import os
import time
import numpy as np
from miniprojectutils import *
from miniproject_a import (
    to_grayscale,
    simple_blur,
    get_edge, edge_difference, find_best_tile, reconstruct,
    GIVEN_POS, N_MISSING, GRID, SOLUTION,
)

# ── Step 0: Reconstruct and prepare the map (provided — do not modify) ────────

def prepare_map(tiles_folder: str = "tiles") -> Image:
    """
    Reconstruct the cartoon map from its checkerboard puzzle tiles (using
    functions from Mini-project a), convert to grayscale, and return the
    grayscale image used for all pattern searches.

    Saves:
      imgs/out/cartoon_map_reconstructed.jpg  — reassembled colour map
      imgs/out/cartoon_map_gray.jpg           — grayscale working image

    This function is provided. Do not modify it. This function requires your implementation from miniproject_a.py to work, so complete that first if you haven't already.
    """
    os.makedirs("imgs/out", exist_ok=True)

    given_tiles   = load_given_tiles(tiles_folder, GIVEN_POS)
    missing_tiles = load_missing_tiles(tiles_folder, N_MISSING)

    arrangement = reconstruct(given_tiles, missing_tiles, GRID) # your reconstruct function from miniproject_a.py

    tile_sample    = next(iter(given_tiles.values()))
    tile_h, tile_w = tile_sample.shape[:2]
    canvas         = np.full((GRID * tile_h, GRID * tile_w, 3), 160, dtype=np.uint8)

    for (r, c), tile in given_tiles.items():
        canvas[r*tile_h:(r+1)*tile_h, c*tile_w:(c+1)*tile_w] = \
            np.clip(tile, 0, 255).astype(np.uint8)

    for file_idx, (r, c) in arrangement.items():
        canvas[r*tile_h:(r+1)*tile_h, c*tile_w:(c+1)*tile_w] = \
            np.clip(missing_tiles[file_idx], 0, 255).astype(np.uint8)

    save_image(canvas, "imgs/out/cartoon_map_reconstructed.jpg")
    print("   Saved imgs/out/cartoon_map_reconstructed.jpg")

    img_gray = to_grayscale(canvas) # your to_grayscale function from miniproject_a.py
    save_image(img_gray, "imgs/out/cartoon_map_gray.jpg")
    h, w = dimensions(img_gray)
    print(f"   Saved imgs/out/cartoon_map_gray.jpg  ({w}×{h} px)")

    return img_gray


# ── Part 1: Brute-force pattern search ───────────────────────────────────────

def pattern_difference(
    img_gray:     Image,
    pattern_gray: Image,
    position:     Position,
) -> float:
    """
    Similarity score between pattern_gray and the region of img_gray whose
    top-left corner is at position = (row, col).

    Returns 1.0 if the region falls (even partially) outside img_gray.
    Otherwise returns:

        score = mean(|region - pattern|) / 255   in [0, 1]

    0 = pixel-perfect match, 1 = maximally different.
    No loops — use np.abs, np.mean, and array slicing only.

    Note: this is the same formula as edge_difference in miniproject_a,
    applied to a 2D region instead of a 1D edge strip.
    """
    ph, pw = dimensions(pattern_gray)
    ih, iw = dimensions(img_gray)
    r, c   = position

    # TODO 1: Return 1.0 if the region falls (even partially) outside img_gray.
    # Hint:
    #   if r ... or c ... or r + ph ... or c + pw ...:
    #       return 1.0
    # Replace (or comment) the line below with your implementation.
    raise NotImplementedError("pattern_difference: boundary check not implemented yet")

    # TODO 2: Extract the region from img_gray, then compute and return the score.
    # Break the formula into steps: get the region, compute per-pixel differences,
    # take the absolute value, average them, and normalise to [0, 1].
    # Hint:
    #   region     = img_gray[r : ..., c : ...]
    #   pixel_diff = region.astype(np.int32) - pattern_gray.astype(np.int32)
    #   abs_diff   = np.abs(...)
    #   score      = float(np.mean(...)) / 255.0
    #   return score
    # Note: we convert image to int32 before subtraction to avoid underflow issues with uint8.
    # Replace (or comment) the NotImplementedError line below with your implementation.
    raise NotImplementedError("pattern_difference: score not computed yet")


def find_similar_pattern_position(
    img_gray:     Image,
    pattern_gray: Image,
) -> Position:
    """
    Find the top-left position in img_gray that best matches pattern_gray
    by checking every valid position.

