

  describe "#perform_completion!" do
    context "when using regular mode" do
      context "with simple prompts" do
        it "completes a trivial prompt and logs the response" do
          compliance.regular_mode_simple_prompt(open_ai_mock)
        end
      end

      context "with tools" do
        it "returns a function invocation" do
          compliance.regular_mode_tools(open_ai_mock)
        end
      end
    end

    describe "when using streaming mode" do
      context "with simple prompts" do
        it "completes a trivial prompt and logs the response" do
          compliance.streaming_mode_simple_prompt(open_ai_mock)
        end

        it "will automatically recover from a bad payload" do
          # this should not happen, but lets ensure nothing bad happens
          # the row with test1 is invalid json
          raw_data = <<~TEXT.strip
            d|a|t|a|:| |{|"choices":[{"delta":{"content":"test,"}}]}

            data: {"choices":[{"delta":{"content":"test1,"}}]

            data: {"choices":[{"delta":|{"content":"test2,"}}]}

            data: {"choices":[{"delta":{"content":"test3,"}}]|}

            data: {"choices":[{|"|d|elta":{"content":"test4"}}]|}

            data: [D|ONE]
          TEXT

          chunks = raw_data.split("|")

          open_ai_mock.with_chunk_array_support do
            open_ai_mock.stub_streamed_response(compliance.dialect.translate, chunks) do
              partials = []

              endpoint.perform_completion!(compliance.dialect, user) do |partial|
                partials << partial
              end

              expect(partials.join).to eq("test,test1,test2,test3,test4")
            end
          end
        end
      end

      context "with tools" do
        it "returns a function invocation" do
          compliance.streaming_mode_tools(open_ai_mock)
        end
      end
    end
  end
end