import math
import warnings
from typing import Union, Tuple, Optional

import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F

from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput, BaseModelOutput
from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
from transformers.integrations.fsdp import is_fsdp_managed_module
from transformers.models.hubert.modeling_hubert import (
    HubertFeatureEncoder, 
    HubertFeatureProjection, 
    HubertEncoderStableLayerNorm, 
    HubertEncoder, 
    _HIDDEN_STATES_START_POSITION
)

from .configuration_hubert_spkreg import HubertSpkRegConfig


class HubertSpkRegPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = HubertSpkRegConfig
    base_model_prefix = "hubert"
    main_input_name = "input_values"
    supports_gradient_checkpointing = True
    _supports_flash_attn_2 = True
    _supports_sdpa = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, nn.Conv1d):
            if is_deepspeed_zero3_enabled():
                import deepspeed

                if hasattr(module, "weight_v") and hasattr(module, "weight_g"):
                    with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0):
                        nn.init.kaiming_normal_(module.weight.data)
                else:
                    with deepspeed.zero.GatheredParameters(module.weight, modifier_rank=0):
                        nn.init.kaiming_normal_(module.weight.data)
            else:
                nn.init.kaiming_normal_(module.weight.data)

        if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None:
            module.bias.data.zero_()

    def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
        """
        Computes the output length of the convolutional layers
        """

        def _conv_out_length(input_length, kernel_size, stride):
            # 1D convolutional layer output length formula taken
            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
            return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1

        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
            input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

        return input_lengths

    def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
        output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
        batch_size = attention_mask.shape[0]

        attention_mask = torch.zeros(
            (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
        )
        # these two operations makes sure that all values before the output lengths idxs are attended to
        attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
        attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
        return attention_mask
    

# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
def _compute_mask_indices(
    shape: Tuple[int, int],
    mask_prob: float,
    mask_length: int,
    attention_mask: Optional[torch.LongTensor] = None,
    min_masks: int = 0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
    ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on
    CPU as part of the preprocessing during training.

    Args:
        shape: The shape for which to compute masks. This should be of a tuple of size 2 where
               the first element is the batch size and the second element is the length of the axis to span.
        mask_prob:  The percentage of the whole axis (between 0 and 1) which will be masked. The number of
                    independently generated mask spans of length `mask_length` is computed by
                    `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
                    actual percentage will be smaller.
        mask_length: size of the mask
        min_masks: minimum number of masked spans
        attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
                        each batch dimension.
    """
    batch_size, sequence_length = shape

    if mask_length < 1:
        raise ValueError("`mask_length` has to be bigger than 0.")

    if mask_length > sequence_length:
        raise ValueError(
            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
            f" and `sequence_length`: {sequence_length}`"
        )

    # epsilon is used for probabilistic rounding
    epsilon = np.random.rand(1).item()

    def compute_num_masked_span(input_length):
        """Given input length, compute how many spans should be masked"""
        num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
        num_masked_span = max(num_masked_span, min_masks)

        # make sure num masked span <= sequence_length
        if num_masked_span * mask_length > sequence_length:
            num_masked_span = sequence_length // mask_length

        # make sure num_masked span is also <= input_length - (mask_length - 1)
        if input_length - (mask_length - 1) < num_masked_span:
            num_masked_span = max(input_length - (mask_length - 1), 0)

        return num_masked_span

    # compute number of masked spans in batch
    input_lengths = (
        attention_mask.sum(-1).detach().tolist()
        if attention_mask is not None
        else [sequence_length for _ in range(batch_size)]
    )

    # SpecAugment mask to fill
    spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
    spec_aug_mask_idxs = []

    max_num_masked_span = compute_num_masked_span(sequence_length)

    if max_num_masked_span == 0:
        return spec_aug_mask

    for input_length in input_lengths:
        # compute num of masked spans for this input
        num_masked_span = compute_num_masked_span(input_length)

        # get random indices to mask
        spec_aug_mask_idx = np.random.choice(
            np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
        )

