# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
# 
# 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 os

from ...base import BaseModel
from ...base.utils.arg import CLIArgument
from ...base.utils.subprocess import CompletedProcess
from ....utils.misc import abspath
from ....utils import logging


class TextRecModel(BaseModel):
    """ Text Recognition Model """

    METRICS = [
        'acc', 'norm_edit_dis', 'Teacher_acc', 'Teacher_norm_edit_dis',
        'precision', 'recall', 'hmean'
    ]

    def train(self,
              batch_size: int=None,
              learning_rate: float=None,
              epochs_iters: int=None,
              ips: str=None,
              device: str='gpu',
              resume_path: str=None,
              dy2st: bool=False,
              amp: str='OFF',
              num_workers: int=None,
              use_vdl: bool=True,
              save_dir: str=None,
              **kwargs) -> CompletedProcess:
        """train self

        Args:
            batch_size (int, optional): the train batch size value. Defaults to None.
            learning_rate (float, optional): the train learning rate value. Defaults to None.
            epochs_iters (int, optional): the train epochs value. Defaults to None.
            ips (str, optional): the ip addresses of nodes when using distribution. Defaults to None.
            device (str, optional): the running device. Defaults to 'gpu'.
            resume_path (str, optional): the checkpoint file path to resume training. Train from scratch if it is set
                to None. Defaults to None.
            dy2st (bool, optional): Enable dynamic to static. Defaults to False.
            amp (str, optional): the amp settings. Defaults to 'OFF'.
            num_workers (int, optional): the workers number. Defaults to None.
            use_vdl (bool, optional): enable VisualDL. Defaults to True.
            save_dir (str, optional): the directory path to save train output. Defaults to None.

        Returns:
           CompletedProcess: the result of training subprocess execution.
        """
        config = self.config.copy()

        if batch_size is not None:
            config.update_batch_size(batch_size)

        if learning_rate is not None:
            config.update_learning_rate(learning_rate)

        if epochs_iters is not None:
            config._update_epochs(epochs_iters)

        # No need to handle `ips`

        config.update_device(device)

        if resume_path is not None:
            resume_path = abspath(resume_path)
            config._update_checkpoints(resume_path)

        config._update_to_static(dy2st)

        config._update_amp(amp)

        if num_workers is not None:
            config.update_num_workers(num_workers, 'train')

        config._update_use_vdl(use_vdl)

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            save_dir = abspath(config.get_train_save_dir())
        config._update_output_dir(save_dir)

        cli_args = []

        do_eval = kwargs.pop('do_eval', True)

        profile = kwargs.pop('profile', None)
        if profile is not None:
            cli_args.append(CLIArgument('--profiler_options', profile))

        # Benchmarking mode settings
        benchmark = kwargs.pop('benchmark', None)
        if benchmark is not None:
            envs = benchmark.get('env', None)
            seed = benchmark.get('seed', None)
            do_eval = benchmark.get('do_eval', False)
            num_workers = benchmark.get('num_workers', None)
            config.update_log_ranks(device)
            config._update_amp(benchmark.get('amp', None))
            config.update_shuffle(benchmark.get('shuffle', False))
            config.update_cal_metrics(benchmark.get('cal_metrics', True))
            config.update_shared_memory(benchmark.get('shared_memory', True))
            config.update_print_mem_info(benchmark.get('print_mem_info', True))
            if num_workers is not None:
                config.update_num_workers(num_workers)
            if seed is not None:
                config.update_seed(seed)
            if envs is not None:
                for env_name, env_value in envs.items():
                    os.environ[env_name] = str(env_value)

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)

            return self.runner.train(
                config_path, cli_args, device, ips, save_dir, do_eval=do_eval)

    def evaluate(self,
                 weight_path: str,
                 batch_size: int=None,
                 ips: str=None,
                 device: str='gpu',
                 amp: str='OFF',
                 num_workers: int=None,
                 **kwargs) -> CompletedProcess:
        """evaluate self using specified weight

        Args:
            weight_path (str): the path of model weight file to be evaluated.
            batch_size (int, optional): the batch size value in evaluating. Defaults to None.
            ips (str, optional): the ip addresses of nodes when using distribution. Defaults to None.
            device (str, optional): the running device. Defaults to 'gpu'.
            amp (str, optional): the AMP setting. Defaults to 'OFF'.
            num_workers (int, optional): the workers number in evaluating. Defaults to None.

        Returns:
            CompletedProcess: the result of evaluating subprocess execution.
        """
        config = self.config.copy()

        weight_path = abspath(weight_path)
        config._update_checkpoints(weight_path)

        if batch_size is not None:
            config.update_batch_size(batch_size)

        # No need to handle `ips`

        config.update_device(device)

        config._update_amp(amp)

        if num_workers is not None:
            config.update_num_workers(num_workers, 'eval')

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            cp = self.runner.evaluate(config_path, [], device, ips)
            return cp

    def predict(self,
                weight_path: str,
                input_path: str,
                device: str='gpu',
                save_dir: str=None,
                **kwargs) -> CompletedProcess:
        """predict using specified weight

        Args:
            weight_path (str): the path of model weight file used to predict.
            input_path (str): the path of image file to be predicted.
            device (str, optional): the running device. Defaults to 'gpu'.
            save_dir (str, optional): the directory path to save predict output. Defaults to None.

