'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
import copy
import inspect
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from torch.nn.functional import grid_sample
from sapiens_transformers import CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...image_processor import VaeImageProcessor
from ...loaders import StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import USE_PEFT_BACKEND, BaseOutput, is_invisible_watermark_available, scale_lora_layers, unscale_lora_layers
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
if is_invisible_watermark_available(): from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
def rearrange_0(tensor, f):
    F, C, H, W = tensor.size()
    tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4))
    return tensor
def rearrange_1(tensor):
    B, C, F, H, W = tensor.size()
    return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W))
def rearrange_3(tensor, f):
    F, D, C = tensor.size()
    return torch.reshape(tensor, (F // f, f, D, C))
def rearrange_4(tensor):
    B, F, D, C = tensor.size()
    return torch.reshape(tensor, (B * F, D, C))
class CrossFrameAttnProcessor:
    """Args:"""
    def __init__(self, batch_size=2): self.batch_size = batch_size
    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        query = attn.to_q(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if encoder_hidden_states is None: encoder_hidden_states = hidden_states
        elif attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)
        if not is_cross_attention:
            video_length = key.size()[0] // self.batch_size
            first_frame_index = [0] * video_length
            key = rearrange_3(key, video_length)
            key = key[:, first_frame_index]
            value = rearrange_3(value, video_length)
            value = value[:, first_frame_index]
            key = rearrange_4(key)
            value = rearrange_4(value)
        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)
        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)
        hidden_states = attn.to_out[0](hidden_states)
        hidden_states = attn.to_out[1](hidden_states)
        return hidden_states
class CrossFrameAttnProcessor2_0:
    """Args:"""
    def __init__(self, batch_size=2):
        if not hasattr(F, 'scaled_dot_product_attention'): raise ImportError('AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.')
        self.batch_size = batch_size
    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
        batch_size, sequence_length, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        inner_dim = hidden_states.shape[-1]
        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
        query = attn.to_q(hidden_states)
        is_cross_attention = encoder_hidden_states is not None
        if encoder_hidden_states is None: encoder_hidden_states = hidden_states
        elif attn.norm_cross: encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)
        if not is_cross_attention:
            video_length = max(1, key.size()[0] // self.batch_size)
            first_frame_index = [0] * video_length
            key = rearrange_3(key, video_length)
            key = key[:, first_frame_index]
            value = rearrange_3(value, video_length)
            value = value[:, first_frame_index]
            key = rearrange_4(key)
            value = rearrange_4(value)
        head_dim = inner_dim // attn.heads
        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False)
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)
        hidden_states = attn.to_out[0](hidden_states)
        hidden_states = attn.to_out[1](hidden_states)
        return hidden_states
@dataclass
class TextToVideoSDXLPipelineOutput(BaseOutput):
    """Args:"""
    images: Union[List[PIL.Image.Image], np.ndarray]
def coords_grid(batch, ht, wd, device):
    coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))
    coords = torch.stack(coords[::-1], dim=0).float()
    return coords[None].repeat(batch, 1, 1, 1)
def warp_single_latent(latent, reference_flow):
    """Returns:"""
    _, _, H, W = reference_flow.size()
    _, _, h, w = latent.size()
    coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype)
    coords_t0 = coords0 + reference_flow
    coords_t0[:, 0] /= W
    coords_t0[:, 1] /= H
    coords_t0 = coords_t0 * 2.0 - 1.0
    coords_t0 = F.interpolate(coords_t0, size=(h, w), mode='bilinear')
    coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1))
    warped = grid_sample(latent, coords_t0, mode='nearest', padding_mode='reflection')
    return warped
def create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype):
    """Returns:"""
    seq_length = len(frame_ids)
    reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype)
    for fr_idx in range(seq_length):
        reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * frame_ids[fr_idx]
        reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * frame_ids[fr_idx]
    return reference_flow
def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents):
    """Returns:"""
    motion_field = create_motion_field(motion_field_strength_x=motion_field_strength_x, motion_field_strength_y=motion_field_strength_y,
    frame_ids=frame_ids, device=latents.device, dtype=latents.dtype)
    warped_latents = latents.clone().detach()
    for i in range(len(warped_latents)): warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None])
    return warped_latents
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """Returns:"""
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg
class TextToVideoZeroSDXLPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin):
    """Args:"""
    model_cpu_offload_seq = 'text_encoder->text_encoder_2->unet->vae'
    _optional_components = ['tokenizer', 'tokenizer_2', 'text_encoder', 'text_encoder_2', 'image_encoder', 'feature_extractor']
    def __init__(self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel,
    scheduler: KarrasDiffusionSchedulers, image_encoder: CLIPVisionModelWithProjection=None, feature_extractor: CLIPImageProcessor=None,
    force_zeros_for_empty_prompt: bool=True, add_watermarker: Optional[bool]=None):
        super().__init__()
        self.register_modules(vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, scheduler=scheduler,
        image_encoder=image_encoder, feature_extractor=feature_extractor)
