# Copyright (c) 2022, Tri Dao.
# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/forward_step.py#L31
from typing import Optional

from dataclasses import dataclass, field
import torch
from torch import Tensor

from einops import rearrange

from transformers.generation import GreedySearchDecoderOnlyOutput, SampleDecoderOnlyOutput


@dataclass
class InferenceParams:
    """Inference parameters that are passed to the main model in order
    to efficienly calculate and store the context during inference."""
    max_sequence_len: int
    max_batch_size: int
    sequence_len_offset: int = 0
    batch_size_offset: int = 0
    key_value_memory_dict: dict = field(default_factory=dict)
    fused_ft_kernel: bool = False
    lengths_per_sample: Optional[Tensor] = None


# https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/sampling.py
# https://github.com/huggingface/transformers/blob/a44985b41cfa2de48a5e1de7f1f93b7483da25d1/src/transformers/generation/logits_process.py#L170
def modify_logits_for_top_p_filtering(logits, top_p):
    """Set the logits for none top-p values to -inf."""
    if top_p <= 0.0:
        return
    # First sort and calculate cumulative sum of probabilities.
    sorted_logits, sorted_indices = torch.sort(logits, descending=False)
    cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
     # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
    sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
    # scatter sorted tensors to original indexing
    indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
    logits = logits.masked_fill(indices_to_remove, float('-inf'))


def sample(logits, top_k=1, top_p=0.0, temperature=1.0):
    """Sample from top-k logits.
    Arguments:
        logits: Tensor of shape (batch_size, vocab_size)
    """
    if top_k == 1:  # Short-circuit for greedy decoding
        return logits.argmax(dim=-1)
    else:
        if top_p > 0.0:
            assert top_p <= 1.0, 'top-p should be in (0, 1].'
        if top_k > 0:
            top_k = min(top_k, logits.size(-1))  # Safety check
            logits_top, indices = torch.topk(logits, top_k, dim=-1)
            logits_top /= temperature
            modify_logits_for_top_p_filtering(logits_top, top_p)
            return indices[
                torch.arange(indices.shape[0], device=indices.device),
                torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(dim=-1)
            ]
        else:
            logits_top = logits / temperature
            modify_logits_for_top_p_filtering(logits_top, top_p)
            return torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(dim=-1)


def decode(input_ids, model, max_length, top_k=1, top_p=0.0, temperature=1.0, fused_ft_kernel=True):
    """Decoding, either greedy or with top-k or top-p sampling.
    If top-k = 0, don't limit the number of candidates (pure sampling).
    Top-k and top-p can be used together. If top_k > 0 and top_p > 0, then top-k is applied first,
    then top-p.
    We assume that all sequences in the same batch have the same length.

    Arguments:
        input_ids: (batch, seq_len)
        max_length: int
    Returns: GreedySearchDecoderOnlyOutput or SampleDecoderOnlyOutput, with the following fields:
        sequences: (batch, max_length)
        scores: tuples of (batch, vocab_size)
    """
    batch_size, seqlen_og = input_ids.shape
    inference_params = InferenceParams(max_sequence_len=max_length, max_batch_size=batch_size,
                                       fused_ft_kernel=fused_ft_kernel)
    scores = []
    with torch.inference_mode():
        logits = model(input_ids, inference_params=inference_params).logits[:, -1]
        scores.append(logits)
        next_token = sample(logits, top_k=top_k, top_p=top_p, temperature=temperature)
        sequences = [next_token]
        inference_params.sequence_len_offset = seqlen_og
        while True:
            position_ids = torch.full((batch_size, 1), inference_params.sequence_len_offset,
                                      dtype=torch.long, device=input_ids.device)
            logits = model(rearrange(next_token, 'b -> b 1'), position_ids=position_ids,
                           inference_params=inference_params).logits[:, -1]
            scores.append(logits)
            next_token = sample(logits, top_k=top_k, temperature=temperature)
            sequences.append(next_token)
            inference_params.sequence_len_offset += 1
            if inference_params.sequence_len_offset >= max_length - 1:
                break
    output_cls = GreedySearchDecoderOnlyOutput if top_k == 1 else SampleDecoderOnlyOutput
    return output_cls(
        sequences=torch.cat([input_ids, torch.stack(sequences, dim=1)], dim=1),
        scores=tuple(scores)
    )


class GenerationMixin:

    def generate(self, input_ids, max_length, top_k=1, top_p=0.0, temperature=1.0,
                 return_dict_in_generate=False, output_scores=False, **kwargs):
        output = decode(input_ids, self, max_length, top_k=top_k, top_p=top_p,
                        temperature=temperature, **kwargs)
        if not output_scores:
            output.scores = None
        return output if return_dict_in_generate else output.sequences