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	# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py

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

	import numpy as np
	import torch
	from tqdm import tqdm

	from diffusers.utils import is_accelerate_available
	from packaging import version
	from transformers import CLIPTextModel, CLIPTokenizer

	from diffusers.configuration_utils import FrozenDict
	from diffusers.models import AutoencoderKL
	from diffusers import DiffusionPipeline
	from diffusers.schedulers import (
	DDIMScheduler,
	DPMSolverMultistepScheduler,
	EulerAncestralDiscreteScheduler,
	EulerDiscreteScheduler,
	LMSDiscreteScheduler,
	PNDMScheduler,
	)
	from diffusers.utils import deprecate, logging, BaseOutput

	from einops import rearrange

	from animatediff.models.unet import UNet3DConditionModel
	from animatediff.pipelines.pipeline_animation import AnimationPipelineOutput


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


	class AnimationPipeline(DiffusionPipeline):
	_optional_components = []

	def __init__(
	self,
	vae: AutoencoderKL,
	text_encoder: CLIPTextModel,
	tokenizer: CLIPTokenizer,
	unet: UNet3DConditionModel,
	scheduler: Union[
	DDIMScheduler,
	PNDMScheduler,
	LMSDiscreteScheduler,
	EulerDiscreteScheduler,
	EulerAncestralDiscreteScheduler,
	DPMSolverMultistepScheduler,
	],
	):
	super().__init__()

	if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
	deprecation_message = (
	f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
	f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
	"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
	" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
	" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
	" file"
	)
	deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
	new_config = dict(scheduler.config)
	new_config["steps_offset"] = 1
	scheduler._internal_dict = FrozenDict(new_config)

	if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
	deprecation_message = (
	f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
	" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
	" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
	" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
	" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
	)
	deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
	new_config = dict(scheduler.config)
	new_config["clip_sample"] = False
	scheduler._internal_dict = FrozenDict(new_config)

	is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
	version.parse(unet.config._diffusers_version).base_version
	) < version.parse("0.9.0.dev0")
	is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
	if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
	deprecation_message = (
	"The configuration file of the unet has set the default `sample_size` to smaller than"
	" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
	" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
	" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
	" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
	" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
	" in the config might lead to incorrect results in future versions. If you have downloaded this"
	" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
	" the `unet/config.json` file"
	)
	deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
	new_config = dict(unet.config)
	new_config["sample_size"] = 64
	unet._internal_dict = FrozenDict(new_config)

	self.register_modules(
	vae=vae,
	text_encoder=text_encoder,
	tokenizer=tokenizer,
	unet=unet,
	scheduler=scheduler,
	)
	self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)

	def enable_vae_slicing(self):
	self.vae.enable_slicing()

	def disable_vae_slicing(self):
	self.vae.disable_slicing()

	def enable_sequential_cpu_offload(self, gpu_id=0):
	if is_accelerate_available():
	from accelerate import cpu_offload
	else:
	raise ImportError("Please install accelerate via `pip install accelerate`")

	device = torch.device(f"cuda:{gpu_id}")

	for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
	if cpu_offloaded_model is not None:
	cpu_offload(cpu_offloaded_model, device)


	@property
	def _execution_device(self):
	if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
	return self.device
	for module in self.unet.modules():
	if (
	hasattr(module, "_hf_hook")
	and hasattr(module._hf_hook, "execution_device")
	and module._hf_hook.execution_device is not None
	):
	return torch.device(module._hf_hook.execution_device)
	return self.device

	def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
	batch_size = len(prompt) if isinstance(prompt, list) else 1

	text_inputs = self.tokenizer(
	prompt,
	padding="max_length",
	max_length=self.tokenizer.model_max_length,
	truncation=True,
	return_tensors="pt",
	)
	text_input_ids = text_inputs.input_ids
	untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

	if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
	removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
	logger.warning(
	"The following part of your input was truncated because CLIP can only handle sequences up to"
	f" {self.tokenizer.model_max_length} tokens: {removed_text}"
	)

	if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
	attention_mask = text_inputs.attention_mask.to(device)
	else:
	attention_mask = None

	text_embeddings = self.text_encoder(
	text_input_ids.to(device),
	attention_mask=attention_mask,
	)
	text_embeddings = text_embeddings[0]

