pipeline/pipeline.py

import inspect
from dataclasses import dataclass
from typing import Callable, Dict, List, Optional, Union

import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKLTemporalDecoder
from diffusers.utils import BaseOutput, logging
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from utils.scheduling_euler_discrete_karras_fix import EulerDiscreteScheduler

from models.unet_spatio_temporal_condition_controlnet import UNetSpatioTemporalConditionControlNetModel
from models.traj_ctrlnet import FlowControlNet as DragControlNet
from models.ldmk_ctrlnet import FlowControlNet as FaceControlNet


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


def _get_add_time_ids(
        noise_aug_strength,
        dtype,
        batch_size,
        fps=4,
        motion_bucket_id=128,
        unet=None,
    ):
        add_time_ids = [fps, motion_bucket_id, noise_aug_strength]

        passed_add_embed_dim = unet.config.addition_time_embed_dim * len(add_time_ids)
        expected_add_embed_dim = unet.add_embedding.linear_1.in_features

        if expected_add_embed_dim != passed_add_embed_dim:
            raise ValueError(
                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
            )

        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
        # add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)


        return add_time_ids


def _append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
    return x[(...,) + (None,) * dims_to_append]


def tensor2vid(video: torch.Tensor, processor, output_type="np"):
    # Based on:
    # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78

    batch_size, channels, num_frames, height, width = video.shape
    outputs = []
    for batch_idx in range(batch_size):
        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
        batch_output = processor.postprocess(batch_vid, output_type)

        outputs.append(batch_output)

    return outputs


@dataclass
class FlowControlNetPipelineOutput(BaseOutput):
    r"""
    Output class for zero-shot text-to-video pipeline.

    Args:
        frames (`[List[PIL.Image.Image]`, `np.ndarray`]):
            List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
            num_channels)`.
    """

    frames: Union[List[PIL.Image.Image], np.ndarray]
    controlnet_flow: torch.Tensor


class FlowControlNetPipeline(DiffusionPipeline):
    model_cpu_offload_seq = "image_encoder->unet->vae"
    _callback_tensor_inputs = ["latents"]
    def __init__(
        self,
        vae: AutoencoderKLTemporalDecoder,
        image_encoder: CLIPVisionModelWithProjection,
        unet: UNetSpatioTemporalConditionControlNetModel,
        drag_controlnet: DragControlNet,
        face_controlnet: FaceControlNet,
        scheduler: EulerDiscreteScheduler,
        feature_extractor: CLIPImageProcessor,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            image_encoder=image_encoder,
            drag_controlnet=drag_controlnet,
            face_controlnet=face_controlnet, 
            unet=unet,
            scheduler=scheduler,
            feature_extractor=feature_extractor,
        )
        
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)


    def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance):
        dtype = next(self.image_encoder.parameters()).dtype

        if not isinstance(image, torch.Tensor):
            image = self.image_processor.pil_to_numpy(image)
            image = self.image_processor.numpy_to_pt(image)

        #image = image.unsqueeze(0)
        image = _resize_with_antialiasing(image, (224, 224))

        image = image.to(device=device, dtype=dtype)
        image_embeddings = self.image_encoder(image).image_embeds
        image_embeddings = image_embeddings.unsqueeze(1)

        # duplicate image embeddings for each generation per prompt, using mps friendly method
        bs_embed, seq_len, _ = image_embeddings.shape
        image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
        image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)

        if do_classifier_free_guidance:
            negative_image_embeddings = torch.zeros_like(image_embeddings)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])

        return image_embeddings

    def _encode_vae_image(
        self,
        image: torch.Tensor,
        device,
        num_videos_per_prompt,
        do_classifier_free_guidance,
    ):
        image = image.to(device=device)
        image_latents = self.vae.encode(image).latent_dist.mode()

        if do_classifier_free_guidance:
            negative_image_latents = torch.zeros_like(image_latents)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            image_latents = torch.cat([negative_image_latents, image_latents])

