


from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			['''vqvae''']

							def __init__(							self:     Any   ,						UpperCamelCase_:     AutoencoderKL   ,						UpperCamelCase_:     UNetaDConditionModel   ,						UpperCamelCase_:     Mel   ,						UpperCamelCase_:     Union[DDIMScheduler, DDPMScheduler]   ,						)      ->       Optional[Any]:





									"""simple docstring"""







									super().__init__()
									self.register_modules(unet=UpperCamelCase_   ,						scheduler=UpperCamelCase_   ,						mel=UpperCamelCase_   ,						vqvae=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int							)      ->       int:





									"""simple docstring"""







									return 50 if isinstance(self.scheduler   ,						UpperCamelCase_							) else 1_000

							@torch.no_grad()
							def __call__(							self:     List[str]   ,						UpperCamelCase_:     int = 1   ,						UpperCamelCase_:     str = None   ,						UpperCamelCase_:     np.ndarray = None   ,						UpperCamelCase_:     int = 0   ,						UpperCamelCase_:     int = 0   ,						UpperCamelCase_:     int = None   ,						UpperCamelCase_:     torch.Generator = None   ,						UpperCamelCase_:     float = 0   ,						UpperCamelCase_:     float = 0   ,						UpperCamelCase_:     torch.Generator = None   ,						UpperCamelCase_:     float = 0   ,						UpperCamelCase_:     torch.Tensor = None   ,						UpperCamelCase_:     torch.Tensor = None   ,						UpperCamelCase_:     Any=True   ,						)      ->       Union[
							    Union[AudioPipelineOutput, ImagePipelineOutput],
							    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
							]:





									"""simple docstring"""







									lowercase__						=				steps or self.get_default_steps()
									self.scheduler.set_timesteps(UpperCamelCase_							)
									lowercase__						=				step_generator or generator
									# For backwards compatibility
									if type(self.unet.config.sample_size							) == int:
											lowercase__						=				(self.unet.config.sample_size, self.unet.config.sample_size)
									if noise is None:
											lowercase__						=				randn_tensor(
											    (
											        batch_size,
											        self.unet.config.in_channels,
											        self.unet.config.sample_size[0],
											        self.unet.config.sample_size[1],
											    )   ,						generator=UpperCamelCase_   ,						device=self.device   ,						)
									lowercase__						=				noise
									lowercase__						=				None

									if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(UpperCamelCase_   ,						UpperCamelCase_							)
											lowercase__						=				self.mel.audio_slice_to_image(UpperCamelCase_							)
											lowercase__						=				np.frombuffer(input_image.tobytes()   ,						dtype='''uint8'''							).reshape(
											    (input_image.height, input_image.width)							)
											lowercase__						=				(input_image / 255) * 2 - 1
											lowercase__						=				torch.tensor(input_image[np.newaxis, :, :]   ,						dtype=torch.float							).to(self.device							)

											if self.vqvae is not None:
													lowercase__						=				self.vqvae.encode(torch.unsqueeze(UpperCamelCase_   ,						0							)							).latent_dist.sample(
													    generator=UpperCamelCase_							)[0]
													lowercase__						=				self.vqvae.config.scaling_factor * input_images

											if start_step > 0:
													lowercase__						=				self.scheduler.add_noise(UpperCamelCase_   ,						UpperCamelCase_   ,						self.scheduler.timesteps[start_step - 1]							)

											lowercase__						=				(
											    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
											)
											lowercase__						=				int(mask_start_secs * pixels_per_second							)
											lowercase__						=				int(mask_end_secs * pixels_per_second							)
											lowercase__						=				self.scheduler.add_noise(UpperCamelCase_   ,						UpperCamelCase_   ,						torch.tensor(self.scheduler.timesteps[start_step:]							)							)

									for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
											if isinstance(self.unet   ,						UpperCamelCase_							):
													lowercase__						=				self.unet(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)['''sample''']
											else:
													lowercase__						=				self.unet(UpperCamelCase_   ,						UpperCamelCase_							)['''sample''']

											if isinstance(self.scheduler   ,						UpperCamelCase_							):
													lowercase__						=				self.scheduler.step(
													    model_output=UpperCamelCase_   ,						timestep=UpperCamelCase_   ,						sample=UpperCamelCase_   ,						eta=UpperCamelCase_   ,						generator=UpperCamelCase_   ,						)['''prev_sample''']
											else:
													lowercase__						=				self.scheduler.step(
													    model_output=UpperCamelCase_   ,						timestep=UpperCamelCase_   ,						sample=UpperCamelCase_   ,						generator=UpperCamelCase_   ,						)['''prev_sample''']

											if mask is not None:
													if mask_start > 0:
															lowercase__						=				mask[:, step, :, :mask_start]
													if mask_end > 0:
															lowercase__						=				mask[:, step, :, -mask_end:]

									if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											lowercase__						=				1 / self.vqvae.config.scaling_factor * images
											lowercase__						=				self.vqvae.decode(UpperCamelCase_							)['''sample''']

