"""Calculates the Frechet Inception Distance (FID) to evalulate GANs

The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.

When run as a stand-alone program, it compares the distribution of
images that are stored as PNG/JPEG at a specified location with a
distribution given by summary statistics (in pickle format).

The FID is calculated by assuming that X_1 and X_2 are the activations of
the pool_3 layer of the inception net for generated samples and real world
samples respectively.

See --help to see further details.

Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
of Tensorflow

Copyright 2018 Institute of Bioinformatics, JKU Linz

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import json
import os
import pathlib

import numpy as np
import torch
import torchvision.transforms as TF
from PIL import Image
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d

try:
    from tqdm import tqdm
except ImportError:
    # If tqdm is not available, provide a mock version of it
    def tqdm(x):
        return x

from .inception import InceptionV3
from transformers import CLIPProcessor, CLIPModel, CLIPVisionModel


IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm',
                    'tif', 'tiff', 'webp'}


class ImagePathDataset(torch.utils.data.Dataset):
    def __init__(self, files, transforms=None):
        self.files = files
        self.transforms = transforms

    def __len__(self):
        return len(self.files)

    def __getitem__(self, i):
        path = self.files[i]
        img = Image.open(path).convert('RGB')
        if self.transforms is not None:
            img = self.transforms(img)
        return img

class ImageCaptionDataset(torch.utils.data.Dataset):
    def __init__(self, path, transforms=None):
        self.path = path
        self.transforms = transforms

        self.files = sorted([file for ext in IMAGE_EXTENSIONS
                             for file in pathlib.Path(path).glob('*.{}'.format(ext))])
        self.captions = sorted([file for ext in ["npy"]
                             for file in pathlib.Path(path).glob('*.{}'.format(ext))])

    def __len__(self):
        return len(self.files)

    def __getitem__(self, i):
        img_path = self.files[i]
        img = Image.open(img_path).convert('RGB')
        if self.transforms is not None:
            img = self.transforms(img)
        
        caption = np.load(self.captions[i]) 
        caption = torch.tensor(caption, dtype=torch.float32)  # Assuming caption is a numpy array
        return img, caption
    

def get_activations(files, model, batch_size=50, dims=2048, device='cpu', num_workers=8):
    """Calculates the activations of the pool_3 layer for all images.

    Params:
    -- files       : List of image files paths
    -- model       : Instance of inception model
    -- batch_size  : Batch size of images for the model to process at once.
                     Make sure that the number of samples is a multiple of
                     the batch size, otherwise some samples are ignored. This
                     behavior is retained to match the original FID score
                     implementation.
    -- dims        : Dimensionality of features returned by Inception
    -- device      : Device to run calculations
    -- num_workers : Number of parallel dataloader workers

    Returns:
    -- A numpy array of dimension (num images, dims) that contains the
       activations of the given tensor when feeding inception with the
       query tensor.
    """
    model.eval()

    if batch_size > len(files):
        print(('Warning: batch size is bigger than the data size. '
               'Setting batch size to data size'))
        batch_size = len(files)

    dataset = ImagePathDataset(files, transforms=TF.ToTensor())
    dataloader = torch.utils.data.DataLoader(dataset,
                                             batch_size=batch_size,
                                             shuffle=False,
                                             drop_last=False,
                                             num_workers=num_workers)

    pred_arr = np.empty((len(files), dims))

    start_idx = 0

    for batch in tqdm(dataloader):
        batch = batch.to(device)

        with torch.no_grad():
            pred = model(batch)[0]

        # If model output is not scalar, apply global spatial average pooling.
        # This happens if you choose a dimensionality not equal 2048.
        if pred.size(2) != 1 or pred.size(3) != 1:
            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))

        pred = pred.squeeze(3).squeeze(2).cpu().numpy()

        pred_arr[start_idx:start_idx + pred.shape[0]] = pred

        start_idx = start_idx + pred.shape[0]

    return pred_arr


def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    """Numpy implementation of the Frechet Distance.
    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
    and X_2 ~ N(mu_2, C_2) is
            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).

    Stable version by Dougal J. Sutherland.

    Params:
    -- mu1   : Numpy array containing the activations of a layer of the
               inception net (like returned by the function 'get_predictions')
               for generated samples.
    -- mu2   : The sample mean over activations, precalculated on an
               representative data set.
    -- sigma1: The covariance matrix over activations for generated samples.
    -- sigma2: The covariance matrix over activations, precalculated on an
               representative data set.