        # duplicate image_latents for each generation per prompt, using mps friendly method
        image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)

        return image_latents

    def _get_add_time_ids(
        self,
        fps,
        motion_bucket_id,
        noise_aug_strength,
        dtype,
        batch_size,
        num_videos_per_prompt,
        do_classifier_free_guidance,
    ):
        add_time_ids = [fps, motion_bucket_id, noise_aug_strength]

        passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids)
        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features

        if expected_add_embed_dim != passed_add_embed_dim:
            raise ValueError(
                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
            )

        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
        add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)

        if do_classifier_free_guidance:
            add_time_ids = torch.cat([add_time_ids, add_time_ids])

        return add_time_ids

    def decode_latents(self, latents, num_frames, decode_chunk_size=14):
        # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
        latents = latents.flatten(0, 1)

        latents = 1 / self.vae.config.scaling_factor * latents

        accepts_num_frames = "num_frames" in set(inspect.signature(self.vae.forward).parameters.keys())

        # decode decode_chunk_size frames at a time to avoid OOM
        frames = []
        for i in range(0, latents.shape[0], decode_chunk_size):
            num_frames_in = latents[i : i + decode_chunk_size].shape[0]
            decode_kwargs = {}
            if accepts_num_frames:
                # we only pass num_frames_in if it's expected
                decode_kwargs["num_frames"] = num_frames_in

            frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample
            frames.append(frame)
        frames = torch.cat(frames, dim=0)

        # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
        frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        frames = frames.float()
        return frames

    def check_inputs(self, image, height, width):
        if (
            not isinstance(image, torch.Tensor)
            and not isinstance(image, PIL.Image.Image)
            and not isinstance(image, list)
        ):
            raise ValueError(
                "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
                f" {type(image)}"
            )

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

    def prepare_latents(
        self,
        batch_size,
        num_frames,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        shape = (
            batch_size,
            num_frames,
            num_channels_latents // 2,
            height // self.vae_scale_factor,
            width // self.vae_scale_factor,
        )

        # print(shape)

        # assert False
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @torch.no_grad()
    def __call__(
        self,
        image: Union[PIL.Image.Image, torch.FloatTensor], 
        controlnet_condition: torch.FloatTensor = None, 

        controlnet_flow: torch.FloatTensor = None, 
        landmarks: torch.FloatTensor = None, 

        drag_flow: torch.FloatTensor = None,
        mask: torch.FloatTensor = None,

        height: int = 576,
        width: int = 1024,
        num_frames: Optional[int] = None,
        num_inference_steps: int = 25,
        min_guidance_scale: float = 1.0,
        max_guidance_scale: float = 3.0,
        fps: int = 7,
        motion_bucket_id: int = 127,
        noise_aug_strength: int = 0.02,
        decode_chunk_size: Optional[int] = None,
        num_videos_per_prompt: Optional[int] = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        return_dict: bool = True,
        ctrl_scale_traj=1.0,
        ctrl_scale_ldmk=1.0,
        batch_size=1,
    ):

        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        num_frames = num_frames if num_frames is not None else self.unet.config.num_frames
        decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(image, height, width)

        # 2. Define call parameters
        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = max_guidance_scale > 1.0

        # 3. Encode input image
        image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)

        # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
        # is why it is reduced here.
        # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
        fps = fps - 1

        # 4. Encode input image using VAE
        image = self.image_processor.preprocess(image, height=height, width=width)
        noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype)
        image = image + noise_aug_strength * noise

        needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
        if needs_upcasting:
            self.vae.to(dtype=torch.float32)

        image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
        image_latents = image_latents.to(image_embeddings.dtype)

        # cast back to fp16 if needed
        if needs_upcasting:
            self.vae.to(dtype=torch.float16)

        # Repeat the image latents for each frame so we can concatenate them with the noise
        # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
        image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
        #image_latents = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents
        
        # 5. Get Added Time IDs
        added_time_ids = self._get_add_time_ids(
            fps,
            motion_bucket_id,
            noise_aug_strength,
            image_embeddings.dtype,
            batch_size,
            num_videos_per_prompt,
            do_classifier_free_guidance,
        )
        added_time_ids = added_time_ids.to(device)

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
 
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_videos_per_prompt,
            num_frames,
            num_channels_latents,
            height,
            width,
            image_embeddings.dtype,
            device,
            generator,
            latents,
        )