        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.default_sample_size = self.unet.config.sample_size
        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
        if add_watermarker: self.watermark = StableDiffusionXLWatermarker()
        else: self.watermark = None
    def prepare_extra_step_kwargs(self, generator, eta):
        accepts_eta = 'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta: extra_step_kwargs['eta'] = eta
        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 upcast_vae(self):
        dtype = self.vae.dtype
        self.vae.to(dtype=torch.float32)
        use_torch_2_0_or_xformers = isinstance(self.vae.decoder.mid_block.attentions[0].processor, (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0))
        if use_torch_2_0_or_xformers:
            self.vae.post_quant_conv.to(dtype)
            self.vae.decoder.conv_in.to(dtype)
            self.vae.decoder.mid_block.to(dtype)
    def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None):
        add_time_ids = list(original_size + crops_coords_top_left + target_size)
        passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
        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)
        return add_time_ids
    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, int(height) // self.vae_scale_factor, int(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 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)
        latents = latents * self.scheduler.init_noise_sigma
        return latents
    def check_inputs(self, prompt, prompt_2, height, width, callback_steps, negative_prompt=None, negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None, pooled_prompt_embeds=None,
    negative_pooled_prompt_embeds=None, callback_on_step_end_tensor_inputs=None):
        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 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 {type(callback_steps)}.')
        if callback_on_step_end_tensor_inputs is not None and (not all((k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs))): raise ValueError(f'`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}')
        if prompt is not None and prompt_embeds is not None: raise ValueError(f'Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to only forward one of the two.')
        elif prompt_2 is not None and prompt_embeds is not None: raise ValueError(f'Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to only forward one of the two.')
        elif prompt is None and prompt_embeds is None: raise ValueError('Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.')
        elif prompt is not None and (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)}')
        elif prompt_2 is not None and (not isinstance(prompt_2, str) and (not isinstance(prompt_2, list))): raise ValueError(f'`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}')
        if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError(f'Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to only forward one of the two.')
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None: raise ValueError(f'Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to only forward one of the two.')
        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError(f'`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds` {negative_prompt_embeds.shape}.')
        if prompt_embeds is not None and pooled_prompt_embeds is None: raise ValueError('If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`.')
        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: raise ValueError('If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`.')
    def encode_prompt(self, prompt: str, prompt_2: Optional[str]=None, device: Optional[torch.device]=None, num_images_per_prompt: int=1, do_classifier_free_guidance: bool=True,
    negative_prompt: Optional[str]=None, negative_prompt_2: Optional[str]=None, prompt_embeds: Optional[torch.Tensor]=None, negative_prompt_embeds: Optional[torch.Tensor]=None,
    pooled_prompt_embeds: Optional[torch.Tensor]=None, negative_pooled_prompt_embeds: Optional[torch.Tensor]=None, lora_scale: Optional[float]=None, clip_skip: Optional[int]=None):
        """Args:"""
        device = device or self._execution_device
        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
            self._lora_scale = lora_scale
            if self.text_encoder is not None:
                if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
                else: scale_lora_layers(self.text_encoder, lora_scale)
            if self.text_encoder_2 is not None:
                if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
                else: scale_lora_layers(self.text_encoder_2, lora_scale)
        prompt = [prompt] if isinstance(prompt, str) else prompt
        if prompt is not None: batch_size = len(prompt)
        else: batch_size = prompt_embeds.shape[0]
        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
        text_encoders = [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
            prompt_embeds_list = []
            prompts = [prompt, prompt_2]
            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, tokenizer)
                text_inputs = tokenizer(prompt, padding='max_length', max_length=tokenizer.model_max_length, truncation=True, return_tensors='pt')
                text_input_ids = text_inputs.input_ids
                untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1:-1])
                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
                pooled_prompt_embeds = prompt_embeds[0]
                if clip_skip is None: prompt_embeds = prompt_embeds.hidden_states[-2]
                else: prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
                prompt_embeds_list.append(prompt_embeds)
            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
        elif do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ''
            negative_prompt_2 = negative_prompt_2 or negative_prompt
            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
            negative_prompt_2 = batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
            uncond_tokens: List[str]
            if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError(f'`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} != {type(prompt)}.')
