# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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
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"""
Contain small torch utilities
"""

from typing import Dict, Union, List, Optional

import torch
import torch.distributed
import torch.nn.functional as F
from tensordict import TensorDict
from torch import nn

try:
    from flash_attn.ops.triton.cross_entropy import cross_entropy_loss
    FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE = True
except ImportError:
    FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE = False


def gather_from_labels(data, label):
    """Gather the label from data. The value in label should be [0, vocab_size)

    Args:
        data: (..., vocab_size)
        label (torch.IntTensor) : (...,)

    Returns:

    """

    output = torch.gather(data, -1, label.unsqueeze(-1)).squeeze(-1)
    return output


def logprobs_from_logits(logits, labels):
    """
    See: https://github.com/pytorch/pytorch/issues/563#issuecomment-330103591
    """
    if FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE:
        batch_dim = logits.shape[:-1]
        last_dim = logits.shape[-1]
        logits = logits.reshape(-1, last_dim)
        labels = labels.reshape(-1)
        output = logprobs_from_logits_flash_attn(logits, labels)
        output = output.view(*batch_dim)
    else:
        output = logprobs_from_logits_v2(logits, labels)
    return output


def logprobs_from_logits_flash_attn(logits, labels):
    output = cross_entropy_loss(logits, labels)
    assert isinstance(
        output, tuple), "please make sure flash-attn>=2.4.3 where cross_entropy_loss returns Tuple[losses, z_losses]."
    return -output[0]


def logprobs_from_logits_naive(logits, labels):
    logp = F.log_softmax(logits, dim=-1)
    logpy = gather_from_labels(logp, labels)
    return logpy


def logprobs_from_logits_v2(logits: torch.FloatTensor, labels):
    """
    A memory efficient implementation of logprobs_from_logits
    """
    if logits.dtype in [torch.float32, torch.float64]:
        logits_labels = torch.gather(logits, dim=-1, index=labels.unsqueeze(-1)).squeeze(-1)
        # loop to reduce peak mem consumption
        logsumexp_values = torch.stack([torch.logsumexp(l, dim=-1) for l in logits])
        logprobs_labels = logits_labels - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
    else:
        # logsumexp approach is unstable with bfloat16, fall back to slightly less efficent approach
        logprobs_labels = []
        for row_logits, row_labels in zip(logits, labels):  # loop to reduce peak mem consumption
            row_logprobs = F.log_softmax(row_logits, dim=-1)
            row_logprobs_labels = row_logprobs.gather(dim=-1, index=row_labels.unsqueeze(-1)).squeeze(-1)
            logprobs_labels.append(row_logprobs_labels)
        logprobs_labels = torch.stack(logprobs_labels)
    return logprobs_labels


def clip_by_value(x, tensor_min, tensor_max):
    """
    Tensor extenstion to torch.clamp
    https://github.com/pytorch/pytorch/issues/2793#issuecomment-428784713
    """
    clipped = torch.max(torch.min(x, tensor_max), tensor_min)
    return clipped


def entropy_from_logits(logits: torch.Tensor):
    """Calculate entropy from logits."""
    pd = torch.nn.functional.softmax(logits, dim=-1)
    entropy = torch.logsumexp(logits, dim=-1) - torch.sum(pd * logits, dim=-1)
    return entropy


def masked_sum(values, mask, axis=None):
    """Compute mean of tensor with a masked values."""
    return (values * mask).sum(axis=axis)


def masked_mean(values, mask, axis=None):
    """Compute mean of tensor with a masked values."""
    return (values * mask).sum(axis=axis) / (mask.sum(axis=axis) + 1e-8)


def masked_var(values, mask, unbiased=True):
    """Compute variance of tensor with masked values."""
    mean = masked_mean(values, mask)
    centered_values = values - mean
    variance = masked_mean(centered_values**2, mask)
    if unbiased:
        mask_sum = mask.sum()
        if mask_sum == 0:
            raise ValueError("At least one element in the mask has to be 1.")
        # note that if mask_sum == 1, then there is a division by zero issue
        # to avoid it you just need to use a larger minibatch_size
        if mask_sum == 1:
            raise ValueError("The sum of the mask is one, which can cause a division by zero.")
        bessel_correction = mask_sum / (mask_sum - 1)
        variance = variance * bessel_correction
    return variance


def masked_whiten(values, mask, shift_mean=True):
    """Whiten values with masked values."""
    mean, var = masked_mean(values, mask), masked_var(values, mask)
    whitened = (values - mean) * torch.rsqrt(var + 1e-8)
    if not shift_mean:
        whitened += mean
    return whitened


