defmodule Dspy.CBLEVisionProcessor do
  @moduledoc """
  Advanced vision processing for CBLE exam questions with multi-modal understanding,
  diagram analysis, table extraction, and formula recognition.
  """

  require Logger
  alias Dspy.{EnhancedSignature}

  defstruct [
    :ocr_engine,
    :diagram_analyzer,
    :table_extractor,
    :formula_recognizer,
    :layout_analyzer,
    :visual_qa_engine,
    :enhancement_pipeline
  ]

  @type visual_element :: %{
          type: :text | :diagram | :table | :formula | :chart | :image,
          content: any(),
          bbox: {integer(), integer(), integer(), integer()},
          confidence: float(),
          relationships: [String.t()],
          semantic_meaning: String.t()
        }

  @type visual_analysis :: %{
          elements: [visual_element()],
          layout: map(),
          relationships: [map()],
          extracted_text: String.t(),
          semantic_summary: String.t(),
          visual_complexity: float()
        }

  def new(opts \\ []) do
    %__MODULE__{
      ocr_engine: initialize_ocr_engine(opts),
      diagram_analyzer: initialize_diagram_analyzer(opts),
      table_extractor: initialize_table_extractor(opts),
      formula_recognizer: initialize_formula_recognizer(opts),
      layout_analyzer: initialize_layout_analyzer(opts),
      visual_qa_engine: initialize_visual_qa_engine(opts),
      enhancement_pipeline: configure_enhancement_pipeline(opts)
    }
  end

  # Extract and analyze all visual content from PDF
  def extract_pdf(processor, pdf_path) do
    Logger.info("Extracting visual content from PDF: #{pdf_path}")

    with {:ok, raw_pages} <- extract_pdf_pages(pdf_path),
         {:ok, enhanced_pages} <- enhance_pages(processor, raw_pages),
         {:ok, analyzed_pages} <- analyze_pages_visual(processor, enhanced_pages) do
      {:ok,
       %{
         pages: analyzed_pages,
         total_pages: length(analyzed_pages),
         has_images: has_visual_content?(analyzed_pages),
         visual_summary: generate_visual_summary(analyzed_pages),
         metadata: extract_pdf_metadata(pdf_path)
       }}
    end
  end

  # Enhanced page processing with multi-modal understanding
  defp enhance_pages(processor, pages) do
    enhanced =
      Enum.map(pages, fn page ->
        Task.async(fn ->
          enhance_single_page(processor, page)
        end)
      end)
      |> Task.await_many(30_000)

    {:ok, enhanced}
  end

  defp enhance_single_page(processor, page) do
    # Apply enhancement pipeline
    enhanced_images =
      Enum.map(page["images"] || [], fn image ->
        apply_enhancements(processor, image)
      end)

    Map.put(page, "images", enhanced_images)
  end

  defp apply_enhancements(processor, image) do
    Enum.reduce(processor.enhancement_pipeline, image, fn enhancement, acc ->
      case enhancement do
        :denoise -> denoise_image(acc)
        :contrast -> enhance_contrast(acc)
        :sharpen -> sharpen_image(acc)
        :deskew -> deskew_image(acc)
        :binarize -> binarize_image(acc)
        _ -> acc
      end
    end)
  end

  # Visual content analysis with deep understanding
  defp analyze_pages_visual(processor, pages) do
    analyzed =
      Enum.map(pages, fn page ->
        Task.async(fn ->
          analyze_page_content(processor, page)
        end)
      end)
      |> Task.await_many(60_000)

    {:ok, analyzed}
  end

  defp analyze_page_content(processor, page) do
    # Extract all visual elements
    elements = extract_visual_elements(processor, page)

    # Analyze layout and structure
    layout = analyze_page_layout(processor, page, elements)

    # Detect relationships between elements
    relationships = detect_element_relationships(elements, layout)

    # Generate semantic understanding
    semantic_analysis = generate_semantic_analysis(processor, elements, relationships)

    Map.merge(page, %{
      "visual_elements" => elements,
      "layout_analysis" => layout,
      "element_relationships" => relationships,
      "semantic_analysis" => semantic_analysis,
      "visual_complexity" => calculate_visual_complexity(elements, relationships)
    })
  end

  defp analyze_page_layout(_processor, _page, _elements) do
    # Analyze the page layout structure
    %{
      columns: 1,
      reading_order: :top_to_bottom,
      regions: [],
      margins: %{top: 0, bottom: 0, left: 0, right: 0}
    }
  end

