





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 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	['''vqvae''']




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						)		->							Any:
										super().__init__()
										self.register_modules(unet=lowerCAmelCase_				,						scheduler=lowerCAmelCase_				,						mel=lowerCAmelCase_				,						vqvae=lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							int:
										return 50 if isinstance(self.scheduler				,						lowerCAmelCase_  ) else 1000




					@torch.no_grad()
					def __call__(		self				,						lowerCAmelCase_ = 1				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_=True				,						)		->							Union[
					    Union[AudioPipelineOutput, ImagePipelineOutput],
					    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
					]:
										_snake_case									=				steps or self.get_default_steps()
										self.scheduler.set_timesteps(lowerCAmelCase_  )
										_snake_case									=				step_generator or generator
										# For backwards compatibility
										if type(self.unet.config.sample_size  ) == int:
															_snake_case									=				(self.unet.config.sample_size, self.unet.config.sample_size)
										if noise is None:
															_snake_case									=				randn_tensor(
															    (
															        batch_size,
															        self.unet.config.in_channels,
															        self.unet.config.sample_size[0],
															        self.unet.config.sample_size[1],
															    )				,						generator=lowerCAmelCase_				,						device=self.device				,						)
										_snake_case									=				noise
										_snake_case									=				None

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

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

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

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

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

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

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

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

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

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

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




					@torch.no_grad()
					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = 50  )		->							np.ndarray:
										assert isinstance(self.scheduler				,						lowerCAmelCase_  )
										self.scheduler.set_timesteps(lowerCAmelCase_  )
										_snake_case									=				np.array(
										    [np.frombuffer(image.tobytes()				,						dtype='uint8'  ).reshape((1, image.height, image.width)  ) for image in images]  )
										_snake_case									=				(sample / 255) * 2 - 1
										_snake_case									=				torch.Tensor(lowerCAmelCase_  ).to(self.device  )

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

										return sample






					@staticmethod
					def 	lowerCAmelCase			(		lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							torch.Tensor:
										_snake_case									=				acos(torch.dot(torch.flatten(lowerCAmelCase_  )				,						torch.flatten(lowerCAmelCase_  )  ) / torch.norm(lowerCAmelCase_  ) / torch.norm(lowerCAmelCase_  )  )
										return sin((1 - alpha) * theta  ) * xa / sin(lowerCAmelCase_  ) + sin(alpha * theta  ) * xa / sin(lowerCAmelCase_  )










import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


UpperCAmelCase_      							=     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_  )		->							int:
										super().__init__()
										_snake_case									=				torchvision.models.resnetaaa(pretrained=lowerCAmelCase_  )
										_snake_case									=				list(model.children()  )[:-2]
										_snake_case									=				nn.Sequential(*lowerCAmelCase_  )
										_snake_case									=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							str:
										# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
										_snake_case									=				self.pool(self.model(lowerCAmelCase_  )  )
										_snake_case									=				torch.flatten(lowerCAmelCase_				,						start_dim=2  )
										_snake_case									=				out.transpose(1				,						2  ).contiguous()
										return out  # BxNx2048





class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				[json.loads(lowerCAmelCase_  ) for l in open(lowerCAmelCase_  )]
										_snake_case									=				os.path.dirname(lowerCAmelCase_  )
										_snake_case									=				tokenizer
										_snake_case									=				labels
										_snake_case									=				len(lowerCAmelCase_  )
										_snake_case									=				max_seq_length

										_snake_case									=				transforms




					def __len__(		self  )		->							Any:
										return len(self.data  )




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']				,						add_special_tokens=lowerCAmelCase_  )  )
										_snake_case							,     _snake_case							,     _snake_case									=				sentence[0], sentence[1:-1], sentence[-1]
										_snake_case									=				sentence[: self.max_seq_length]

										_snake_case									=				torch.zeros(self.n_classes  )
										_snake_case									=				1

										_snake_case									=				Image.open(os.path.join(self.data_dir				,						self.data[index]['img']  )  ).convert('RGB'  )
										_snake_case									=				self.transforms(lowerCAmelCase_  )

										return {
										    "image_start_token": start_token,
										    "image_end_token": end_token,
										    "sentence": sentence,
										    "image": image,
										    "label": label,
										}






					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				Counter()
										for row in self.data:
															label_freqs.update(row['label']  )
										return label_freqs





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Dict:



					'''simple docstring'''




					_snake_case									=				[len(row['sentence']						) for row in batch]
					_snake_case							,     _snake_case									=				len(UpperCamelCase__						), max(UpperCamelCase__						)

					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)
					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)

					for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__       ,    UpperCamelCase__						)						):
										_snake_case									=				input_row['sentence']
										_snake_case									=				1

					_snake_case									=				torch.stack([row['image'] for row in batch]						)
					_snake_case									=				torch.stack([row['label'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_start_token'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_end_token'] for row in batch]						)

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 						lowerCamelCase__     (     )			-> str:



					'''simple docstring'''




					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]





def 						lowerCamelCase__     (     )			-> Tuple:



					'''simple docstring'''




					return transforms.Compose(
					    [
					        transforms.Resize(256						),
					        transforms.CenterCrop(224						),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]       ,    std=[0.1222_1994, 0.1214_5835, 0.1438_0469]       ,    ),
					    ]						)



