import re
import torch
import numpy as np
from queue import Queue
from typing import Tuple, List, Union, Iterable
from transformers.utils import logging, add_start_docstrings
from transformers.generation.logits_process import LogitsProcessor, LOGITS_PROCESSOR_INPUTS_DOCSTRING, LogitsProcessorList


def make_context(model, tokenizer, 
                 messages: List[dict], 
                 system: str = "You are a helpful assistant.",
                 max_new_tokens: int=0, 
                ):
    
    max_new_tokens = max_new_tokens or model.generation_config.max_new_tokens
    max_input_length = model.config.model_max_length - max_new_tokens

    im_start_id = [tokenizer.im_start_id]
    im_end_id = [tokenizer.im_end_id]
    nl_tokens = tokenizer.encode("\n")

    def _tokenize_str(role, content):
        return tokenizer.encode(role, allowed_special=set()) + nl_tokens + tokenizer.encode(content, allowed_special=set())
    
    def _parse_messages(messages):
        system, query, history = "", "", []
        ## system
        if messages[0]["role"] == "system":
            system = messages[0]["content"]
            messages = messages[1:]
        ## query
        assert messages[-1]["role"] == "user"
        query = messages[-1]["content"]
        messages = messages[:-1]
        ## history
        assert len(messages) % 2 == 0
        for i in range(0, len(messages), 2):
            assert messages[i]["role"] == "user" and messages[i+1]["role"] == "assistant"
            history.append([messages[i]["content"], messages[i+1]["content"]])

        return system, query, history
    
    _system, query, history = _parse_messages(messages)

    ## system
    system_text = _system if _system != "" else system
    system_tokens = []
    if system_text:
        system_tokens = im_start_id +  _tokenize_str("system", system_text) + im_end_id + nl_tokens
    
    ## query
    query_tokens = im_start_id + _tokenize_str("user", query) + im_end_id + nl_tokens
    ## final assistant
    final_tokens = im_start_id + tokenizer.encode("assistant", allowed_special=set()) + nl_tokens
    
    ## max_history_tokens
    max_history_length = max_input_length - len(system_tokens) - len(query_tokens) - len(final_tokens)
    
    ## history
    context_tokens = []
    for turn_query, turn_response in reversed(history):
        ## query tokens
        history_query_tokens = im_start_id + _tokenize_str("user", turn_query) + im_end_id + nl_tokens
        ## answer tokens
        histroy_response_tokens = im_start_id + _tokenize_str("assistant", turn_response)  + im_end_id + nl_tokens
        ## this round tokens
        next_context_tokens = history_query_tokens + histroy_response_tokens
        ## concat
        current_context_size = len(next_context_tokens) + len(context_tokens)
        if current_context_size < max_history_length:
            context_tokens = next_context_tokens + context_tokens
        else:
            break
    input_tokens = system_tokens + context_tokens + query_tokens + final_tokens

    return torch.LongTensor([input_tokens]).to(model.device)


class TextIterStreamer:
    def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
        self.tokenizer = tokenizer
        self.skip_prompt = skip_prompt
        self.skip_special_tokens = skip_special_tokens
        self.tokens = []
        self.text_queue = Queue()
        self.next_tokens_are_prompt = True

    def put(self, value):
        if self.skip_prompt and self.next_tokens_are_prompt:
            self.next_tokens_are_prompt = False
        else:
            if len(value.shape) > 1:
                value = value[0]
            self.tokens.extend(value.tolist())
            tokens_str = self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens, errors='ignore')
            self.text_queue.put(tokens_str)

    def end(self):
        self.text_queue.put(None)

    def __iter__(self):
        return self

    def __next__(self):
        value = self.text_queue.get()
        if value is None:
            raise StopIteration()
        else:
            return value


class OutputRepetitionPenaltyLogitsProcessor(LogitsProcessor):
    r"""
    [`OutputLogitsProcessor`] that prevents the repetition of previous tokens through a penalty. This penalty is applied at
    most once per token. Note that, for decoder-only models like most LLMs, the considered tokens include the prompt.

    In the original [paper](https://arxiv.org/pdf/1909.05858.pdf), the authors suggest the use of a penalty of around
    1.2 to achieve a good balance between truthful generation and lack of repetition. To penalize and reduce
    repetition, use `penalty` values above 1.0, where a higher value penalizes more strongly. To reward and encourage
    repetition, use `penalty` values between 0.0 and 1.0, where a lower value rewards more strongly.

    Args:
        penalty (`float`):
            The parameter for repetition penalty. 1.0 means no penalty. Above 1.0 penalizes previously generated
            tokens. Between 0.0 and 1.0 rewards previously generated tokens.
    """

    def __init__(self, input_length: int, 
                    presence_penalties: float = 1.0,
                    frequency_penalties: float = 0,
                    repetition_penalties: float = 0):
        if not (repetition_penalties > 0):
            raise ValueError(f"`repetition_penalties` has to be a strictly positive float, but is {repetition_penalties}")
        if not ( (frequency_penalties >= -2) and (frequency_penalties <= 2) ):
            raise ValueError(f"`frequency_penalties` has to be [-2, 2], but is {frequency_penalties}")
        if not ( (presence_penalties >= -2) and (presence_penalties <= 2) ):
            raise ValueError(f"`presence_penalties` has to be [-2, 2], but is {presence_penalties}")

        self.repetition_penalties = repetition_penalties
        self.frequency_penalties = frequency_penalties
        self.presence_penalties = presence_penalties
        self.input_length = input_length

    def _get_bin_counts_and_mask(
        self,
        tokens: torch.Tensor,
        vocab_size: int,
        num_seqs: int,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # Compute the bin counts for the tokens.
        # vocab_size + 1 for padding.
        bin_counts = torch.zeros((num_seqs, vocab_size + 1),
                                dtype=torch.long,
                                device=tokens.device)
        bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))
        bin_counts = bin_counts[:, :vocab_size]
        mask = bin_counts > 0

        return bin_counts, mask

    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
    def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
        prompt_tokens_tensor = input_ids[:, :self.input_length+1]
        output_tokens_tensor = input_ids[:, self.input_length+1:]

        num_seqs, vocab_size = logits.shape
        _, prompt_mask = self._get_bin_counts_and_mask(
            prompt_tokens_tensor, vocab_size, num_seqs)
        output_bin_counts, output_mask = self._get_bin_counts_and_mask(
            output_tokens_tensor, vocab_size, num_seqs)

        repetition_penalties = torch.Tensor([self.repetition_penalties]).to(logits.device)
        frequency_penalties = torch.Tensor([self.frequency_penalties]).to(logits.device)
        presence_penalties = torch.Tensor([self.presence_penalties]).to(logits.device)

        repetition_penalties = repetition_penalties[:, None].repeat(1, vocab_size)
        repetition_penalties[~(prompt_mask | output_mask)] = 1.0
        logits = torch.where(logits > 0, logits / repetition_penalties,
                            logits * repetition_penalties)

        # We follow the definition in OpenAI API.
        # Refer to https://platform.openai.com/docs/api-reference/parameter-details
        logits -= frequency_penalties.unsqueeze_(dim=1) * output_bin_counts
        logits -= presence_penalties.unsqueeze_(dim=1) * output_mask

        return logits