from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union

import torch
from torch import nn
from transformers import PreTrainedModel, trainer
from trl import DPOTrainer as HFDPOTrainer

from swift.llm.utils.template import Context, Template
from swift.llm.utils.utils import sort_by_max_length
from swift.utils import get_logger
from .callback import DefaultFlowCallbackNew, PrinterCallbackNew, ProgressCallbackNew
from .mixin import PushToMsHubMixin, SwiftMixin

logger = get_logger()


class DPOTrainer(PushToMsHubMixin, SwiftMixin, HFDPOTrainer):

    def __init__(self, *args, template: Template, sft_beta=0., test_oom_error=False, **kwargs):
        self.template = template
        self.sft_beta = sft_beta
        super().__init__(*args, **kwargs)
        train_ds_info = self.stat_dataset(self.train_dataset)
        val_ds_info = self.stat_dataset(self.eval_dataset)
        self.dataset_info = {'train_dataset': train_ds_info, 'val_dataset': val_ds_info}
        if test_oom_error:
            self.train_dataset = sort_by_max_length(self.train_dataset, 20000)
        # performance
        self.perf: Dict[str, Any] = {
            'gen_time': 0.,
            'gen_len': 0,
            'memory': {},
            'model': self.model.get_trainable_parameters() if hasattr(self.model, 'get_trainable_parameters') else None,
        }

    def train(self, *args, **kwargs) -> torch.Tensor:
        res = super().train(*args, **kwargs)
        for i in range(torch.cuda.device_count()):
            self.perf['memory'][f'cuda:{i}'] = f'{torch.cuda.max_memory_reserved(i)/1024/1024/1024:.2f}GiB'
        return res

    def concat_template(self, feature):
        query: Optional[str] = feature.get('query', None)
        system: Optional[str] = feature.get('system', None)
        history: List = feature.get('history', [])
        if system is None:
            if self.template.use_default_system:
                system = self.template.default_system
        else:
            assert self.template.prefix_has_system is not None, 'not support `system`'
        res_context_list: List[Context] = []
        compute_loss_idx: List[float] = []
        if system is None:
            assert self.template.prefix != self.template.prefix_has_system, f'template.prefix: {self.template.prefix}'
            prefix = self.template.prefix
        else:
            prefix = self.template.prefix_has_system
        self.template._concat_context_list(prefix, res_context_list, compute_loss_idx, system=system)
        for i, (q, r) in enumerate(history):
            self.template._concat_context_list(
                [
                    *self.template.prompt,
                    '{{RESPONSE}}',
                    *self.template.chat_sep  # noqa
                ],
                res_context_list,
                compute_loss_idx,
                query=q,
                response=r,
                round0=i)  # noqa
        self.template._concat_context_list(
            self.template.prompt, res_context_list, compute_loss_idx, query=query, round0=len(history))
        res_context_list, compute_loss_idx = self.template._simplify_context_list(res_context_list, compute_loss_idx)

        return res_context_list, feature['response'], feature['rejected_response'], compute_loss_idx

    def build_tokenized_answer(self, prompt, answer, prompt_loss_scale):
        input_ids, labels, loss_scale, kwargs = self.template._encode_context_list(prompt, prompt_loss_scale)
        tgt_input_ids = self.template._encode_context_list([answer], [1.0])[0]
        tgt_input_ids += self.template._encode_context_list(self.template.suffix, [1.0])[0]
        return dict(
            prompt_input_ids=input_ids,
            prompt_attention_mask=[1] * len(input_ids),
            input_ids=tgt_input_ids,
            attention_mask=[1] * len(tgt_input_ids),
        )

    def tokenize_row(self, feature, model: Union[PreTrainedModel, nn.Module] = None) -> Dict:
        batch = {}
        if not self.is_encoder_decoder:
            prompt, chosen, rejected, loss_scale = self.concat_template(feature)

            prompt_tokens, _, _, _ = self.template._encode_context_list(prompt, loss_scale)
            prompt_tokens = {
                'input_ids': prompt_tokens,
                'attention_mask': [1] * len(prompt_tokens),
            }
            prompt_tokens = {f'prompt_{k}': v for k, v in prompt_tokens.items()}

            if not isinstance(chosen, str):
                raise ValueError(f'chosen should be an str but got {type(chosen)}')
            chosen_tokens = self.build_tokenized_answer(prompt, chosen, loss_scale)

            if not isinstance(rejected, str):
                raise ValueError(f'rejected should be an str but got {type(rejected)}')
            rejected_tokens = self.build_tokenized_answer(prompt, rejected, loss_scale)

            longer_response_length = max(len(chosen_tokens['input_ids']), len(rejected_tokens['input_ids']))