									lowercase__						=				(images / 2 + 0.5).clamp(0   ,						1							)
									lowercase__						=				images.cpu().permute(0   ,						2   ,						3   ,						1							).numpy()
									lowercase__						=				(images * 255).round().astype('''uint8'''							)
									lowercase__						=				list(
									    (Image.fromarray(_[:, :, 0]							) for _ in images)
									    if images.shape[3] == 1
									    else (Image.fromarray(UpperCamelCase_   ,						mode='''RGB'''							).convert('''L'''							) for _ in images)							)

									lowercase__						=				[self.mel.image_to_audio(UpperCamelCase_							) for _ in images]
									if not return_dict:
											return images, (self.mel.get_sample_rate(), audios)

									return BaseOutput(**AudioPipelineOutput(np.array(UpperCamelCase_							)[:, np.newaxis, :]							)   ,						**ImagePipelineOutput(UpperCamelCase_							)							)

							@torch.no_grad()
							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     List[Image.Image]   ,						UpperCamelCase_:     int = 50							)      ->       np.ndarray:





									"""simple docstring"""







									assert isinstance(self.scheduler   ,						UpperCamelCase_							)
									self.scheduler.set_timesteps(UpperCamelCase_							)
									lowercase__						=				np.array(
									    [np.frombuffer(image.tobytes()   ,						dtype='''uint8'''							).reshape((1, image.height, image.width)							) for image in images]							)
									lowercase__						=				(sample / 255) * 2 - 1
									lowercase__						=				torch.Tensor(UpperCamelCase_							).to(self.device							)

									for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,						(0,)							)							):
											lowercase__						=				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
											lowercase__						=				self.scheduler.alphas_cumprod[t]
											lowercase__						=				(
											    self.scheduler.alphas_cumprod[prev_timestep]
											    if prev_timestep >= 0
											    else self.scheduler.final_alpha_cumprod
											)
											lowercase__						=				1 - alpha_prod_t
											lowercase__						=				self.unet(UpperCamelCase_   ,						UpperCamelCase_							)['''sample''']
											lowercase__						=				(1 - alpha_prod_t_prev) ** 0.5 * model_output
											lowercase__						=				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
											lowercase__						=				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

									return sample


							@staticmethod
							def 				lowerCamelCase_			(							UpperCamelCase_:     torch.Tensor   ,						UpperCamelCase_:     torch.Tensor   ,						UpperCamelCase_:     float							)      ->       torch.Tensor:





									"""simple docstring"""







									lowercase__						=				acos(torch.dot(torch.flatten(UpperCamelCase_							)   ,						torch.flatten(UpperCamelCase_							)							) / torch.norm(UpperCamelCase_							) / torch.norm(UpperCamelCase_							)							)
									return sin((1 - alpha) * theta							) * xa / sin(UpperCamelCase_							) + sin(alpha * theta							) * xa / sin(UpperCamelCase_							)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			'''lxmert'''
							_lowercase     :      Any  				=			{}

							def __init__(							self:     Any   ,						UpperCamelCase_:     List[Any]=30_522   ,						UpperCamelCase_:     int=768   ,						UpperCamelCase_:     Optional[Any]=12   ,						UpperCamelCase_:     Dict=9_500   ,						UpperCamelCase_:     List[Any]=1_600   ,						UpperCamelCase_:     List[Any]=400   ,						UpperCamelCase_:     List[str]=3_072   ,						UpperCamelCase_:     int="gelu"   ,						UpperCamelCase_:     Any=0.1   ,						UpperCamelCase_:     int=0.1   ,						UpperCamelCase_:     List[str]=512   ,						UpperCamelCase_:     Optional[int]=2   ,						UpperCamelCase_:     Union[str, Any]=0.02   ,						UpperCamelCase_:     Dict=1E-1_2   ,						UpperCamelCase_:     List[Any]=9   ,						UpperCamelCase_:     List[Any]=5   ,						UpperCamelCase_:     Optional[Any]=5   ,						UpperCamelCase_:     str=2_048   ,						UpperCamelCase_:     Dict=4   ,						UpperCamelCase_:     Any=6.67   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     List[str]=True   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     Any=True   ,						UpperCamelCase_:     Tuple=True   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     Any=True   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				type_vocab_size
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				num_qa_labels
									lowercase__						=				num_object_labels
									lowercase__						=				num_attr_labels
									lowercase__						=				l_layers
									lowercase__						=				x_layers
									lowercase__						=				r_layers
									lowercase__						=				visual_feat_dim
									lowercase__						=				visual_pos_dim
									lowercase__						=				visual_loss_normalizer
									lowercase__						=				task_matched
									lowercase__						=				task_mask_lm
									lowercase__						=				task_obj_predict
									lowercase__						=				task_qa
									lowercase__						=				visual_obj_loss
									lowercase__						=				visual_attr_loss
									lowercase__						=				visual_feat_loss
									lowercase__						=				{'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
									super().__init__(**UpperCamelCase_							)