        Returns:
            CompletedProcess: the result of predicting subprocess execution.
        """
        config = self.config.copy()

        weight_path = abspath(weight_path)
        config.update_pretrained_weights(weight_path)

        input_path = abspath(input_path)
        config._update_infer_img(
            input_path, infer_list=kwargs.pop('input_list_path', None))

        config.update_device(device)

        # TODO: Handle `device`
        logging.warning("`device` will not be used.")

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            save_dir = abspath(config.get_predict_save_dir())
        config._update_save_res_path(os.path.join(save_dir, 'res.txt'))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.predict(config_path, [], device)

    def export(self, weight_path: str, save_dir: str,
               **kwargs) -> CompletedProcess:
        """export the dynamic model to static model

        Args:
            weight_path (str): the model weight file path that used to export.
            save_dir (str): the directory path to save export output.

        Returns:
            CompletedProcess: the result of exporting subprocess execution.
        """
        config = self.config.copy()

        weight_path = abspath(weight_path)
        config.update_pretrained_weights(weight_path)

        save_dir = abspath(save_dir)
        config._update_save_inference_dir(save_dir)

        class_path = kwargs.pop('class_path', None)
        if class_path is not None:
            config.update_class_path(class_path)

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.export(config_path, [], None, save_dir)

    def infer(self,
              model_dir: str,
              input_path: str,
              device: str='gpu',
              save_dir: str=None,
              **kwargs) -> CompletedProcess:
        """predict image using infernece model

        Args:
            model_dir (str): the directory path of inference model files that would use to predict.
            input_path (str): the path of image that would be predict.
            device (str, optional): the running device. Defaults to 'gpu'.
            save_dir (str, optional): the directory path to save output. Defaults to None.

        Returns:
            CompletedProcess: the result of infering subprocess execution.
        """
        config = self.config.copy()
        cli_args = []

        model_dir = abspath(model_dir)
        cli_args.append(CLIArgument('--rec_model_dir', model_dir))

        input_path = abspath(input_path)
        cli_args.append(CLIArgument('--image_dir', input_path))

        device_type, _ = self.runner.parse_device(device)
        cli_args.append(CLIArgument('--use_gpu', str(device_type == 'gpu')))

        if save_dir is not None:
            logging.warning("`save_dir` will not be used.")

        dict_path = kwargs.pop('dict_path', None)
        if dict_path is not None:
            dict_path = abspath(dict_path)
        else:
            dict_path = config.get_label_dict_path()
        cli_args.append(CLIArgument('--rec_char_dict_path', dict_path))

        model_type = config._get_model_type()
        cli_args.append(CLIArgument('--rec_algorithm', model_type))
        infer_shape = config._get_infer_shape()
        if infer_shape is not None:
            cli_args.append(CLIArgument('--rec_image_shape', infer_shape))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)
            return self.runner.infer(config_path, cli_args, device)

    def compression(self,
                    weight_path: str,
                    batch_size: int=None,
                    learning_rate: float=None,
                    epochs_iters: int=None,
                    device: str='gpu',
                    use_vdl: bool=True,
                    save_dir: str=None,
                    **kwargs) -> CompletedProcess:
        """compression model

        Args:
            weight_path (str): the path to weight file of model.
            batch_size (int, optional): the batch size value of compression training. Defaults to None.
            learning_rate (float, optional): the learning rate value of compression training. Defaults to None.
            epochs_iters (int, optional): the epochs or iters of compression training. Defaults to None.
            device (str, optional): the device to run compression training. Defaults to 'gpu'.
            use_vdl (bool, optional): whether or not to use VisualDL. Defaults to True.
            save_dir (str, optional): the directory to save output. Defaults to None.

        Returns:
            CompletedProcess: the result of compression subprocess execution.
        """
        config = self.config.copy()
        export_cli_args = []

        weight_path = abspath(weight_path)
        config.update_pretrained_weights(weight_path)

        if batch_size is not None:
            config.update_batch_size(batch_size)

        if learning_rate is not None:
            config.update_learning_rate(learning_rate)

        if epochs_iters is not None:
            config._update_epochs(epochs_iters)

        config.update_device(device)

        config._update_use_vdl(use_vdl)

        if save_dir is not None:
            save_dir = abspath(save_dir)
        else:
            save_dir = abspath(config.get_train_save_dir())
        config._update_output_dir(save_dir)
        export_cli_args.append(
            CLIArgument(
                '-o',
                f"Global.save_inference_dir={os.path.join(save_dir, 'export')}"))

        self._assert_empty_kwargs(kwargs)

        with self._create_new_config_file() as config_path:
            config.dump(config_path)

            return self.runner.compression(config_path, [], export_cli_args,
                                           device, save_dir)