            # if combined sequence is too long, truncate the prompt
            for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]:
                if len(answer_tokens['prompt_input_ids']) + longer_response_length > self.max_length:
                    if self.truncation_mode == 'keep_start':
                        for k in ['prompt_input_ids', 'prompt_attention_mask']:
                            answer_tokens[k] = answer_tokens[k][:self.max_prompt_length]
                    elif self.truncation_mode == 'keep_end':
                        for k in ['prompt_input_ids', 'prompt_attention_mask']:
                            answer_tokens[k] = answer_tokens[k][-self.max_prompt_length:]
                    else:
                        raise ValueError(f'Unknown truncation mode: {self.truncation_mode}')

            # if that's still too long, truncate the response
            for answer_tokens in [chosen_tokens, rejected_tokens]:
                if len(answer_tokens['prompt_input_ids']) + longer_response_length > self.max_length:
                    for k in ['input_ids', 'attention_mask']:
                        answer_tokens[k] = answer_tokens[k][:self.max_length - self.max_prompt_length]

            # Create labels
            chosen_sequence_tokens = {
                k: chosen_tokens[f'prompt_{k}'] + chosen_tokens[k]
                for k in ['input_ids', 'attention_mask']
            }
            rejected_sequence_tokens = {
                k: rejected_tokens[f'prompt_{k}'] + rejected_tokens[k]
                for k in ['input_ids', 'attention_mask']
            }
            chosen_sequence_tokens['labels'] = chosen_sequence_tokens['input_ids'][:]
            _paddings = [self.label_pad_token_id] * len(chosen_tokens['prompt_input_ids'])
            chosen_sequence_tokens['labels'][:len(chosen_tokens['prompt_input_ids'])] = _paddings
            rejected_sequence_tokens['labels'] = rejected_sequence_tokens['input_ids'][:]
            _paddings = [self.label_pad_token_id] * len(rejected_tokens['prompt_input_ids'])
            rejected_sequence_tokens['labels'][:len(rejected_tokens['prompt_input_ids'])] = _paddings

            for k, toks in {
                    'chosen_': chosen_sequence_tokens,
                    'rejected_': rejected_sequence_tokens,
                    '': prompt_tokens,
            }.items():
                for type_key, tokens in toks.items():
                    if type_key == 'token_type_ids':
                        continue
                    batch[f'{k}{type_key}'] = tokens

        else:
            # encoder-decoder
            batch = super().tokenize_row(feature, model)

        return batch

    @staticmethod
    def stat_dataset(llm_dataset) -> Any:
        _token_len = []
        from datasets import Dataset as HfDataset
        from swift.utils.np_utils import stat_array
        if isinstance(llm_dataset, HfDataset):
            chosen = llm_dataset['chosen_input_ids']
            rejected = llm_dataset['rejected_input_ids']
            for cc, rr in zip(chosen, rejected):
                _token_len.append(max(len(cc), len(rr)))
        else:
            for d in llm_dataset:
                _token_len.append(max(len(d['chosen_input_ids']), len(d['rejected_input_ids'])))
        _, stat_str = stat_array(_token_len)
        logger.info(f'Dataset Token Length: {stat_str}')
        return stat_str

    def get_batch_loss_metrics(
        self,
        model,
        batch: Dict[str, Union[List, torch.LongTensor]],
        train_eval: Literal['train', 'eval'] = 'train',
    ):
        """Compute the DPO loss and other metrics for the given batch of inputs for train or test."""
        metrics = {}

        (
            policy_chosen_logps,
            policy_rejected_logps,
            policy_chosen_logits,
            policy_rejected_logits,
            concatenated_batch,
        ) = self.concatenated_forward(model, batch)