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

	# get unconditional embeddings for classifier free guidance
	if do_classifier_free_guidance:
	uncond_tokens: List[str]
	if negative_prompt is None:
	uncond_tokens = [""] * batch_size
	elif type(prompt) is not type(negative_prompt):
	raise TypeError(
	f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
	f" {type(prompt)}."
	)
	elif isinstance(negative_prompt, str):
	uncond_tokens = [negative_prompt]
	elif batch_size != len(negative_prompt):
	raise ValueError(
	f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
	f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
	" the batch size of `prompt`."
	)
	else:
	uncond_tokens = negative_prompt

	max_length = text_input_ids.shape[-1]
	uncond_input = self.tokenizer(
	uncond_tokens,
	padding="max_length",
	max_length=max_length,
	truncation=True,
	return_tensors="pt",
	)

	if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
	attention_mask = uncond_input.attention_mask.to(device)
	else:
	attention_mask = None

	uncond_embeddings = self.text_encoder(
	uncond_input.input_ids.to(device),
	attention_mask=attention_mask,
	)
	uncond_embeddings = uncond_embeddings[0]

	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
	seq_len = uncond_embeddings.shape[1]
	uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
	uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)

	# 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
	text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

	return text_embeddings

	def decode_latents(self, latents):
	video_length = latents.shape[2]
	latents = 1 / 0.18215 * latents
	latents = rearrange(latents, "b c f h w -> (b f) c h w")
	# video = self.vae.decode(latents).sample
	video = []
	for frame_idx in tqdm(range(latents.shape[0])):
	video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
	video = torch.cat(video)
	video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
	video = (video / 2 + 0.5).clamp(0, 1)
	# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
	video = video.cpu().float().numpy()
	return video

	def prepare_extra_step_kwargs(self, generator, eta):
	# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
	# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
	# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
	# and should be between [0, 1]

	accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
	extra_step_kwargs = {}
	if accepts_eta:
	extra_step_kwargs["eta"] = eta

	# check if the scheduler accepts generator
	accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
	if accepts_generator:
	extra_step_kwargs["generator"] = generator
	return extra_step_kwargs

	def check_inputs(self, prompt, height, width, callback_steps):
	if not isinstance(prompt, str) and not isinstance(prompt, list):
	raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

	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}.")

	if (callback_steps is None) or (
	callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
	):
	raise ValueError(
	f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
	f" {type(callback_steps)}."
	)

	def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
	shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
	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:
	rand_device = "cpu" if device.type == "mps" else device

	if isinstance(generator, list):
	shape = shape
	# shape = (1,) + shape[1:]
	latents = [
	torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
	for i in range(batch_size)
	]
	latents = torch.cat(latents, dim=0).to(device)
	else:
	latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
	else:
	if latents.shape != shape:
	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
	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

	@torch.no_grad()
	def __call__(
	self,
	prompt: Union[str, List[str]],
	video_length: Optional[int],
	height: Optional[int] = None,
	width: Optional[int] = None,
	num_inference_steps: int = 50,
	guidance_scale: float = 7.5,
	negative_prompt: Optional[Union[str, List[str]]] = None,
	num_videos_per_prompt: Optional[int] = 1,
	eta: float = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	latents: Optional[torch.FloatTensor] = None,
	output_type: Optional[str] = "tensor",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	callback_steps: Optional[int] = 1,
	**kwargs,
	):
	# 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

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

	# Define call parameters
	# batch_size = 1 if isinstance(prompt, str) else len(prompt)
	batch_size = 1
	if latents is not None:
	batch_size = latents.shape[0]
	if isinstance(prompt, list):
	batch_size = len(prompt)

	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 = guidance_scale > 1.0

	# Encode input prompt
	prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
	if negative_prompt is not None:
	negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size
	text_embeddings = self._encode_prompt(
	prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
	)

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

	# Prepare latent variables
	num_channels_latents = self.unet.in_channels
	latents = self.prepare_latents(
	batch_size * num_videos_per_prompt,
	num_channels_latents,
	video_length,
	height,
	width,
	text_embeddings.dtype,
	device,
	generator,
	latents,
	)
	latents_dtype = latents.dtype

	# Prepare extra step kwargs.
	extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

	# Denoising loop
	num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
	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 = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
	latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

	# predict the noise residual
	noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample.to(dtype=latents_dtype)
	# noise_pred = []
	# import pdb
	# pdb.set_trace()
	# for batch_idx in range(latent_model_input.shape[0]):
	# noise_pred_single = self.unet(latent_model_input[batch_idx:batch_idx+1], t, encoder_hidden_states=text_embeddings[batch_idx:batch_idx+1]).sample.to(dtype=latents_dtype)
	# noise_pred.append(noise_pred_single)
	# noise_pred = torch.cat(noise_pred)

	# perform guidance
	if do_classifier_free_guidance:
	noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
	noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

	# call the callback, if provided
	if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
	progress_bar.update()
	if callback is not None and i % callback_steps == 0:
	callback(i, t, latents)

	# Post-processing
	video = self.decode_latents(latents)

	# Convert to tensor
	if output_type == "tensor":
	video = torch.from_numpy(video)

	if not return_dict:
	return video

	return AnimationPipelineOutput(videos=video)