        # pick first sampled index that will serve as a dummy index to pad vector
        # to ensure same dimension for all batches due to probabilistic rounding
        # Picking first sample just pads those vectors twice.
        if len(spec_aug_mask_idx) == 0:
            # this case can only happen if `input_length` is strictly smaller then
            # `sequence_length` in which case the last token has to be a padding
            # token which we can use as a dummy mask id
            dummy_mask_idx = sequence_length - 1
        else:
            dummy_mask_idx = spec_aug_mask_idx[0]

        spec_aug_mask_idx = np.concatenate(
            [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
        )
        spec_aug_mask_idxs.append(spec_aug_mask_idx)

    spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)

    # expand masked indices to masked spans
    spec_aug_mask_idxs = np.broadcast_to(
        spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
    )
    spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)

    # add offset to the starting indexes so that indexes now create a span
    offsets = np.arange(mask_length)[None, None, :]
    offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
        batch_size, max_num_masked_span * mask_length
    )
    spec_aug_mask_idxs = spec_aug_mask_idxs + offsets

    # ensure that we cannot have indices larger than sequence_length
    if spec_aug_mask_idxs.max() > sequence_length - 1:
        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1

    # scatter indices to mask
    np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)

    return spec_aug_mask


class HubertSpkRegModel(HubertSpkRegPreTrainedModel):

    def __init__(self, config: HubertSpkRegConfig):
        super().__init__(config)
        self.config = config
        self.feature_extractor = HubertFeatureEncoder(config)
        self.feature_projection = HubertFeatureProjection(config)

        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
            self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_())

        if config.do_stable_layer_norm:
            self.encoder = HubertEncoderStableLayerNorm(config)
        else:
            self.encoder = HubertEncoder(config)

        # Initialize weights and apply final processing
        self.post_init()

    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states
    def _mask_hidden_states(
        self,
        hidden_states: torch.FloatTensor,
        mask_time_indices: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
    ):
        """
        Masks extracted features along time axis and/or along feature axis according to
        [SpecAugment](https://arxiv.org/abs/1904.08779).
        """

        # `config.apply_spec_augment` can set masking to False
        if not getattr(self.config, "apply_spec_augment", True):
            return hidden_states

        # generate indices & apply SpecAugment along time axis
        batch_size, sequence_length, hidden_size = hidden_states.size()

        if mask_time_indices is not None:
            # apply SpecAugment along time axis with given mask_time_indices
            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
        elif self.config.mask_time_prob > 0 and self.training:
            mask_time_indices = _compute_mask_indices(
                (batch_size, sequence_length),
                mask_prob=self.config.mask_time_prob,
                mask_length=self.config.mask_time_length,
                attention_mask=attention_mask,
                min_masks=self.config.mask_time_min_masks,
            )
            mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool)
            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)

        if self.config.mask_feature_prob > 0 and self.training:
            # generate indices & apply SpecAugment along feature axis
            mask_feature_indices = _compute_mask_indices(
                (batch_size, hidden_size),
                mask_prob=self.config.mask_feature_prob,
                mask_length=self.config.mask_feature_length,
                min_masks=self.config.mask_feature_min_masks,
            )
            mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool)
            mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
            hidden_states[mask_feature_indices] = 0

        return hidden_states

    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        mask_time_indices: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        """

        Returns:

        Example:

        ```python
        >>> from transformers import AutoProcessor, HubertModel
        >>> from datasets import load_dataset
        >>> import soundfile as sf

        >>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
        >>> model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")


        >>> def map_to_array(batch):
        ...     speech, _ = sf.read(batch["file"])
        ...     batch["speech"] = speech
        ...     return batch


        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.map(map_to_array)

        >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
        >>> hidden_states = model(input_values).last_hidden_state
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        extract_features = self.feature_extractor(input_values)
        extract_features = extract_features.transpose(1, 2)

        if attention_mask is not None:
            # compute reduced attention_mask corresponding to feature vectors
            attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask)

        hidden_states = self.feature_projection(extract_features)
        hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)

        encoder_outputs = self.encoder(
            hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = encoder_outputs[0]

        if not return_dict:
            return (hidden_states,) + encoder_outputs[1:]

