from typing import Any, List, Literal, Optional
from argparse import ArgumentParser
from time import sleep
import cv2
import numpy
import onnxruntime

import facefusion.globals
import facefusion.processors.frame.core as frame_processors
from facefusion import config, process_manager, logger, wording
from facefusion.face_analyser import get_many_faces, clear_face_analyser, find_similar_faces, get_one_face
from facefusion.face_masker import create_static_box_mask, create_occlusion_mask, clear_face_occluder
from facefusion.face_helper import warp_face_by_face_landmark_5, paste_back
from facefusion.execution import apply_execution_provider_options
from facefusion.content_analyser import clear_content_analyser
from facefusion.face_store import get_reference_faces
from facefusion.normalizer import normalize_output_path
from facefusion.thread_helper import thread_lock, thread_semaphore
from facefusion.typing import Face, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
from facefusion.common_helper import create_metavar
from facefusion.filesystem import is_file, is_image, is_video, resolve_relative_path
from facefusion.download import conditional_download, is_download_done
from facefusion.vision import read_image, read_static_image, write_image
from facefusion.processors.frame.typings import FaceEnhancerInputs
from facefusion.processors.frame import globals as frame_processors_globals
from facefusion.processors.frame import choices as frame_processors_choices

FRAME_PROCESSOR = None
NAME = __name__.upper()
MODELS : ModelSet =\
{
	'codeformer':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/codeformer.onnx',
		'path': resolve_relative_path('../.assets/models/codeformer.onnx'),
		'template': 'ffhq_512',
		'size': (512, 512)
	},
	'gfpgan_1.2':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.2.onnx',
		'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx'),
		'template': 'ffhq_512',
		'size': (512, 512)
	},
	'gfpgan_1.3':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.3.onnx',
		'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx'),
		'template': 'ffhq_512',
		'size': (512, 512)
	},
	'gfpgan_1.4':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.4.onnx',
		'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx'),
		'template': 'ffhq_512',
		'size': (512, 512)
	},
	'gpen_bfr_256':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_256.onnx',
		'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx'),
		'template': 'arcface_128_v2',
		'size': (256, 256)
	},
	'gpen_bfr_512':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_512.onnx',
		'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx'),
		'template': 'ffhq_512',
		'size': (512, 512)
	},
	'gpen_bfr_1024':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_1024.onnx',
		'path': resolve_relative_path('../.assets/models/gpen_bfr_1024.onnx'),
		'template': 'ffhq_512',
		'size': (1024, 1024)
	},
	'gpen_bfr_2048':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_2048.onnx',
		'path': resolve_relative_path('../.assets/models/gpen_bfr_2048.onnx'),
		'template': 'ffhq_512',
		'size': (2048, 2048)
	},
	'restoreformer_plus_plus':
	{
		'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/restoreformer_plus_plus.onnx',
		'path': resolve_relative_path('../.assets/models/restoreformer_plus_plus.onnx'),
		'template': 'ffhq_512',
		'size': (512, 512)
	}
}
OPTIONS : Optional[OptionsWithModel] = None


def get_frame_processor() -> Any:
	global FRAME_PROCESSOR

	with thread_lock():
		while process_manager.is_checking():
			sleep(0.5)
		if FRAME_PROCESSOR is None:
			model_path = get_options('model').get('path')
			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(facefusion.globals.execution_device_id, facefusion.globals.execution_providers))
	return FRAME_PROCESSOR


def clear_frame_processor() -> None:
	global FRAME_PROCESSOR

	FRAME_PROCESSOR = None


def get_options(key : Literal['model']) -> Any:
	global OPTIONS

	if OPTIONS is None:
		OPTIONS =\
		{
			'model': MODELS[frame_processors_globals.face_enhancer_model]
		}
	return OPTIONS.get(key)


def set_options(key : Literal['model'], value : Any) -> None:
	global OPTIONS

	OPTIONS[key] = value


def register_args(program : ArgumentParser) -> None:
	program.add_argument('--face-enhancer-model', help = wording.get('help.face_enhancer_model'), default = config.get_str_value('frame_processors.face_enhancer_model', 'gfpgan_1.4'), choices = frame_processors_choices.face_enhancer_models)
	program.add_argument('--face-enhancer-blend', help = wording.get('help.face_enhancer_blend'), type = int, default = config.get_int_value('frame_processors.face_enhancer_blend', '80'), choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range))


def apply_args(program : ArgumentParser) -> None:
	args = program.parse_args()
	frame_processors_globals.face_enhancer_model = args.face_enhancer_model
	frame_processors_globals.face_enhancer_blend = args.face_enhancer_blend


def pre_check() -> bool:
	download_directory_path = resolve_relative_path('../.assets/models')
	model_url = get_options('model').get('url')
	model_path = get_options('model').get('path')

	if not facefusion.globals.skip_download:
		process_manager.check()
		conditional_download(download_directory_path, [ model_url ])
		process_manager.end()
	return is_file(model_path)


def post_check() -> bool:
	model_url = get_options('model').get('url')
	model_path = get_options('model').get('path')