    A valid position (r, c) satisfies:
        0 <= r <= ih - ph   and   0 <= c <= iw - pw

    Returns (0, 0) if the pattern is larger than the image in either dimension.
    A loop over all valid positions is expected here.
    """
    ih, iw = dimensions(img_gray)
    ph, pw = dimensions(pattern_gray)

    if ph > ih or pw > iw:
        return (0, 0)

    best_pos   = (0, 0)
    best_score = float("inf")

    # TODO 3: Loop over all valid (r, c) positions. At each position call
    # pattern_difference and update best_pos / best_score when a lower
    # score is found.
    # Hint:
    #   for r in range(...):
    #       for c in range(...):
    #           score = pattern_difference(img_gray, pattern_gray, (r, c))
    #           if score < ...:
    #               best_score = ...
    #               best_pos   = ...
    # Note: this brute-force search may take a while on large images and patterns, so be patient!
    # Replace (or comment) the NotImplementedError line below with your implementation.
    raise NotImplementedError("find_similar_pattern_position: search loop not implemented yet")

    return best_pos


def highlight_rectangle(
    img:       Image,
    corner:    Position,
    size:      Size,
    value:     int,
    linewidth: int,
) -> Image:
    """
    Return a NEW copy of img with a rectangular border drawn around the
    region defined by corner and size.  Interior pixels are unchanged.

    corner    = (row, col): top-left of the interior.
    size      = (height, width): dimensions of the interior.
    value     : border pixel intensity (0 = black, 255 = white).
    linewidth : border thickness in pixels, drawn OUTSIDE the interior.

    Use clamp(0, limit, x) from miniprojectutils for every index that
    could go out of bounds. 
    clamp(a, b, x) returns x clamped to the range [a, b] inclusive — i.e. the closest value to x that is between a and b.
    """
    result       = copy_image(img)
    img_h, img_w = dimensions(img)
    r0, c0       = corner
    h,  w        = size

    # TODO 4: Compute the outer extents of the border region (the rectangle that
    # surrounds the interior, linewidth pixels thick on each side).
    # Hint:
    #   r_out0 = clamp(0, img_h - 1, r0 - ...)
    #   r_out1 = clamp(0, img_h - 1, r0 + h + ... - 1)
    #   c_out0 = clamp(0, img_w - 1, c0 - ...)
    #   c_out1 = clamp(0, img_w - 1, c0 + w + ... - 1)
    # Replace r_out0, r_out1, c_out0, c_out1 lines below with your implementation.
    r_out0 = None
    r_out1 = None
    c_out0 = None
    c_out1 = None
    # DO NOT remove/modify the NotImplementedError line below.
    if r_out0 is None or r_out1 is None or c_out0 is None or c_out1 is None:
        raise NotImplementedError("highlight_rectangle: outer extents not set yet")

    # TODO 5: Compute the inner extents (pixel coordinates of the interior itself).
    # Hint:
    #   r_in0 = clamp(0, img_h - 1, ...)
    #   r_in1 = clamp(0, img_h - 1, r0 + ... - 1)
    #   c_in0 = clamp(0, img_w - 1, ...)
    #   c_in1 = clamp(0, img_w - 1, c0 + ... - 1)
    # Replace r_in0, r_in1, c_in0, c_in1 lines below with your implementation.
    r_in0 = None
    r_in1 = None
    c_in0 = None
    c_in1 = None
    # DO NOT remove/modify the NotImplementedError line below.
    if r_in0 is None or r_in1 is None or c_in0 is None or c_in1 is None:
        raise NotImplementedError("highlight_rectangle: inner extents not set yet")