  # Extract different types of visual elements
  defp extract_visual_elements(processor, page) do
    elements = []

    # Extract text regions with OCR
    text_elements = extract_text_regions(processor, page)
    elements = elements ++ text_elements

    # Detect and analyze diagrams
    diagram_elements = detect_diagrams(processor, page)
    elements = elements ++ diagram_elements

    # Extract tables
    table_elements = extract_tables(processor, page)
    elements = elements ++ table_elements

    # Recognize formulas
    formula_elements = recognize_formulas(processor, page)
    elements = elements ++ formula_elements

    # Detect charts and graphs
    chart_elements = detect_charts(processor, page)
    elements = elements ++ chart_elements

    elements
  end

  # Advanced OCR with multiple engines and voting
  defp extract_text_regions(processor, page) do
    images = page["images"] || []

    Enum.flat_map(images, fn image ->
      # Use multiple OCR engines for better accuracy
      ocr_results = run_multiple_ocr(processor, image)

      # Combine results with voting
      combined_text = combine_ocr_results(ocr_results)

      # Segment text into logical regions
      segment_text_regions(combined_text, image)
    end)
  end

  defp run_multiple_ocr(_processor, image) do
    engines = [:tesseract, :easyocr, :paddle_ocr]

    Enum.map(engines, fn engine ->
      Task.async(fn ->
        run_ocr_engine(engine, image)
      end)
    end)
    |> Task.await_many(10_000)
  end

  # Diagram detection and analysis
  defp detect_diagrams(processor, page) do
    images = page["images"] || []

    Enum.flat_map(images, fn image ->
      if is_diagram?(processor, image) do
        analyze_diagram(processor, image)
      else
        []
      end
    end)
  end

  defp is_diagram?(_processor, image) do
    # Use vision model to classify image type
    signature = create_image_classification_signature()

    case Dspy.Module.forward(signature, %{image: image}) do
      {:ok, result} -> result.is_diagram
      _ -> false
    end
  end

  defp analyze_diagram(processor, image) do
    # Extract diagram components
    components = extract_diagram_components(processor, image)

    # Analyze connections and flow
    connections = analyze_diagram_connections(components)

    # Generate diagram description
    description = generate_diagram_description(processor, components, connections)

    [
      %{
        type: :diagram,
        content: %{
          components: components,
          connections: connections,
          description: description
        },
        bbox: extract_bbox(image),
        confidence: 0.9,
        relationships: [],
        semantic_meaning: description
      }
    ]
  end

  # Table extraction with structure understanding
  defp extract_tables(processor, page) do
    images = page["images"] || []

    Enum.flat_map(images, fn image ->
      tables = detect_table_regions(processor, image)

      Enum.map(tables, fn table_region ->
        extract_table_structure(processor, image, table_region)
      end)
    end)
  end

  defp detect_table_regions(_processor, image) do
    # Use table detection model
    signature = create_table_detection_signature()

    case Dspy.Module.forward(signature, %{image: image}) do
      {:ok, result} -> result.table_regions
      _ -> []
    end
  end

  defp create_table_detection_signature do
    EnhancedSignature.new("TableDetection",
      description: "Detect table regions in images",
      input_fields: [
        %{name: :image, type: :image, required: true}
      ],
      output_fields: [
        %{name: :table_regions, type: :list, required: true}
      ],
      vision_enabled: true
    )
  end

  defp extract_table_structure(processor, image, region) do
    # Extract rows and columns
    structure = analyze_table_structure(image, region)

    # Extract cell contents
    cells = extract_table_cells(processor, image, structure)

    # Understand table semantics
    semantics = understand_table_semantics(cells, structure)

    %{
      type: :table,
      content: %{
        structure: structure,
        cells: cells,
        headers: semantics.headers,
        data: semantics.data
      },
      bbox: region.bbox,
      confidence: 0.85,
      relationships: [],
      semantic_meaning: semantics.description
    }
  end

  # Formula recognition with LaTeX conversion
  defp recognize_formulas(processor, page) do
    images = page["images"] || []

    Enum.flat_map(images, fn image ->
      formula_regions = detect_formula_regions(processor, image)

      Enum.map(formula_regions, fn region ->
        recognize_formula(processor, image, region)
      end)
    end)
  end

  defp recognize_formula(processor, image, region) do
    # Extract formula image
    formula_image = crop_image(image, region)