        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )
    

class AngularLinear(nn.Module):

    def __init__(self, in_features: int, out_features: int):
        super(AngularLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = torch.nn.Parameter(
            torch.FloatTensor(out_features, in_features), requires_grad=True
        )
        nn.init.xavier_normal_(self.weight, gain=1)

    def forward(
        self, 
        inputs: torch.Tensor, 
    ):
        # Calculation of cos(theta)
        cosine = F.linear(F.normalize(inputs), F.normalize(self.weight))
        return cosine

    def extra_repr(self) -> str:
        return 'in_features={}, out_features={}'.format(
            self.in_features, self.out_features
        )


class AMSoftmaxLoss(nn.Module):
    """Additive Margin Softmax (CosFace).
    
    Paper: Wang, Feng, et al. "Additive margin softmax for face verification." 
    IEEE Signal Processing Letters 25.7 (2018): 926-930.
    """
    def __init__(
        self, 
        scale: float = 30.0, 
        margin: float = 0.35, 
        label_smoothing: float = 0.0, 
        reduction: str = "mean"
    ):
        """
        Args:
            num_classes: Number of classes (output dimension)
            scale: Scaling factor for logits (default: 30.0)
            margin: Angular margin (default: 0.35)
        """
        super(AMSoftmaxLoss, self).__init__()
        self.scale = scale
        self.margin = margin
        self.label_smoothing = label_smoothing
        self.reduction = reduction

    def forward(
        self, 
        inputs: torch.Tensor, 
        targets: torch.Tensor, 
    ):
        """
        Args:
            inputs: Input features of shape (batch_size, num_labels)
            targets: Ground truth labels of shape (batch_size)
            label_smoothing: Label smoothing factor (default: 0.0)
            reduction: Reduction method (default: "mean")
        Returns:
            Loss value
        """
        _, num_labels = inputs.shape
        # `inputs` are the outputs from AngularLinear()
        cos_theta = torch.clamp(inputs, -1.0 + 1e-7, 1.0 - 1e-7)
        psi = cos_theta - self.margin
        one_hot = nn.functional.one_hot(targets, num_labels)
        outputs = self.scale * torch.where(one_hot.bool(), psi, cos_theta)
        loss = F.cross_entropy(
            outputs, targets, label_smoothing=self.label_smoothing, reduction=self.reduction
        )
        return loss


class AAMSoftmaxLoss(nn.Module):
    """Additive Angular Margin Softmax (ArcFace).

    Paper: Deng, Jiankang, et al. "Arcface: Additive angular margin loss for deep face recognition." 
    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
    """
    def __init__(
        self, 
        scale: float = 30.0, 
        margin: float = 0.2, 
        easy_margin: bool = False, 
        label_smoothing: float = 0.0, 
        reduction: str = "mean"
    ):
        """
        Args:
            num_classes: Number of classes (output dimension)
            scale: Scaling factor for logits (default: 30.0)
            margin: Angular margin (default: 0.35)
            easy_margin: Use the easy margin loss (default: False)
        """
        super(AAMSoftmaxLoss, self).__init__()
        self.scale = scale
        self.margin = margin
        self.easy_margin = easy_margin
        self.label_smoothing = label_smoothing
        self.reduction = reduction
        
    def forward(
        self, 
        inputs: torch.Tensor, 
        targets: torch.Tensor, 
    ):
        """
        Args:
            inputs: Input features of shape (batch_size, num_labels)
            targets: Ground truth labels of shape (batch_size)
        Returns:
            Loss value
        """
        _, num_labels = inputs.shape
        # `inputs` are the outputs from AngularLinear()
        epsilon = 1e-6
        # theta = torch.acos(cos_theta)
        # psi = torch.cos(theta + self.margin)
        cos_theta = torch.clamp(inputs, -1.0 + epsilon, 1.0 - epsilon)
        sin_theta = torch.sqrt(1.0 - torch.pow(cos_theta, 2))
        sin_theta = torch.clamp(sin_theta, 0.0 + epsilon, 1.0 - epsilon)