	if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
		logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
		return False
	if not is_file(model_path):
		logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
		return False
	return True


def pre_process(mode : ProcessMode) -> bool:
	if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
		logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
		return False
	if mode == 'output' and not normalize_output_path(facefusion.globals.target_path, facefusion.globals.output_path):
		logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
		return False
	return True


def post_process() -> None:
	read_static_image.cache_clear()
	if facefusion.globals.video_memory_strategy == 'strict' or facefusion.globals.video_memory_strategy == 'moderate':
		clear_frame_processor()
	if facefusion.globals.video_memory_strategy == 'strict':
		clear_face_analyser()
		clear_content_analyser()
		clear_face_occluder()


def enhance_face(target_face: Face, temp_vision_frame : VisionFrame) -> VisionFrame:
	model_template = get_options('model').get('template')
	model_size = get_options('model').get('size')
	crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), model_template, model_size)
	box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], facefusion.globals.face_mask_blur, (0, 0, 0, 0))
	crop_mask_list =\
	[
		box_mask
	]

	if 'occlusion' in facefusion.globals.face_mask_types:
		occlusion_mask = create_occlusion_mask(crop_vision_frame)
		crop_mask_list.append(occlusion_mask)
	crop_vision_frame = prepare_crop_frame(crop_vision_frame)
	crop_vision_frame = apply_enhance(crop_vision_frame)
	crop_vision_frame = normalize_crop_frame(crop_vision_frame)
	crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
	paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
	temp_vision_frame = blend_frame(temp_vision_frame, paste_vision_frame)
	return temp_vision_frame


def apply_enhance(crop_vision_frame : VisionFrame) -> VisionFrame:
	frame_processor = get_frame_processor()
	frame_processor_inputs = {}

	for frame_processor_input in frame_processor.get_inputs():
		if frame_processor_input.name == 'input':
			frame_processor_inputs[frame_processor_input.name] = crop_vision_frame
		if frame_processor_input.name == 'weight':
			weight = numpy.array([ 1 ]).astype(numpy.double)
			frame_processor_inputs[frame_processor_input.name] = weight
	with thread_semaphore():
		crop_vision_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
	return crop_vision_frame


def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
	crop_vision_frame = crop_vision_frame[:, :, ::-1] / 255.0
	crop_vision_frame = (crop_vision_frame - 0.5) / 0.5
	crop_vision_frame = numpy.expand_dims(crop_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
	return crop_vision_frame


def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
	crop_vision_frame = numpy.clip(crop_vision_frame, -1, 1)
	crop_vision_frame = (crop_vision_frame + 1) / 2
	crop_vision_frame = crop_vision_frame.transpose(1, 2, 0)
	crop_vision_frame = (crop_vision_frame * 255.0).round()
	crop_vision_frame = crop_vision_frame.astype(numpy.uint8)[:, :, ::-1]
	return crop_vision_frame


def blend_frame(temp_vision_frame : VisionFrame, paste_vision_frame : VisionFrame) -> VisionFrame:
	face_enhancer_blend = 1 - (frame_processors_globals.face_enhancer_blend / 100)
	temp_vision_frame = cv2.addWeighted(temp_vision_frame, face_enhancer_blend, paste_vision_frame, 1 - face_enhancer_blend, 0)
	return temp_vision_frame


def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
	return enhance_face(target_face, temp_vision_frame)


def process_frame(inputs : FaceEnhancerInputs) -> VisionFrame:
	reference_faces = inputs.get('reference_faces')
	target_vision_frame = inputs.get('target_vision_frame')

	if facefusion.globals.face_selector_mode == 'many':
		many_faces = get_many_faces(target_vision_frame)
		if many_faces:
			for target_face in many_faces:
				target_vision_frame = enhance_face(target_face, target_vision_frame)
	if facefusion.globals.face_selector_mode == 'one':
		target_face = get_one_face(target_vision_frame)
		if target_face:
			target_vision_frame = enhance_face(target_face, target_vision_frame)
	if facefusion.globals.face_selector_mode == 'reference':
		similar_faces = find_similar_faces(reference_faces, target_vision_frame, facefusion.globals.reference_face_distance)
		if similar_faces:
			for similar_face in similar_faces:
				target_vision_frame = enhance_face(similar_face, target_vision_frame)
	return target_vision_frame


def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None

	for queue_payload in process_manager.manage(queue_payloads):
		target_vision_path = queue_payload['frame_path']
		target_vision_frame = read_image(target_vision_path)
		output_vision_frame = process_frame(
		{
			'reference_faces': reference_faces,
			'target_vision_frame': target_vision_frame
		})
		write_image(target_vision_path, output_vision_frame)
		update_progress(1)


def process_image(source_path : str, target_path : str, output_path : str) -> None:
	reference_faces = get_reference_faces() if 'reference' in facefusion.globals.face_selector_mode else None
	target_vision_frame = read_static_image(target_path)
	output_vision_frame = process_frame(
	{
		'reference_faces': reference_faces,
		'target_vision_frame': target_vision_frame
	})
	write_image(output_path, output_vision_frame)


def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
	frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)