import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,)
    # mpu)


class Reward():
    def __init__(self, args, tokenizer: AutoTokenizer, model: AutoModelForCausalLM):
        self.args = args
        self.tokenizer = tokenizer
        self.model = model
        self.pad_token_id = tokenizer.pad_token_id
        self.eos_token_id = tokenizer.eos_token_id

    def get_input_batch(self, input_ids, gen_ids, output_pos=True):
        full_ids = torch.cat([input_ids, gen_ids], dim=-1)
        attention_mask = (full_ids != self.pad_token_id)

        model_inputs = {
            "input_ids": full_ids,
            "attention_mask": attention_mask,
            "use_cache": False
        }
        
        if (self.args.model_type in ["gpt2"]) and output_pos:
            position_ids = torch.cumsum(attention_mask, dim=-1) - 1
            position_ids.masked_fill_(~attention_mask, 0)
            model_inputs["position_ids"] = position_ids
        
        return model_inputs

    def reward_fn(self, input_ids, gen_ids, inf_mask=None, output_pos=True):
        # not include eos token
        
        self.model.eval()
        # input_ids = input_ids.repeat(1, 1)
        
        model_inputs = self.get_input_batch(input_ids, gen_ids, output_pos=output_pos)

        with torch.no_grad():
            outputs = self.model(**model_inputs)
        
        logits = outputs.logits  # (B, L, V)
        if self.args.model_parallel:
            logits = logits - mpu.parallel_mean(logits.float(), dim=-1).unsqueeze(-1)
        else:
            logits = logits - torch.mean(logits, dim=-1, keepdim=True)
        
        mask = model_inputs["attention_mask"]
        logits = logits * mask.unsqueeze(-1)  # set logits output by padding to 0
        
        logits = logits[:, input_ids.size(-1)-1:, :]
        mask = mask[:, input_ids.size(-1)-1:]

        if self.args.model_parallel:
            selection_value = mpu.parallel_gather(logits[:, :-1, :], -1, model_inputs["input_ids"][:, input_ids.size(-1):, None]).squeeze(-1)
        else:
            selection_value = torch.gather(logits[:, :-1, :], -1, model_inputs["input_ids"][:, input_ids.size(-1):, None]).squeeze(-1)

        current_logits = logits[:, :-1, :]
        if self.args.model_parallel:
            next_state_value = mpu.parallel_logsumexp(current_logits.float(), dim=-1)
        else:
            next_state_value = torch.logsumexp(current_logits, dim=-1)
        next_state_value = next_state_value * mask[:, :-1]
        raw_next_state_value = next_state_value

        scores = selection_value - next_state_value
        
        assert all((~torch.isinf(scores.view(-1))) & (~torch.isnan(scores.view(-1))))
        
        assert scores.size() == gen_ids.size()
        
        return {
            "rewards": scores,
            "inf_mask": inf_mask
        }