def get_response_mask(response_id: torch.Tensor, eos_token: Union[int, List[int]] = 2, dtype=torch.int64):
    '''
    end of sentence token can be int or list: 1 or [1, 2]
    e.g. 
    response_id = torch.tensor([[20, 10, 34, 1, 0, 0, 0],
                                [78, 0, 76, 2, 1, 0, 0],
                                [23, 98, 1, 0, 0, 0, 0],
                                [33, 3, 98, 45, 1, 0, 0]])
    #eos_token=1
    response_mask:  tensor([[1, 1, 1, 1, 0, 0, 0],
                            [1, 1, 1, 1, 1, 0, 0],
                            [1, 1, 1, 0, 0, 0, 0],
                            [1, 1, 1, 1, 1, 0, 0]])
    #eos_token=[1,2]
    response_mask:  tensor([[1, 1, 1, 1, 0, 0, 0],
                            [1, 1, 1, 1, 0, 0, 0],
                            [1, 1, 1, 0, 0, 0, 0],
                            [1, 1, 1, 1, 1, 0, 0]])
    '''
    eos_mask = torch.isin(response_id, torch.tensor(eos_token, device=response_id.device)).int()
    return (eos_mask.cumsum(dim=1) - eos_mask).eq(0).to(dtype)


def compute_grad_norm(model: nn.Module):
    total_grad_square = 0
    total_params = 0
    for param in model.parameters():
        if param.grad is not None:
            total_grad_square += torch.sum(torch.square(param.grad.detach())).item()
    return total_grad_square


def broadcast_dict_tensor(tensors: Union[Dict[str, torch.Tensor], TensorDict], src, group):
    """
    TODO: optimize this. Technically, we only need one broadcast
    """

    for key in tensors.sorted_keys:
        torch.distributed.broadcast(tensors[key], src=src, group=group, async_op=False)


def allgather_dict_tensors(tensors: Union[Dict[str, torch.Tensor], TensorDict], size, group, dim=0):
    """
    TODO: optimize this.
    - We can use async ops
    - We can use only one allgather
    Args:
        tensors:
        size:
        group:

    Returns:

    """
    if isinstance(tensors, TensorDict):
        is_tensor_dict = True
        tensors_as_dict = tensors.to_dict()
    else:
        tensors_as_dict = tensors
        is_tensor_dict = False

    output = {}
    sorted_keys = sorted(tensors_as_dict.keys())
    for key in sorted_keys:
        val = tensors_as_dict[key]
        output[key] = [torch.empty_like(val) for _ in range(size)]
        torch.distributed.all_gather(output[key], val, group=group, async_op=False)
        output[key] = torch.cat(output[key], dim=dim)

    if is_tensor_dict:
        output = TensorDict(source=output, batch_size=tensors.batch_size[0] * size)

    return output


def split_dict_tensor_into_batches(tensors: TensorDict, batch_size) -> List[TensorDict]:
    assert tensors.batch_size[0] % batch_size == 0, \
        f'input data batch size: {tensors.batch_size[0]}, split batch size: {batch_size}'
    return tensors.split(batch_size)


def pad_2d_list_to_length(response, pad_token_id, max_length=None):
    """
    pad a 2D list (e.g. responses, logprobs) to a 2D tensor.
    """
    response_length = max(len(sub_list) for sub_list in response)
    if max_length is not None and max_length > response_length:
        target_length = max_length
    else:
        target_length = response_length
    padded_response = [tuple(sub_list) + (pad_token_id,) * (target_length - len(sub_list)) for sub_list in response]
    tensor = torch.tensor(padded_response)
    return tensor


def pad_sequence_to_length(tensors, max_seq_len, pad_token_id, left_pad=False):
    """
    pad a 2D tensors (e.g. responses, logprobs) in the last dim to max_seq_length.
    input shape: [bs, seq_length]
    output shape: [bs, max_seq_length]
    (0, max_seq_len - tensors.shape[-1]) means right pad to max_seq_length and no left pad
    """
    if tensors.shape[-1] >= max_seq_len:
        return tensors
    pad_tuple = (max_seq_len - tensors.shape[-1], 0) if left_pad else (0, max_seq_len - tensors.shape[-1])
    return F.pad(tensors, pad_tuple, 'constant', pad_token_id)


def postprocess_data(input_ids: torch.Tensor,
                     attention_mask: torch.Tensor,
                     max_length: int,
                     pad_token_id: int,
                     left_pad=True,
                     truncation='error'):
    """
    input_data is the output from tokenizer.
    """
    assert truncation in ['left', 'right', 'error']
    assert input_ids.ndim == 2