        #prepare controlnet condition
        controlnet_condition = self.image_processor.preprocess(controlnet_condition, height=height, width=width)
        # controlnet_condition = controlnet_condition.unsqueeze(0)
        controlnet_condition = torch.cat([controlnet_condition] * 2) if do_classifier_free_guidance else controlnet_condition
        controlnet_condition = controlnet_condition.to(device, latents.dtype)

        controlnet_flow = torch.cat([controlnet_flow] * 2) if do_classifier_free_guidance else controlnet_flow
        controlnet_flow = controlnet_flow.to(device, latents.dtype)

        drag_flow = torch.cat([drag_flow] * 2) if do_classifier_free_guidance else drag_flow
        drag_flow = drag_flow.to(device, latents.dtype)

        # mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
        mask = mask.to(device, latents.dtype)

        landmarks = torch.cat([landmarks] * 2) if do_classifier_free_guidance else landmarks
        landmarks = landmarks.to(device, latents.dtype)

        # 7. Prepare guidance scale
        # modified num_frames to window_size here !!!!!!!!!!!!!!
        guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
        guidance_scale = guidance_scale.to(device, latents.dtype)
        guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
        guidance_scale = _append_dims(guidance_scale, latents.ndim)

        self._guidance_scale = guidance_scale

        noise_aug_strength = 0.02 #"¯\_(ツ)_/¯
        added_time_ids = _get_add_time_ids(
            noise_aug_strength,
            image_embeddings.dtype,
            batch_size,
            6,
            128,
            unet=self.unet,
        )
        added_time_ids = torch.cat([added_time_ids] * 2) 
        added_time_ids = added_time_ids.to(latents.device)

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        self._num_timesteps = len(timesteps)
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):

                # expand the latents if we are doing classifier free guidance
                latent_model_input_tmp = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input_tmp = self.scheduler.scale_model_input(latent_model_input_tmp, t)

                # Concatenate image_latents over channels dimention
                latent_model_input_tmp = torch.cat([latent_model_input_tmp, image_latents], dim=2)

                down_res_face_tmp, mid_res_face_tmp, controlnet_flow, _ = self.face_controlnet(
                    latent_model_input_tmp,
                    t,
                    encoder_hidden_states=image_embeddings,
                    controlnet_cond=controlnet_condition,
                    controlnet_flow=controlnet_flow,
                    landmarks=landmarks, 
                    added_time_ids=added_time_ids,
                    conditioning_scale=ctrl_scale_ldmk,
                    guess_mode=False,
                    return_dict=False,
                )

                down_res_drag_tmp, mid_res_drag_tmp, _, _ = self.drag_controlnet(
                    latent_model_input_tmp,
                    t,
                    encoder_hidden_states=image_embeddings,
                    controlnet_cond=controlnet_condition,
                    controlnet_flow=drag_flow,
                    added_time_ids=added_time_ids,
                    conditioning_scale=ctrl_scale_traj,
                    guess_mode=False,
                    return_dict=False,
                )

                down_block_res_samples_tmp = []
                for down_face, down_drag in zip(down_res_face_tmp, down_res_drag_tmp):
                    _, _, h, w = down_face.shape
                    mask_tmp = F.interpolate(mask, (h, w), mode='nearest')
                    res = down_face * mask_tmp + down_drag * (1 - mask_tmp)
                    down_block_res_samples_tmp.append(res)

                _, _, h, w = mid_res_face_tmp.shape
                mask_tmp = F.interpolate(mask, (h, w), mode='nearest')
                mid_block_res_sample_tmp = mid_res_face_tmp * mask_tmp + mid_res_drag_tmp * (1 - mask_tmp)

                # predict the noise residual
                noise_pred_tmp = self.unet(
                    latent_model_input_tmp,
                    t,
                    encoder_hidden_states=image_embeddings,
                    down_block_additional_residuals=down_block_res_samples_tmp,
                    mid_block_additional_residual=mid_block_res_sample_tmp,
                    added_time_ids=added_time_ids,
                    return_dict=False,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond_tmp, noise_pred_cond_tmp = noise_pred_tmp.chunk(2)
                    noise_pred_tmp = noise_pred_uncond_tmp + self.guidance_scale * (noise_pred_cond_tmp - noise_pred_uncond_tmp)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred_tmp, t, latents).prev_sample

                if callback_on_step_end is not None:
                    callback_kwargs = {}
                    for k in callback_on_step_end_tensor_inputs:
                        callback_kwargs[k] = locals()[k]
                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                    latents = callback_outputs.pop("latents", latents)