    Returns:
    --   : The Frechet Distance.
    """

    mu1 = np.atleast_1d(mu1)
    mu2 = np.atleast_1d(mu2)

    sigma1 = np.atleast_2d(sigma1)
    sigma2 = np.atleast_2d(sigma2)

    assert mu1.shape == mu2.shape, \
        'Training and test mean vectors have different lengths'
    assert sigma1.shape == sigma2.shape, \
        'Training and test covariances have different dimensions'

    diff = mu1 - mu2

    # Product might be almost singular
    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
    if not np.isfinite(covmean).all():
        msg = ('fid calculation produces singular product; '
               'adding %s to diagonal of cov estimates') % eps
        print(msg)
        offset = np.eye(sigma1.shape[0]) * eps
        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))

    # Numerical error might give slight imaginary component
    if np.iscomplexobj(covmean):
        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
            m = np.max(np.abs(covmean.imag))
            raise ValueError('Imaginary component {}'.format(m))
        covmean = covmean.real

    tr_covmean = np.trace(covmean)

    return (diff.dot(diff) + np.trace(sigma1)
            + np.trace(sigma2) - 2 * tr_covmean)


def calculate_activation_statistics(files, model, batch_size=50, dims=2048,
                                    device='cpu', num_workers=8):
    """Calculation of the statistics used by the FID.
    Params:
    -- files       : List of image files paths
    -- model       : Instance of inception model
    -- batch_size  : The images numpy array is split into batches with
                     batch size batch_size. A reasonable batch size
                     depends on the hardware.
    -- dims        : Dimensionality of features returned by Inception
    -- device      : Device to run calculations
    -- num_workers : Number of parallel dataloader workers

    Returns:
    -- mu    : The mean over samples of the activations of the pool_3 layer of
               the inception model.
    -- sigma : The covariance matrix of the activations of the pool_3 layer of
               the inception model.
    """
    act = get_activations(files, model, batch_size, dims, device, num_workers)
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    return mu, sigma


def compute_statistics_of_path(path, model, batch_size, dims, device, num_workers=8):
    if path.endswith('.npz'):
        with np.load(path) as f:
            m, s = f['mu'][:], f['sigma'][:]
    else:
        path = pathlib.Path(path)
        files = sorted([file for ext in IMAGE_EXTENSIONS
                       for file in path.glob('*.{}'.format(ext))])
        m, s = calculate_activation_statistics(files, model, batch_size,
                                               dims, device, num_workers)

    return m, s


def save_statistics_of_path(path, out_path, device=None, batch_size=50, dims=2048, num_workers=8):
    if device is None:
        device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
    else:
        device = torch.device(device)
    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
    model = InceptionV3([block_idx]).to(device)
    m1, s1 = compute_statistics_of_path(path, model, batch_size, dims, device, num_workers)
    np.savez(out_path, mu=m1, sigma=s1)


def get_clip_embeddings(path, model, processor, batch_size=32, device="cpu", num_workers=4):
    """Compute CLIP embeddings for images and captions."""
    dataset = ImageCaptionDataset(path, transforms=TF.ToTensor())
    dataloader = torch.utils.data.DataLoader(dataset,
                                             batch_size=batch_size,
                                             shuffle=False,
                                             num_workers=num_workers)

    all_img_embeds, all_txt_embeds = [], []

    with torch.no_grad():
        for images, texts in tqdm(dataloader):
            images = images.to(device)

            inputs = processor(images=images, return_tensors="pt").to(device)
            img_embeds = model.get_image_features(**inputs)
            
            txt_embeds = texts.to(device)
            text_projection = model.text_projection
            pooled = txt_embeds[:, -1, :]
            txt_embeds = pooled @ text_projection.weight.T.to(device)
            #txt_embeds = torch.nn.functional.normalize(text_features, dim=-1)