    sequence_length = input_ids.shape[-1]
    if sequence_length < max_length:
        input_ids = pad_sequence_to_length(input_ids,
                                           max_seq_len=max_length,
                                           pad_token_id=pad_token_id,
                                           left_pad=left_pad)
        attention_mask = pad_sequence_to_length(attention_mask,
                                                max_seq_len=max_length,
                                                pad_token_id=0,
                                                left_pad=left_pad)
    elif sequence_length > max_length:
        if truncation == 'left':
            # actually, left truncation may not be reasonable
            input_ids = input_ids[:, -max_length:]
            attention_mask = attention_mask[:, -max_length:]
        elif truncation == 'right':
            input_ids = input_ids[:, :max_length]
            attention_mask = attention_mask[:, :max_length]
        elif truncation == 'error':
            raise NotImplementedError(f'{sequence_length=} is larger than {max_length=}')
        else:
            raise NotImplementedError(f'Unknown truncation method {truncation}')

    return input_ids, attention_mask


def remove_pad_token(input_ids: torch.Tensor, attention_mask: torch.Tensor):
    """ Remove the pad token.

    Args:
        input_ids shape: [bs, seq_length]
        attention_mask shape: [bs, seq_length]
    Returns:
        no_padding_batch(List[List[int]]): contains the rmpad token ids per query.
    """
    no_padding_batch = []
    for ids, mask in zip(input_ids, attention_mask):
        no_padding_batch.append((ids[len(ids) - mask.sum():]).cpu().numpy().tolist())
    return no_padding_batch


def log_probs_from_logits_response(input_ids, logits, response_length):
    """Compute the response log_probs from full logits. Note that logits = model(input_ids)

    Args:
        input_ids: [batch_size, seqlen]
        logits: [batch_size, seqlen, vocab_size]

    Returns:
        response_log_prob:
    """
    response_logits = logits[:, -response_length - 1:-1]
    response = input_ids[:, -response_length:]
    response_log_prob = logprobs_from_logits(logits=response_logits, labels=response)
    return response_log_prob


def log_probs_from_logits_response_rmpad(input_ids, attention_mask, logits_rmpad, response_length):
    """Compute the log_probs from logits with rmpad logits and pad input. Note that
    logits_rmpad = model(input_ids_rmpad). For each sentences, there is a shift between
    logits and input_ids.
    The reason for this function to is to compute logprobs_from_logits in rmpad mode because it is memory-intensive
    for large vocab_size

    Args:
        input_ids: [batch_size, seqlen]
        attention_mask: [batch_size, seqlen]
        logits_rmpad: [total_nnz, vocab_size]
        response_length: int
    """
    from flash_attn.bert_padding import pad_input, unpad_input

    batch_size, seqlen = input_ids.shape
    input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask=attention_mask)
    input_ids_rmpad = input_ids_rmpad.squeeze(-1)
    input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=0)
    full_log_probs_rmpad = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled)  # (total_nnz,)
    full_output = pad_input(hidden_states=full_log_probs_rmpad.unsqueeze(-1),
                            indices=indices,
                            batch=batch_size,
                            seqlen=seqlen)
    output = full_output.squeeze(-1)[:, -response_length - 1:-1]  # [batch_size, response_length]
    return output


def log_probs_from_logits_all_rmpad(input_ids_rmpad, logits_rmpad, indices, batch_size, seqlen, response_length):
    """Compute the log_probs from logits with rmpad input_ids and logits. Note that
    logits_rmpad = model(input_ids_rmpad). For each sentences, there is a shift between
    logits and input_ids.
    The reason for this function to is to compute logprobs_from_logits in rmpad mode because it is memory-intensive
    for large vocab_size

    Args:
        input_ids_rmpad: [1, total_nnz]
        logits_rmpad: [total_nnz, vocab_size]
        indices: [total_nnz]
        batch_size: int
        seqlen: int
        response_length: int
    """
    from flash_attn.bert_padding import pad_input
    input_ids_rmpad = input_ids_rmpad.transpose(0, 1)  # transpose back to [total_nnz, 1]
    input_ids_rmpad = input_ids_rmpad.squeeze(-1)
    input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=0)
    full_log_probs_rmpad = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled)  # (total_nnz,)
    full_output = pad_input(hidden_states=full_log_probs_rmpad.unsqueeze(-1),
                            indices=indices,
                            batch=batch_size,
                            seqlen=seqlen)
    output = full_output.squeeze(-1)[:, -response_length - 1:-1]  # [batch_size, response_length]
    return output


from transformers.generation.logits_process import (TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper)


def post_process_logits(input_ids, logits, temperature, top_k, top_p):
    if temperature != 1.:
        logits = logits.div_(temperature)  # inplace operation to avoid OOM
    # TODO: add them back
    # if top_k is not None and top_k > 0:
    #     logits = TopKLogitsWarper(top_k=top_k)(input_ids, logits)
    # if top_p is not None and top_p < 1.0 and top_p > 0.0:
    #     logits = TopPLogitsWarper(top_p=top_p)(input_ids, logits)
    return logits


"""
Optimizer related
"""

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
import math


def get_cosine_schedule_with_warmup(
    optimizer: Optimizer,
    num_warmup_steps: int,
    num_training_steps: int,
    min_lr_ratio: float = 0.0,
    num_cycles: float = 0.5,
    last_epoch: int = -1,
):
    """
    Create a schedule with a learning rate that decreases following the values of the cosine function between the
    initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
    initial lr set in the optimizer.
    Args:
        optimizer (:class:`~torch.optim.Optimizer`):
            The optimizer for which to schedule the learning rate.
        num_warmup_steps (:obj:`int`):
            The number of steps for the warmup phase.
        num_training_steps (:obj:`int`):
            The total number of training steps.
        min_lr_ratio (:obj:`float`, `optional`, defaults to 0.0):
            The minimum lr ratio w.r.t the maximum.
        num_cycles (:obj:`float`, `optional`, defaults to 0.5):
            The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
            following a half-cosine).
        last_epoch (:obj:`int`, `optional`, defaults to -1):
            The index of the last epoch when resuming training.
    Return:
        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
    """
    assert min_lr_ratio >= 0 and min_lr_ratio <= 1.
    coef = (1 - min_lr_ratio) * 0.5
    intercept = (1 + min_lr_ratio) * 0.5

    def lr_lambda(current_step):
        if current_step < num_warmup_steps:
            return float(current_step) / float(max(1, num_warmup_steps))
        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
        x = math.cos(math.pi * float(num_cycles) * 2.0 * progress)
        return max(0.0, x * coef + intercept)

    return LambdaLR(optimizer, lr_lambda, last_epoch)


def get_constant_schedule_with_warmup(
    optimizer: Optimizer,
    num_warmup_steps: int,
    last_epoch: int = -1,
):

    def lr_lambda(current_step):
        return min(1, float(current_step) / float(max(1, num_warmup_steps)))

    return LambdaLR(optimizer, lr_lambda, last_epoch)


def prepare_decoder_attention_mask(attention_mask, input_shape, inputs_embeds):
    # create causal mask
    # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
    combined_attention_mask = None
    if input_shape[-1] > 1:
        combined_attention_mask = _make_causal_mask(
            input_shape,
            inputs_embeds.dtype,
            device=inputs_embeds.device,
        )

    if attention_mask is not None:
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype,
                                          tgt_len=input_shape[-1]).to(inputs_embeds.device)
        combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask +
                                   combined_attention_mask)

    return combined_attention_mask


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


def get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


def get_wsd_schedule_with_warmup(
    optimizer: Optimizer,
    num_warmup_steps: int,
    num_training_steps: int,
    min_lr_ratio: float = 0.0,
    num_cycles: float = 0.5,
    last_epoch: int = -1,
    stable_ratio: float = 0.9,
):
    """
    Create a Warmup-Stable-Decay learning rate scheduler.

    The schedule follows three phases:
    1. Warmup: Learning rate increases linearly from 0 to the initial LR
    2. Stable: Learning rate remains constant at the initial LR
    3. Decay: Learning rate decreases following a cosine curve to min_lr_ratio * initial LR

    Args:
        optimizer (:class:`~torch.optim.Optimizer`):
            The optimizer for which to schedule the learning rate.
        num_warmup_steps (:obj:`int`):
            The number of steps for the warmup phase.
        num_training_steps (:obj:`int`):
            The total number of training steps.
        min_lr_ratio (:obj:`float`, `optional`, defaults to 0.0):
            The minimum learning rate ratio w.r.t the initial learning rate.
        num_cycles (:obj:`float`, `optional`, defaults to 0.5):
            The number of waves in the cosine schedule during decay phase.
        last_epoch (:obj:`int`, `optional`, defaults to -1):
            The index of the last epoch when resuming training.
        stable_ratio (:obj:`float`, `optional`, defaults to 0.0):
            The ratio of non-warmup steps that should maintain a constant learning rate.
            Set to 0.0 to behave exactly like cosine schedule.

    Return:
        :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
    """
    remaining_steps = max(0, num_training_steps - num_warmup_steps)
    num_stable_steps = int(remaining_steps * stable_ratio)
    num_decay_steps = remaining_steps - num_stable_steps

    def lr_lambda(current_step):
        if current_step < num_warmup_steps:
            return float(current_step) / float(max(1, num_warmup_steps))
        if current_step < num_warmup_steps + num_stable_steps:
            return 1.0
        if current_step < num_training_steps:
            progress = float(current_step - num_warmup_steps - num_stable_steps) / float(max(1, num_decay_steps))
            value = max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))
            return (1.0 - min_lr_ratio) * value + min_lr_ratio
        return min_lr_ratio

    return LambdaLR(optimizer, lr_lambda, last_epoch)