    # TODO 6: Fill the four border bands with `value` using numpy slice assignments.
    # The border is the area between the outer and inner extents — four strips:
    # top, bottom, left, right. The top/bottom strips span the full outer width.
    # Hint:
    #   result[r_out0 : ...,         c_out0 : c_out1 + 1] = value  # top
    #   result[... : r_out1 + 1,     c_out0 : c_out1 + 1] = value  # bottom
    #   result[r_in0 : r_in1 + 1,    c_out0 : ...]        = value  # left
    #   result[r_in0 : r_in1 + 1,    ... : c_out1 + 1]    = value  # right
    # Replace (or comment) the NotImplementedError line below with your implementation.
    raise NotImplementedError("highlight_rectangle: border bands not filled yet")

    return result


def highlight_similar_pattern(
    img_gray:     Image,
    pattern_gray: Image,
    value:        int,
    linewidth:    int,
) -> Image:
    """
    Find the best-matching position with find_similar_pattern_position,
    then draw a border around it with highlight_rectangle.
    This function is two lines.
    """
    # TODO 7: Find the best position, then return the image with the border drawn.
    # Hint:
    #   best_pos = find_similar_pattern_position(img_gray, ...)
    #   return highlight_rectangle(img_gray, best_pos, ..., value, linewidth)
    # Replace (or comment) the NotImplementedError line below with your implementation.
    raise NotImplementedError("highlight_similar_pattern is not implemented yet")


# ── Part 2: Accelerated coarse-to-fine search ─────────────────────────────────

def downsample(img_gray: Image, factor: int) -> Image:
    """
    Keep every factor-th pixel in each dimension starting from (0, 0).
    No loops — one numpy slice.

    Always call simple_blur(ksize=factor) before this function so that
    neighbouring pixels are averaged together before any are dropped.
    """
    # TODO 8: Return the downsampled image using a single numpy slice.
    # Recall that array[::N] keeps every N-th element along one axis.
    # Hint:
    #   return img_gray[::..., ::...]
    # Replace (or comment) the NotImplementedError line below with your implementation.
    raise NotImplementedError("downsample is not implemented yet")


def find_most_likely_positions(
    img_gray:     Image,
    pattern_gray: Image,
    top_n:        int,
) -> list[Position]:
    """
    Return the top_n positions with the lowest pattern_difference scores,
    sorted from best (lowest) to worst, built in a single pass.

    We need top_n > 1 because the coarse image is an approximation — the
    true best position at full resolution may not rank first at low resolution.

    Algorithm: maintain a sorted list `best` of at most top_n (score, position)
    pairs.  For each new score:
      - If len(best) < top_n: append the pair and re-sort.
      - Elif score < best[-1][0]: replace best[-1] with the new pair and re-sort.

    Return [pos for _, pos in best].
    """
    ih, iw = dimensions(img_gray)
    ph, pw = dimensions(pattern_gray)

    best: list[tuple[float, Position]] = []

    for r in range(ih - ph + 1):
        for c in range(iw - pw + 1):
            score = pattern_difference(img_gray, pattern_gray, (r, c))

            # TODO 9: Update `best` with (score, (r, c)) using the algorithm
            # described in the docstring above.
            # Hint:
            #   if len(best) < ...:
            #       best.append((score, (r, c)))
            #       best.sort()
            #   elif score < best[...][0]:
            #       best[...] = (score, (r, c))
            #       best.sort()
            # Replace (or comment) the NotImplementedError line below with your implementation.
            raise NotImplementedError("find_most_likely_positions: update logic not implemented yet")

    return [pos for _, pos in best]


def faster_search(
    img_gray:     Image,
    pattern_gray: Image,
    factor:       int,
    top_n:        int,
) -> Position:
    """
    Two-stage coarse-to-fine pattern search.