        cos_m = math.cos(self.margin)
        sin_m = math.sin(self.margin)
        psi = cos_theta * cos_m - sin_theta * sin_m # cos(theta + m)

        if self.easy_margin:
            psi = torch.where(cos_theta > 0, psi, cos_theta)
        else:
            # Make the function cos(theta+m) monotonic decreasing while theta in [0°, 180°]
            psi = torch.where((cos_theta - math.cos(math.pi - self.margin)) > 0, psi, cos_theta - self.margin)

        one_hot = nn.functional.one_hot(targets, num_labels)
        outputs = self.scale * torch.where(one_hot.bool(), psi, cos_theta)
        loss = F.cross_entropy(
            outputs, targets, label_smoothing=self.label_smoothing, reduction=self.reduction
        )
        return loss
    

class HubertSpkRegForSequenceClassification(HubertSpkRegPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        if hasattr(config, "add_adapter") and config.add_adapter:
            raise ValueError(
                "Sequence classification does not support the use of Hubert adapters (config.add_adapter=True)"
            )
        self.hubert = HubertSpkRegModel(config)
        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
        if config.use_weighted_layer_sum:
            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
        self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
        
        if self.config.loss_fct == 'cross_entropy':
            self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
        elif self.config.loss_fct == 'additive_margin':
            self.classifier = AngularLinear(config.classifier_proj_size, config.num_labels)
        elif self.config.loss_fct == 'additive_angular_margin':
            self.classifier = AngularLinear(config.classifier_proj_size, config.num_labels)
        else:
            raise ValueError(f"Unsupported loss function: {self.config.loss_fct}")

        # Initialize weights and apply final processing
        self.post_init()

    def freeze_feature_extractor(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameters will
        not be updated during training.
        """
        warnings.warn(
            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. "
            "Please use the equivalent `freeze_feature_encoder` method instead.",
            FutureWarning,
        )
        self.freeze_feature_encoder()

    def freeze_feature_encoder(self):
        """
        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
        not be updated during training.
        """
        self.hubert.feature_extractor._freeze_parameters()

    def freeze_base_model(self):
        """
        Calling this function will disable the gradient computation for the base model so that its parameters will not
        be updated during training. Only the classification head will be updated.
        """
        for param in self.hubert.parameters():
            param.requires_grad = False

    def forward(
        self,
        input_values: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
    ) -> Union[Tuple, SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states

        outputs = self.hubert(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.config.use_weighted_layer_sum:
            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
            hidden_states = torch.stack(hidden_states, dim=1)
            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
        else:
            hidden_states = outputs[0]

        hidden_states = self.projector(hidden_states)
        if attention_mask is None:
            pooled_output = hidden_states.mean(dim=1)
        else:
            padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
            hidden_states[~padding_mask] = 0.0
            pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)

        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.config.loss_fct == 'cross_entropy':
                loss_fct = nn.CrossEntropyLoss(
                    label_smoothing=self.config.label_smoothing, 
                    reduction=self.config.reduction
                )
            elif self.config.loss_fct == 'additive_margin':
                loss_fct = AMSoftmaxLoss(
                    scale=self.config.scale, 
                    margin=self.config.margin, 
                    label_smoothing=self.config.label_smoothing, 
                    reduction=self.config.reduction
                )
            elif self.config.loss_fct == 'additive_angular_margin':
                loss_fct = AAMSoftmaxLoss(
                    scale=self.config.scale, 
                    margin=self.config.margin, 
                    easy_margin=self.config.easy_margin, 
                    label_smoothing=self.config.label_smoothing, 
                    reduction=self.config.reduction
                )
            loss = loss_fct(
                logits.view(-1, self.config.num_labels), 
                labels.view(-1), 
            )

        if not return_dict:
            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )