# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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
# limitations under the License.
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn
import math

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput
from diffusers.models.modeling_utils import ModelMixin

from .attention import BasicTransformerBlock

@dataclass
class TransformerTemporalModelOutput(BaseOutput):
    """
    The output of [`TransformerTemporalModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
            The hidden states output conditioned on `encoder_hidden_states` input.
    """

    sample: torch.FloatTensor


class TransformerTemporalModel(ModelMixin, ConfigMixin):
    """
    A Transformer model for video-like data.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
            This is fixed during training since it is used to learn a number of position embeddings.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
        attention_bias (`bool`, *optional*):
            Configure if the `TransformerBlock` attention should contain a bias parameter.
        double_self_attention (`bool`, *optional*):
            Configure if each `TransformerBlock` should contain two self-attention layers.
    """

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        dropout: float = 0.0,
        norm_num_groups: int = 32,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        sample_size: Optional[int] = None,
        activation_fn: str = "geglu",
        norm_elementwise_affine: bool = True,
        double_self_attention: bool = True,
    ):
        super().__init__()
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        inner_dim = num_attention_heads * attention_head_dim

        self.in_channels = in_channels

        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
        self.proj_in = nn.Linear(in_channels, inner_dim)

        # 3. Define transformers blocks
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    dropout=dropout,
                    cross_attention_dim=cross_attention_dim,
                    activation_fn=activation_fn,
                    attention_bias=attention_bias,
                    double_self_attention=double_self_attention,
                    norm_elementwise_affine=norm_elementwise_affine,
                )
                for d in range(num_layers)
            ]
        )

        self.proj_out = nn.Linear(inner_dim, in_channels)

    def forward(
        self,
        hidden_states,
        encoder_hidden_states=None,
        timestep=None,
        class_labels=None,
        num_frames=1,
        cross_attention_kwargs=None,
        return_dict: bool = True,
        attention_mask=None,
        encoder_attention_mask=None,
        **kwargs,
    ):
        """
        The [`TransformerTemporal`] forward method.

        Args:
            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                Input hidden_states.
            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.long`, *optional*):
                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                `AdaLayerZeroNorm`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
                returned, otherwise a `tuple` where the first element is the sample tensor.
        """
        # 1. Input
        batch_frames, channel, height, width = hidden_states.shape
        batch_size = batch_frames // num_frames
        if attention_mask is not None:
            
            if not isinstance(attention_mask, list):
                # Attn mask - (32, 1, 1024
                new_attn_mask = attention_mask.clone() 
                # Convert to (2,16,1024)
                new_attn_mask = new_attn_mask.permute(1,0,2).reshape(-1,num_frames, new_attn_mask.shape[2])
                # spatial_dim_attn_mask = int(math.sqrt(new_attn_mask.shape[-1]))
                scaling_factor = int(math.sqrt(new_attn_mask.shape[2] / (height*width)))
                
                mask_x = int(height * scaling_factor)
                mask_y = int(width * scaling_factor)
                
                
                # Scale the attention mask possibly
                new_attn_mask = new_attn_mask.reshape(-1, num_frames, mask_x, mask_y)[:,:,::scaling_factor, ::scaling_factor]
                # Convert to (2,16,64)
                new_attn_mask = new_attn_mask.reshape(-1, num_frames, height*width).permute(0,2,1)
                # Convert to (128, 1, 16) when hidden states are (128, 16, 1280)
                new_attn_mask = new_attn_mask.reshape(-1,1,num_frames)

                # Trying to invert this mask, so that background is the only thing active - 
                new_attn_mask = torch.where(new_attn_mask < 0., 0., -10000.).type(new_attn_mask.dtype).to(new_attn_mask.device)
            else:
                new_attn_mask_list = []
                for attn_mask in attention_mask:
                    new_attn_mask = attn_mask.clone() 
                    new_attn_mask = new_attn_mask.permute(1,0,2).reshape(-1,num_frames, new_attn_mask.shape[2])
                    scaling_factor = int(math.sqrt(new_attn_mask.shape[2] / (height*width)))

                    mask_x = int(height * scaling_factor)
                    mask_y = int(width * scaling_factor)


                    # Scale the attention mask possibly
                    new_attn_mask = new_attn_mask.reshape(-1, num_frames, mask_x, mask_y)[:,:,::scaling_factor, ::scaling_factor]
                    new_attn_mask = new_attn_mask.reshape(-1, num_frames, height*width).permute(0,2,1)
                    new_attn_mask = new_attn_mask.reshape(-1,1,num_frames)
                    new_attn_mask = torch.where(new_attn_mask < 0., 0., -10000.).type(new_attn_mask.dtype).to(new_attn_mask.device)
                    new_attn_mask_list.append(new_attn_mask)

                new_attn_mask = new_attn_mask_list                
        else:
            new_attn_mask = None
            
        residual = hidden_states

        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width)
        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)

        hidden_states = self.norm(hidden_states)
        hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel)

        hidden_states = self.proj_in(hidden_states)


        # 2. Blocks
        for block in self.transformer_blocks:
            hidden_states = block(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                timestep=timestep,
                cross_attention_kwargs=cross_attention_kwargs,
                class_labels=class_labels,
                attention_mask=new_attn_mask,
                encoder_attention_mask=encoder_attention_mask,
                # make_2d_attention_mask=True,  # Check this 
                # block_diagonal_attention=True,  # TODO - Check this                
                **kwargs,
            )

        # 3. Output
        hidden_states = self.proj_out(hidden_states)
        hidden_states = (
            hidden_states[None, None, :]
            .reshape(batch_size, height, width, channel, num_frames)
            .permute(0, 3, 4, 1, 2)
            .contiguous()
        )
        hidden_states = hidden_states.reshape(batch_frames, channel, height, width)

        output = hidden_states + residual

        if not return_dict:
            return (output,)

        return TransformerTemporalModelOutput(sample=output)