            # Normalize
            img_embeds /= img_embeds.norm(dim=-1, keepdim=True)
            txt_embeds /= txt_embeds.norm(dim=-1, keepdim=True)

            all_img_embeds.append(img_embeds.cpu())
            all_txt_embeds.append(txt_embeds.cpu())

    all_img_embeds = torch.cat(all_img_embeds, dim=0)
    all_txt_embeds = torch.cat(all_txt_embeds, dim=0)

    return all_img_embeds, all_txt_embeds


def calculate_clipscore(path, model, processor, batch_size=32, device="cpu", num_workers=4):
    """Compute mean CLIPScore across dataset."""
    img_embeds, txt_embeds = get_clip_embeddings(path,
                                                 model,
                                                 processor,
                                                 batch_size,
                                                 device,
                                                 num_workers)
    
    print(f"Image embeddings shape: {img_embeds.shape}")
    print(f"Text embeddings shape: {txt_embeds.shape}")

    # Modify txt_embeds to match img_embeds shape
    #text_projection = model.text_projection
    #pooled = txt_embeds[:, -1, :].to(device) 
    #text_features = pooled @ text_projection.weight.T.to(device) + text_projection.bias.to(device)
    #text_features = torch.nn.functional.normalize(text_features, dim=-1)

    #print(f"Image embeddings shape: {img_embeds.shape}")
    #print(f"Text embeddings shape: {txt_embeds.shape}")

    # Cosine similarity for paired (image, text)
    scores = (img_embeds * txt_embeds).sum(dim=-1)  # dot product since normalized
    mean_score = scores.mean().item()

    return mean_score, scores.numpy()


"""
Main function to run the FID and ClipScore calculations.
"""
def calculate_metrics_given_paths(
        path_original,
        path_generated,
        device=None,
        batch_size=50,
        dims=2048,
        num_workers=8
    ):
    """Calculates the FID of original and generated images and the CLIP score of generated images and captions.""" 
    # Device
    if device is None:
        device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
    else:
        device = torch.device(device)

    # Check paths
    for p in [path_original, path_generated]:
        if not os.path.exists(p):
            raise RuntimeError('Invalid path: %s' % p)

    # Load and define Inception model
    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
    model = InceptionV3([block_idx]).to(device)

    # Compute FID
    m1, s1 = compute_statistics_of_path(path_original, model, batch_size,
                                        dims, device, num_workers)
    m2, s2 = compute_statistics_of_path(path_generated, model, batch_size,
                                        dims, device, num_workers)
    fid_value = calculate_frechet_distance(m1, s1, m2, s2)

    # Compute CLIP score 
    model = CLIPModel.from_pretrained("/gpfs/projects/bsc70/bsc193242/Models/clip-vit-large-patch14", use_safetensors=False).to(device)
    processor = CLIPProcessor.from_pretrained("/gpfs/projects/bsc70/bsc193242/Models/clip-vit-large-patch14")
    mean_score, scores = calculate_clipscore(path_generated,
                                             model,
                                             processor,
                                             device=device,
                                             batch_size=batch_size,
                                             num_workers=num_workers)

    return fid_value, mean_score, scores


def main(args):
    """Main function to run the FID and CLIP score calculations."""
    fid_value, mean_clip_score, clip_scores = calculate_metrics_given_paths(
        args.path_original,
        args.path_generated,
        device=args.device,
        batch_size=args.batch_size,
        dims=args.dims,
        num_workers=args.num_workers
    )

    print(f"FID: {fid_value}")
    print(f"Mean CLIP Score: {mean_clip_score}")
    print(f"Individual CLIP Scores: {clip_scores[:10]}")  # Print first 10 scores for brevity 

    # Save results to a file
    results = {
        "fid": fid_value,
        "mean_clip_score": mean_clip_score,
        "clip_scores": clip_scores.tolist()  # Convert to list for JSON serialization
    }
    results_path = os.path.join(args.path_generated, "metrics_results.json")
    with open(results_path, 'w') as f:
        json.dump(results, f, indent=4)
    print(f"Results saved to {results_path}")


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Calculate FID and CLIP score.")
    parser.add_argument('--path_original', type=str, required=True, help='Path to original images.')
    parser.add_argument('--path_generated', type=str, required=True, help='Path to generated images.')
    parser.add_argument('--device', type=str, default=None, help='Device to run calculations (e.g., "cuda" or "cpu").')
    parser.add_argument('--batch_size', type=int, default=50, help='Batch size for processing images.')
    parser.add_argument('--dims', type=int, default=2048, help='Dimensionality of features returned by Inception.')
    parser.add_argument('--num_workers', type=int, default=8, help='Number of workers for data loading.')

    args = parser.parse_args()
    main(args)