"""

prepare_classification_script.py

Notebook-y script used to prepare a series of shell commands to run a classifier
(other than MegaClassifier) on a MegaDetector result set.

Differs from prepare_classification_script_mc.py only in the final class mapping step.

"""

#%% Job options

import os

def main(): # noqa
    organization_name = 'idfg'
    job_name = 'idfg-2022-01-27-EOE2021S_Group6'
    input_filename = 'idfg-2022-01-27-EOE2021S_Group6_detections.filtered_rde_0.60_0.85_30_0.20.json'
    image_base = '/datadrive/idfg/EOE2021S_Group6'
    crop_path = os.path.join(os.path.expanduser('~/crops'),job_name + '_crops')
    device_id = 1

    working_dir_base = os.path.join(os.path.expanduser('~/postprocessing'),
                                                    organization_name,
                                                    job_name)

    output_base = os.path.join(working_dir_base,'combined_api_outputs')

    assert os.path.isdir(working_dir_base)
    assert os.path.isdir(output_base)

    output_file = os.path.join(working_dir_base,'run_idfgclassifier_' + job_name +  '.sh')

    input_files = [
        os.path.join(
            os.path.expanduser('~/postprocessing'),
                            organization_name,
                            job_name,
                            'combined_api_outputs',
                            input_filename
            )
        ]

    for fn in input_files:
        assert os.path.isfile(fn)


    #%% Constants

    include_cropping = False

    classifier_base = os.path.expanduser('~/models/camera_traps/idfg_classifier/idfg_classifier_20200905_042558')
    assert os.path.isdir(classifier_base)

    checkpoint_path = os.path.join(classifier_base,'idfg_classifier_ckpt_14_compiled.pt')
    assert os.path.isfile(checkpoint_path)

    classifier_categories_path = os.path.join(classifier_base,'label_index.json')
    assert os.path.isfile(classifier_categories_path)

    classifier_output_suffix = '_idfg_classifier_output.csv.gz'
    final_output_suffix = '_idfgclassifier.json'

    threshold_str = '0.65'
    n_threads_str = '50'
    image_size_str = '300'
    batch_size_str = '64'
    num_workers_str = '8'
    logdir = working_dir_base

    classification_threshold_str = '0.05'

    # This is just passed along to the metadata in the output file, it has no impact
    # on how the classification scripts run.
    typical_classification_threshold_str = '0.75'

    classifier_name = 'idfg4'


    #%% Set up environment

    commands = []
    # commands.append('cd MegaDetector/classification\n')
    # commands.append('conda activate cameratraps-classifier\n')


    #%% Crop images

    if include_cropping:

        commands.append('\n### Cropping ###\n')

        # fn = input_files[0]
        for fn in input_files:

            input_file_path = fn
            crop_cmd = ''

            crop_comment = '\n# Cropping {}\n'.format(fn)
            crop_cmd += crop_comment

            crop_cmd += "python crop_detections.py \\\n" + \
                input_file_path + ' \\\n' + \
                crop_path + ' \\\n' + \
                '--images-dir "' + image_base + '"' + ' \\\n' + \
                '--threshold "' + threshold_str + '"' + ' \\\n' + \
                '--square-crops ' + ' \\\n' + \
                '--threads "' + n_threads_str + '"' + ' \\\n' + \
                '--logdir "' + logdir + '"' + ' \\\n' + \
                '\n'
            crop_cmd = '{}'.format(crop_cmd)
            commands.append(crop_cmd)


    #%% Run classifier

    commands.append('\n### Classifying ###\n')

    # fn = input_files[0]
    for fn in input_files:

        input_file_path = fn
        classifier_output_path = crop_path + classifier_output_suffix

        classify_cmd = ''

        classify_comment = '\n# Classifying {}\n'.format(fn)
        classify_cmd += classify_comment

        classify_cmd += "python run_classifier.py \\\n" + \
            checkpoint_path + ' \\\n' + \
            crop_path + ' \\\n' + \
            classifier_output_path + ' \\\n' + \
            '--detections-json "' + input_file_path + '"' + ' \\\n' + \
            '--classifier-categories "' + classifier_categories_path + '"' + ' \\\n' + \
            '--image-size "' + image_size_str + '"' + ' \\\n' + \
            '--batch-size "' + batch_size_str + '"' + ' \\\n' + \
            '--num-workers "' + num_workers_str + '"' + ' \\\n'

        if device_id is not None:
            classify_cmd += '--device {}'.format(device_id)

        classify_cmd += '\n\n'
        classify_cmd = '{}'.format(classify_cmd)
        commands.append(classify_cmd)


    #%% Merge classification and detection outputs

    commands.append('\n### Merging ###\n')

    # fn = input_files[0]
    for fn in input_files:

        input_file_path = fn
        classifier_output_path = crop_path + classifier_output_suffix
        final_output_path = os.path.join(output_base,
                                        os.path.basename(classifier_output_path)).\
                                        replace(classifier_output_suffix,
                                        final_output_suffix)
        final_output_path = final_output_path.replace('_detections','')
        final_output_path = final_output_path.replace('_crops','')

        merge_cmd = ''

        merge_comment = '\n# Merging {}\n'.format(fn)
        merge_cmd += merge_comment

        merge_cmd += "python merge_classification_detection_output.py \\\n" + \
            classifier_output_path + ' \\\n' + \
            classifier_categories_path + ' \\\n' + \
            '--output-json "' + final_output_path + '"' + ' \\\n' + \
            '--detection-json "' + input_file_path + '"' + ' \\\n' + \
            '--classifier-name "' + classifier_name + '"' + ' \\\n' + \
            '--threshold "' + classification_threshold_str + '"' + ' \\\n' + \
            '--typical-confidence-threshold "' + typical_classification_threshold_str + '"' + ' \\\n' + \
            '\n'
        merge_cmd = '{}'.format(merge_cmd)
        commands.append(merge_cmd)


    #%% Write everything out

    with open(output_file,'w') as f:
        for s in commands:
            f.write('{}'.format(s))

    import stat
    st = os.stat(output_file)
    os.chmod(output_file, st.st_mode | stat.S_IEXEC)

if __name__ == '__main__':
    main()