                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
        
        if not output_type == "latent":
            # cast back to fp16 if needed
            if needs_upcasting:
                self.vae.to(dtype=torch.float16)
            frames = self.decode_latents(latents.to(self.vae.dtype), num_frames, decode_chunk_size)
            frames = tensor2vid(frames, self.image_processor, output_type=output_type)
        else:
            frames = latents

        self.maybe_free_model_hooks()

        if not return_dict:
            return frames, controlnet_flow

        return FlowControlNetPipelineOutput(
            frames=frames, 
            controlnet_flow=controlnet_flow
            )
    

# resizing utils
# TODO: clean up later
def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True):
    
    if input.ndim == 3:
        input = input.unsqueeze(0)  # Add a batch dimension
        
    h, w = input.shape[-2:]
    factors = (h / size[0], w / size[1])

    # First, we have to determine sigma
    # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171
    sigmas = (
        max((factors[0] - 1.0) / 2.0, 0.001),
        max((factors[1] - 1.0) / 2.0, 0.001),
    )

    # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma
    # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206
    # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now
    ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))

    # Make sure it is odd
    if (ks[0] % 2) == 0:
        ks = ks[0] + 1, ks[1]

    if (ks[1] % 2) == 0:
        ks = ks[0], ks[1] + 1

    input = _gaussian_blur2d(input, ks, sigmas)

    output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)
    return output


def _compute_padding(kernel_size):
    """Compute padding tuple."""
    # 4 or 6 ints:  (padding_left, padding_right,padding_top,padding_bottom)
    # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad
    if len(kernel_size) < 2:
        raise AssertionError(kernel_size)
    computed = [k - 1 for k in kernel_size]

    # for even kernels we need to do asymmetric padding :(
    out_padding = 2 * len(kernel_size) * [0]

    for i in range(len(kernel_size)):
        computed_tmp = computed[-(i + 1)]

        pad_front = computed_tmp // 2
        pad_rear = computed_tmp - pad_front

        out_padding[2 * i + 0] = pad_front
        out_padding[2 * i + 1] = pad_rear

    return out_padding


def _filter2d(input, kernel):
    # prepare kernel
    b, c, h, w = input.shape
    tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)

    tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)

    height, width = tmp_kernel.shape[-2:]

    padding_shape: list[int] = _compute_padding([height, width])
    input = torch.nn.functional.pad(input, padding_shape, mode="reflect")

    # kernel and input tensor reshape to align element-wise or batch-wise params
    tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)
    input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))

    # convolve the tensor with the kernel.
    output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)

    out = output.view(b, c, h, w)
    return out


def _gaussian(window_size: int, sigma):
    if isinstance(sigma, float):
        sigma = torch.tensor([[sigma]])

    batch_size = sigma.shape[0]

    x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)

    if window_size % 2 == 0:
        x = x + 0.5

    gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))

    return gauss / gauss.sum(-1, keepdim=True)


def _gaussian_blur2d(input, kernel_size, sigma):
    if isinstance(sigma, tuple):
        sigma = torch.tensor([sigma], dtype=input.dtype)
    else:
        sigma = sigma.to(dtype=input.dtype)

    ky, kx = int(kernel_size[0]), int(kernel_size[1])
    bs = sigma.shape[0]
    kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1))
    kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1))
    out_x = _filter2d(input, kernel_x[..., None, :])
    out = _filter2d(out_x, kernel_y[..., None])

    return out


def get_views(video_length, window_size=14, stride=7):
    num_blocks_time = (video_length - window_size) // stride + 1
    views = []
    for i in range(num_blocks_time):
        t_start = int(i * stride)
        t_end = t_start + window_size
        views.append((t_start,t_end))
    return views