    Stage 1 — coarse search on a blurred, downsampled image:
      a. Blur img_gray     with simple_blur(ksize=factor).
      b. Blur pattern_gray with simple_blur(ksize=factor).
      c. Downsample both blurred results by factor.
      d. Call find_most_likely_positions on the small image and pattern
         to get the top_n best candidate positions at low resolution.

    Stage 2 — fine search at full resolution:
      For each coarse candidate (sr, sc):
        Scale back: r0 = sr * factor,  c0 = sc * factor
        Try all offsets dr, dc in [-factor//2, +factor//2].
        Skip positions where the pattern would fall outside the image.
        Track the overall best position and score across all candidates.

    Return the best position found in Stage 2.
    """
    # ── Stage 1 ───────────────────────────────────────────────────────────────
    # TODO 10: Blur and downsample both images, then collect the top_n candidates.
    # Hint:
    #   img_blur  = simple_blur(img_gray, ksize=...)
    #   pat_blur  = simple_blur(pattern_gray, ...)
    #   img_small = downsample(img_blur, ...)
    #   pat_small = downsample(...)
    # Replace the img_blur, pat_blur, img_small, pat_small lines below with your implementation.
    img_blur  = None
    pat_blur  = None
    img_small = None
    pat_small = None
    # DO NOT remove/modify the NotImplementedError line below.
    if img_blur is None:
        raise NotImplementedError("faster_search Stage 1: blur/downsample not implemented yet")

    candidates = find_most_likely_positions(img_small, pat_small, top_n)

    # ── Stage 2 ───────────────────────────────────────────────────────────────
    ih, iw = dimensions(img_gray)
    ph, pw = dimensions(pattern_gray)
    half   = factor // 2

    best_score = float("inf")
    best_pos   = (0, 0)

    for (sr, sc) in candidates:
        # Each coarse coordinate maps back to `factor` pixels in the original.
        r0, c0 = sr * factor, sc * factor

        for dr in range(-half, half + 1):
            for dc in range(-half, half + 1):
                r, c = r0 + dr, c0 + dc

                # TODO 11: Skip out-of-bounds positions, then compute the score
                # and update best_pos / best_score if it is a new best.
                # Hint:
                #   if r < 0 or c < 0 or r + ph ... or c + pw ...:
                #       continue
                #   score = pattern_difference(...)
                #   if score < ...:
                #       ...
                # Replace (or comment) the NotImplementedError line below with your implementation.
                raise NotImplementedError("faster_search Stage 2: inner loop not implemented yet")

    return best_pos


def faster_highlight(
    img_gray:     Image,
    pattern_gray: Image,
    value:        int,
    linewidth:    int,
    factor:       int,
    top_n:        int,
) -> Image:
    """
    Find the best match with faster_search, then draw a border around it.
    This function is two lines — same structure as highlight_similar_pattern.
    """
    # TODO 12: Find the best position, then return the image with the border drawn.
    # Hint:
    #   best_pos = faster_search(img_gray, pattern_gray, ...)
    #   return highlight_rectangle(img_gray, best_pos, ..., value, linewidth)
    # Replace (or comment) the NotImplementedError line below with your implementation.
    raise NotImplementedError("faster_highlight is not implemented yet")


# ── Parameters ────────────────────────────────────────────────────────────────
# Open imgs/out/cartoon_map_gray.jpg, pick a visually distinctive region
# (e.g. a building, a landmark, a patch of forest), and set these values.
# Run the pick_pattern_test first to preview your selection before searching.

PATTERN_CORNER = (400, 100)  # (row, col) — top-left of your chosen region
PATTERN_SIZE   = (256, 256)  # (height, width) — size of your chosen region

print("Welcome to Mini-project B! Implement each function where you see a TODO comment. Do not change any function signatures or the parameters above. Run test_miniproject_b.py to check your work and save your results in imgs/out/. Good luck!")