            elif batch_size != len(negative_prompt): raise ValueError(f'`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`: {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches the batch size of `prompt`.')
            else: uncond_tokens = [negative_prompt, negative_prompt_2]
            negative_prompt_embeds_list = []
            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin): negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
                max_length = prompt_embeds.shape[1]
                uncond_input = tokenizer(negative_prompt, padding='max_length', max_length=max_length, truncation=True, return_tensors='pt')
                negative_prompt_embeds = text_encoder(uncond_input.input_ids.to(device), output_hidden_states=True)
                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
                negative_prompt_embeds_list.append(negative_prompt_embeds)
            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
        if self.text_encoder_2 is not None: prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
        else: prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
        bs_embed, seq_len, _ = prompt_embeds.shape
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
        if do_classifier_free_guidance:
            seq_len = negative_prompt_embeds.shape[1]
            if self.text_encoder_2 is not None: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
            else: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(bs_embed * num_images_per_prompt, -1)
        if do_classifier_free_guidance: negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(bs_embed * num_images_per_prompt, -1)
        if self.text_encoder is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: unscale_lora_layers(self.text_encoder, lora_scale)
        if self.text_encoder_2 is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: unscale_lora_layers(self.text_encoder_2, lora_scale)
        return (prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds)
    def forward_loop(self, x_t0, t0, t1, generator):
        """Returns:"""
        eps = randn_tensor(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device)
        alpha_vec = torch.prod(self.scheduler.alphas[t0:t1])
        x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.sqrt(1 - alpha_vec) * eps
        return x_t1
    def backward_loop(self, latents, timesteps, prompt_embeds, guidance_scale, callback, callback_steps, num_warmup_steps, extra_step_kwargs, add_text_embeds, add_time_ids,
    cross_attention_kwargs=None, guidance_rescale: float=0.0):
        """Returns:"""
        do_classifier_free_guidance = guidance_scale > 1.0
        num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order
        with self.progress_bar(total=num_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                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)
                added_cond_kwargs = {'text_embeds': add_text_embeds, 'time_ids': add_time_ids}
                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=cross_attention_kwargs,
                added_cond_kwargs=added_cond_kwargs, return_dict=False)[0]
                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)
                if do_classifier_free_guidance and guidance_rescale > 0.0: noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
                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)
        return latents.clone().detach()
    @torch.no_grad()
    def __call__(self, prompt: Union[str, List[str]], prompt_2: Optional[Union[str, List[str]]]=None, video_length: Optional[int]=8, height: Optional[int]=None,
    width: Optional[int]=None, num_inference_steps: int=50, denoising_end: Optional[float]=None, guidance_scale: float=7.5, negative_prompt: Optional[Union[str,
    List[str]]]=None, negative_prompt_2: 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, frame_ids: Optional[List[int]]=None, prompt_embeds: Optional[torch.Tensor]=None,
    negative_prompt_embeds: Optional[torch.Tensor]=None, pooled_prompt_embeds: Optional[torch.Tensor]=None, negative_pooled_prompt_embeds: Optional[torch.Tensor]=None,
    latents: Optional[torch.Tensor]=None, motion_field_strength_x: float=12, motion_field_strength_y: float=12, output_type: Optional[str]='tensor', return_dict: bool=True,
    callback: Optional[Callable[[int, int, torch.Tensor], None]]=None, callback_steps: int=1, cross_attention_kwargs: Optional[Dict[str, Any]]=None,
    guidance_rescale: float=0.0, original_size: Optional[Tuple[int, int]]=None, crops_coords_top_left: Tuple[int, int]=(0, 0),
    target_size: Optional[Tuple[int, int]]=None, t0: int=44, t1: int=47):
        """Returns:"""
        assert video_length > 0
        if frame_ids is None: frame_ids = list(range(video_length))
        assert len(frame_ids) == video_length
        assert num_videos_per_prompt == 1
        original_attn_proc = self.unet.attn_processors
        processor = CrossFrameAttnProcessor2_0(batch_size=2) if hasattr(F, 'scaled_dot_product_attention') else CrossFrameAttnProcessor(batch_size=2)