        # if reference_chosen_logps and reference_rejected_logps in batch use them, otherwise use the reference model
        if 'reference_chosen_logps' in batch and 'reference_rejected_logps' in batch:
            reference_chosen_logps = batch['reference_chosen_logps']
            reference_rejected_logps = batch['reference_rejected_logps']
        else:
            with torch.no_grad():
                if self.ref_model is None:
                    with self.null_ref_context():
                        (
                            reference_chosen_logps,
                            reference_rejected_logps,
                            _,
                            _,
                            _,
                        ) = self.concatenated_forward(self.model, batch)
                else:
                    (
                        reference_chosen_logps,
                        reference_rejected_logps,
                        _,
                        _,
                        _,
                    ) = self.concatenated_forward(self.ref_model, batch)

        losses, chosen_rewards, rejected_rewards = self.dpo_loss(
            policy_chosen_logps,
            policy_rejected_logps,
            reference_chosen_logps,
            reference_rejected_logps,
        )

        if self.sft_beta > 0.:
            chosen_labels = concatenated_batch['concatenated_labels'][:batch['chosen_labels'].shape[0]]
            sft_loss = -self.get_batch_logps(policy_chosen_logits, chosen_labels, average_log_prob=True)
            if losses.shape[0] == 2 * sft_loss.shape[0]:
                sft_loss = sft_loss.repeat(2, *sft_loss.shape[1:])
            losses = (1 - self.sft_beta) * losses + self.sft_beta * sft_loss

        reward_accuracies = (chosen_rewards > rejected_rewards).float()

        prefix = 'eval_' if train_eval == 'eval' else ''
        metrics[f'{prefix}rewards/chosen'] = chosen_rewards.mean().cpu()
        metrics[f'{prefix}rewards/rejected'] = rejected_rewards.mean().cpu()
        metrics[f'{prefix}rewards/accuracies'] = reward_accuracies.mean().cpu()
        metrics[f'{prefix}rewards/margins'] = (chosen_rewards - rejected_rewards).mean().cpu()
        metrics[f'{prefix}logps/rejected'] = policy_rejected_logps.detach().mean().cpu()
        metrics[f'{prefix}logps/chosen'] = policy_chosen_logps.detach().mean().cpu()
        metrics[f'{prefix}logps/ref_rejected'] = reference_rejected_logps.detach(  # noqa
        ).mean().cpu()  # noqa
        metrics[f'{prefix}logps/ref_chosen'] = reference_chosen_logps.detach().mean().cpu()
        metrics[f'{prefix}logits/rejected'] = policy_rejected_logits.detach().mean().cpu()
        metrics[f'{prefix}logits/chosen'] = policy_chosen_logits.detach().mean().cpu()

        return losses.mean(), metrics

    def concatenated_forward(
        self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]]
    ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, Dict[str, torch.LongTensor]]:
        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.

        We do this to avoid doing two forward passes, because it's faster for FSDP.
        """
        concatenated_batch = self.concatenated_inputs(
            batch,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
            padding_value=self.padding_value,
            device=self.accelerator.device,
        )
        len_chosen = batch['chosen_labels'].shape[0]

        model_kwargs = ({
            'labels': concatenated_batch['concatenated_labels'],
            'decoder_input_ids': concatenated_batch.pop('concatenated_decoder_input_ids', None),
        } if self.is_encoder_decoder else {})
        all_logits = model(
            concatenated_batch['concatenated_input_ids'],
            attention_mask=concatenated_batch['concatenated_attention_mask'],
            **model_kwargs,
        ).logits

        all_logps = self.get_batch_logps(
            all_logits,
            concatenated_batch['concatenated_labels'],
            average_log_prob=False,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
        )

        chosen_logps = all_logps[:len_chosen]
        rejected_logps = all_logps[len_chosen:]

        chosen_logits = all_logits[:len_chosen]
        rejected_logits = all_logits[len_chosen:]

        return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, concatenated_batch)


# monkey patching
trainer.DEFAULT_PROGRESS_CALLBACK = ProgressCallbackNew
trainer.DEFAULT_CALLBACKS = [DefaultFlowCallbackNew]
trainer.PrinterCallback = PrinterCallbackNew