    # Recognize formula and convert to LaTeX
    latex = recognize_formula_to_latex(processor, formula_image)

    # Parse formula structure
    structure = parse_formula_structure(latex)

    # Understand formula meaning
    meaning = understand_formula_meaning(structure)

    %{
      type: :formula,
      content: %{
        latex: latex,
        structure: structure,
        variables: extract_variables(structure),
        operations: extract_operations(structure)
      },
      bbox: region.bbox,
      confidence: 0.8,
      relationships: [],
      semantic_meaning: meaning
    }
  end

  # Visual Question Answering for complex visual understanding
  def answer_visual_question(processor, image, question) do
    # Create VQA signature
    vqa_signature = create_vqa_signature()

    inputs = %{
      image: image,
      question: question,
      visual_elements: extract_visual_elements(processor, %{"images" => [image]})
    }

    case Dspy.Module.forward(vqa_signature, inputs) do
      {:ok, result} ->
        {:ok,
         %{
           answer: result.answer,
           confidence: result.confidence,
           reasoning: result.visual_reasoning,
           relevant_regions: result.relevant_regions
         }}

      error ->
        error
    end
  end

  # Relationship detection between visual elements
  defp detect_element_relationships(elements, layout) do
    relationships = []

    # Spatial relationships
    spatial_rels = detect_spatial_relationships(elements, layout)
    relationships = relationships ++ spatial_rels

    # Semantic relationships
    semantic_rels = detect_semantic_relationships(elements)
    relationships = relationships ++ semantic_rels

    # Reference relationships (e.g., "see Figure 1")
    reference_rels = detect_reference_relationships(elements)
    relationships = relationships ++ reference_rels

    relationships
  end

  defp detect_spatial_relationships(elements, layout) do
    # Analyze proximity, alignment, containment
    Enum.flat_map(elements, fn elem1 ->
      Enum.map(elements, fn elem2 ->
        if elem1 != elem2 do
          analyze_spatial_relationship(elem1, elem2, layout)
        end
      end)
      |> Enum.reject(&is_nil/1)
    end)
  end

  # Semantic analysis generation
  defp generate_semantic_analysis(_processor, elements, relationships) do
    # Create semantic understanding signature
    semantic_signature = create_semantic_analysis_signature()

    inputs = %{
      visual_elements: elements,
      relationships: relationships,
      element_count: length(elements),
      relationship_count: length(relationships)
    }

    case Dspy.Module.forward(semantic_signature, inputs) do
      {:ok, result} ->
        %{
          summary: result.semantic_summary,
          key_concepts: result.key_concepts,
          visual_narrative: result.visual_narrative,
          importance_ranking: result.element_importance
        }

      _ ->
        %{
          summary: "Complex visual content",
          key_concepts: [],
          visual_narrative: "",
          importance_ranking: []
        }
    end
  end

  # Helper functions
  defp create_image_classification_signature do
    EnhancedSignature.new("ImageClassification",
      description: "Classify image type for CBLE content",
      input_fields: [
        %{name: :image, type: :image, required: true}
      ],
      output_fields: [
        %{name: :is_diagram, type: :boolean, required: true},
        %{name: :is_table, type: :boolean, required: true},
        %{name: :is_formula, type: :boolean, required: true},
        %{name: :is_chart, type: :boolean, required: true},
        %{name: :image_type, type: :string, required: true}
      ],
      vision_enabled: true
    )
  end

  defp create_vqa_signature do
    EnhancedSignature.new("VisualQuestionAnswering",
      description: "Answer questions about visual content",
      input_fields: [
        %{name: :image, type: :image, required: true},
        %{name: :question, type: :string, required: true},
        %{name: :visual_elements, type: :list, required: false}
      ],
      output_fields: [
        %{name: :answer, type: :string, required: true},
        %{name: :confidence, type: :float, required: true},
        %{name: :visual_reasoning, type: :string, required: true},
        %{name: :relevant_regions, type: :list, required: true}
      ],
      vision_enabled: true
    )
  end

  defp create_semantic_analysis_signature do
    EnhancedSignature.new("SemanticVisualAnalysis",
      description: "Generate semantic understanding of visual content",
      input_fields: [
        %{name: :visual_elements, type: :list, required: true},
        %{name: :relationships, type: :list, required: true},
        %{name: :element_count, type: :integer, required: true},
        %{name: :relationship_count, type: :integer, required: true}
      ],
      output_fields: [
        %{name: :semantic_summary, type: :string, required: true},
        %{name: :key_concepts, type: :list, required: true},
        %{name: :visual_narrative, type: :string, required: true},
        %{name: :element_importance, type: :list, required: true}
      ]
    )
  end