        self.unet.set_attn_processor(processor)
        if isinstance(prompt, str): prompt = [prompt]
        if isinstance(negative_prompt, str): negative_prompt = [negative_prompt]
        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor
        original_size = original_size or (height, width)
        target_size = target_size or (height, width)
        self.check_inputs(prompt, prompt_2, height, width, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds,
        negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds)
        batch_size = 1 if isinstance(prompt, str) else len(prompt) if isinstance(prompt, list) else prompt_embeds.shape[0]
        device = self._execution_device
        do_classifier_free_guidance = guidance_scale > 1.0
        text_encoder_lora_scale = cross_attention_kwargs.get('scale', None) if cross_attention_kwargs is not None else None
        prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = self.encode_prompt(prompt=prompt, prompt_2=prompt_2, device=device,
        num_images_per_prompt=num_videos_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2,
        prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, lora_scale=text_encoder_lora_scale)
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(batch_size * num_videos_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents)
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        add_text_embeds = pooled_prompt_embeds
        if self.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
        else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
        add_time_ids = self._get_add_time_ids(original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim)
        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
        prompt_embeds = prompt_embeds.to(device)
        add_text_embeds = add_text_embeds.to(device)
        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_videos_per_prompt, 1)
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        x_1_t1 = self.backward_loop(timesteps=timesteps[:-t1 - 1], prompt_embeds=prompt_embeds, latents=latents, guidance_scale=guidance_scale, callback=callback,
        callback_steps=callback_steps, extra_step_kwargs=extra_step_kwargs, num_warmup_steps=num_warmup_steps, add_text_embeds=add_text_embeds, add_time_ids=add_time_ids)
        scheduler_copy = copy.deepcopy(self.scheduler)
        x_1_t0 = self.backward_loop(timesteps=timesteps[-t1 - 1:-t0 - 1], prompt_embeds=prompt_embeds, latents=x_1_t1, guidance_scale=guidance_scale, callback=callback,
        callback_steps=callback_steps, extra_step_kwargs=extra_step_kwargs, num_warmup_steps=0, add_text_embeds=add_text_embeds, add_time_ids=add_time_ids)
        x_2k_t0 = x_1_t0.repeat(video_length - 1, 1, 1, 1)
        x_2k_t0 = create_motion_field_and_warp_latents(motion_field_strength_x=motion_field_strength_x, motion_field_strength_y=motion_field_strength_y, latents=x_2k_t0, frame_ids=frame_ids[1:])
        x_2k_t1 = self.forward_loop(x_t0=x_2k_t0, t0=timesteps[-t0 - 1].to(torch.long), t1=timesteps[-t1 - 1].to(torch.long), generator=generator)
        latents = torch.cat([x_1_t1, x_2k_t1])
        self.scheduler = scheduler_copy
        timesteps = timesteps[-t1 - 1:]
        b, l, d = prompt_embeds.size()
        prompt_embeds = prompt_embeds[:, None].repeat(1, video_length, 1, 1).reshape(b * video_length, l, d)
        b, k = add_text_embeds.size()
        add_text_embeds = add_text_embeds[:, None].repeat(1, video_length, 1).reshape(b * video_length, k)
        b, k = add_time_ids.size()
        add_time_ids = add_time_ids[:, None].repeat(1, video_length, 1).reshape(b * video_length, k)
        if denoising_end is not None and isinstance(denoising_end, float) and (denoising_end > 0) and (denoising_end < 1):
            discrete_timestep_cutoff = int(round(self.scheduler.config.num_train_timesteps - denoising_end * self.scheduler.config.num_train_timesteps))
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]
        x_1k_0 = self.backward_loop(timesteps=timesteps, prompt_embeds=prompt_embeds, latents=latents, guidance_scale=guidance_scale, callback=callback, callback_steps=callback_steps,
        extra_step_kwargs=extra_step_kwargs, num_warmup_steps=0, add_text_embeds=add_text_embeds, add_time_ids=add_time_ids)
        latents = x_1k_0
        if not output_type == 'latent':
            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
            if needs_upcasting:
                self.upcast_vae()
                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            if needs_upcasting: self.vae.to(dtype=torch.float16)
        else:
            image = latents
            return TextToVideoSDXLPipelineOutput(images=image)
        if self.watermark is not None: image = self.watermark.apply_watermark(image)
        image = self.image_processor.postprocess(image, output_type=output_type)
        self.maybe_free_model_hooks()
        self.unet.set_attn_processor(original_attn_proc)
        if not return_dict: return (image,)
        return TextToVideoSDXLPipelineOutput(images=image)
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