  defp calculate_visual_complexity(elements, relationships) do
    element_score = length(elements) * 0.1
    relationship_score = length(relationships) * 0.15
    type_diversity = calculate_type_diversity(elements) * 0.25

    min(1.0, element_score + relationship_score + type_diversity)
  end

  defp calculate_type_diversity(elements) do
    unique_types =
      elements
      |> Enum.map(& &1.type)
      |> Enum.uniq()
      |> length()

    unique_types / 5.0
  end

  # Initialization functions
  defp initialize_ocr_engine(_opts) do
    %{
      engines: [:tesseract, :easyocr, :paddle_ocr],
      language_models: [:eng, :equation],
      preprocessing: [:deskew, :denoise, :binarize]
    }
  end

  defp initialize_diagram_analyzer(_opts) do
    %{
      object_detector: :yolo,
      edge_detector: :canny,
      shape_recognizer: :hough,
      connection_analyzer: :graph_based
    }
  end

  defp initialize_table_extractor(_opts) do
    %{
      detection_model: :table_transformer,
      structure_analyzer: :row_column_detection,
      cell_extractor: :connected_components
    }
  end

  defp initialize_formula_recognizer(_opts) do
    %{
      detection_model: :mathpix,
      latex_converter: :im2latex,
      symbol_recognizer: :custom_cnn
    }
  end

  defp initialize_layout_analyzer(_opts) do
    %{
      segmentation_model: :detectron2,
      reading_order_detector: :xy_cut,
      column_detector: :projection_profile
    }
  end

  defp initialize_visual_qa_engine(_opts) do
    %{
      vqa_model: :blip2,
      reasoning_engine: :visual_bert,
      attention_visualizer: :grad_cam
    }
  end

  defp configure_enhancement_pipeline(opts) do
    Keyword.get(opts, :enhancements, [
      :denoise,
      :contrast,
      :sharpen,
      :deskew
    ])
  end

  # Stub implementations for external operations
  defp extract_pdf_pages(_pdf_path) do
    # This would use actual PDF extraction
    {:ok, []}
  end

  defp extract_pdf_metadata(_pdf_path) do
    %{
      created_at: DateTime.utc_now(),
      modified_at: DateTime.utc_now(),
      page_count: 0,
      has_forms: false
    }
  end

  defp denoise_image(image), do: image
  defp enhance_contrast(image), do: image
  defp sharpen_image(image), do: image
  defp deskew_image(image), do: image
  defp binarize_image(image), do: image

  defp run_ocr_engine(_engine, _image), do: {:ok, ""}
  defp combine_ocr_results(_results), do: ""
  defp segment_text_regions(_text, _image), do: []

  defp extract_diagram_components(_processor, _image), do: []
  defp analyze_diagram_connections(_components), do: []
  defp generate_diagram_description(_processor, _components, _connections), do: ""

  defp analyze_table_structure(_image, _region), do: %{}
  defp extract_table_cells(_processor, _image, _structure), do: []

  defp understand_table_semantics(_cells, _structure),
    do: %{headers: [], data: [], description: ""}

  defp detect_formula_regions(_processor, _image), do: []
  defp crop_image(_image, _region), do: %{}
  defp recognize_formula_to_latex(_processor, _formula_image), do: ""
  defp parse_formula_structure(_latex), do: %{}
  defp understand_formula_meaning(_structure), do: ""
  defp extract_variables(_structure), do: []
  defp extract_operations(_structure), do: []

  defp detect_charts(_processor, _page), do: []
  defp extract_bbox(_image), do: {0, 0, 0, 0}

  defp analyze_spatial_relationship(_elem1, _elem2, _layout), do: nil
  defp detect_semantic_relationships(_elements), do: []
  defp detect_reference_relationships(_elements), do: []

  defp has_visual_content?(pages) do
    Enum.any?(pages, fn page ->
      elements = page["visual_elements"] || []
      length(elements) > 0
    end)
  end

  defp generate_visual_summary(pages) do
    total_elements =
      Enum.reduce(pages, 0, fn page, acc ->
        elements = page["visual_elements"] || []
        acc + length(elements)
      end)

    %{
      total_visual_elements: total_elements,
      pages_with_visuals: Enum.count(pages, fn p -> length(p["visual_elements"] || []) > 0 end),
      average_complexity: calculate_average_complexity(pages)
    }
  end

  defp calculate_average_complexity(pages) do
    complexities =
      Enum.map(pages, fn page ->
        page["visual_complexity"] || 0.0
      end)

    if length(complexities) > 0 do
      Enum.sum(complexities) / length(complexities)
    else
      0.0
    end
  end
end
