


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     :      Any  				=			['vqvae']

							def __init__(							self:     Union[str, Any]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     str   ,						)      ->       Any:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







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

							@torch.no_grad()
							def __call__(							self:     Union[str, Any]   ,						UpperCamelCase_:     int = 1   ,						UpperCamelCase_:     List[str] = None   ,						UpperCamelCase_:     Dict = None   ,						UpperCamelCase_:     Optional[Any] = 0   ,						UpperCamelCase_:     Optional[Any] = 0   ,						UpperCamelCase_:     Optional[int] = None   ,						UpperCamelCase_:     Any = None   ,						UpperCamelCase_:     Optional[int] = 0   ,						UpperCamelCase_:     Any = 0   ,						UpperCamelCase_:     Optional[Any] = None   ,						UpperCamelCase_:     Dict = 0   ,						UpperCamelCase_:     List[str] = None   ,						UpperCamelCase_:     Any = None   ,						UpperCamelCase_:     int=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(_SCREAMING_SNAKE_CASE							)
									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=_SCREAMING_SNAKE_CASE   ,						device=self.device   ,						)
									lowercase__						=				noise
									lowercase__						=				None

									if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							)
											lowercase__						=				self.mel.audio_slice_to_image(_SCREAMING_SNAKE_CASE							)
											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(_SCREAMING_SNAKE_CASE   ,						0							)							).latent_dist.sample(
													    generator=_SCREAMING_SNAKE_CASE							)[0]
													lowercase__						=				self.vqvae.config.scaling_factor * input_images

											if start_step > 0:
													lowercase__						=				self.scheduler.add_noise(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						torch.tensor(self.scheduler.timesteps[start_step:]							)							)

									for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
											if isinstance(self.unet   ,						_SCREAMING_SNAKE_CASE							):
													lowercase__						=				self.unet(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							)["""sample"""]
											else:
													lowercase__						=				self.unet(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							)["""sample"""]

											if isinstance(self.scheduler   ,						_SCREAMING_SNAKE_CASE							):
													lowercase__						=				self.scheduler.step(
													    model_output=_SCREAMING_SNAKE_CASE   ,						timestep=_SCREAMING_SNAKE_CASE   ,						sample=_SCREAMING_SNAKE_CASE   ,						eta=_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						)["""prev_sample"""]
											else:
													lowercase__						=				self.scheduler.step(
													    model_output=_SCREAMING_SNAKE_CASE   ,						timestep=_SCREAMING_SNAKE_CASE   ,						sample=_SCREAMING_SNAKE_CASE   ,						generator=_SCREAMING_SNAKE_CASE   ,						)["""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(_SCREAMING_SNAKE_CASE							)["""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(_SCREAMING_SNAKE_CASE   ,						mode='''RGB'''							).convert('''L'''							) for _ in images)							)

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

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

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





									"""simple docstring"""







									assert isinstance(self.scheduler   ,						_SCREAMING_SNAKE_CASE							)
									self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE							)
									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(_SCREAMING_SNAKE_CASE							).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(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							)["""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_:     List[Any]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Any							)      ->       torch.Tensor:





									"""simple docstring"""







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






import argparse
from collections import defaultdict

import yaml


lowerCAmelCase	=							'docs/source/en/_toctree.yml'
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				defaultdict(SCREAMING_SNAKE_CASE					)
		lowercase__						=				[]
		lowercase__						=				[]
		for doc in doc_list:
				if "local" in doc:
						counts[doc["local"]] += 1

				if doc["title"].lower() == "overview":
						overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']}					)
				else:
						new_doc_list.append(SCREAMING_SNAKE_CASE					)

		lowercase__						=				new_doc_list
		lowercase__						=				[key for key, value in counts.items() if value > 1]

		lowercase__						=				[]
		for duplicate_key in duplicates:
				lowercase__						=				list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key}					)
				if len(SCREAMING_SNAKE_CASE					) > 1:
						raise ValueError(
						    f'{duplicate_key} is present several times in the documentation table of content at '
						    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
						    '''others.'''					)
				# Only add this once
				new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}					)

		# Add none duplicate-keys
		new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1]					)
		lowercase__						=				sorted(SCREAMING_SNAKE_CASE						,    key=lambda SCREAMING_SNAKE_CASE					: s["title"].lower()					)

		# "overview" gets special treatment and is always first
		if len(SCREAMING_SNAKE_CASE					) > 1:
				raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.'''					)

		overview_doc.extend(SCREAMING_SNAKE_CASE					)

		# Sort
		return overview_doc
def       _a			(				SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		with open(SCREAMING_SNAKE_CASE						,    encoding='''utf-8'''					) as f:
				lowercase__						=				yaml.safe_load(f.read()					)

		# Get to the API doc
		lowercase__						=				0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		lowercase__						=				content[api_idx]['''sections''']

		# Then to the model doc
		lowercase__						=				0
		while api_doc[scheduler_idx]["title"] != "Schedulers":
				scheduler_idx += 1

		lowercase__						=				api_doc[scheduler_idx]['''sections''']
		lowercase__						=				clean_doc_toc(SCREAMING_SNAKE_CASE					)

		lowercase__						=				False
		if new_scheduler_doc != scheduler_doc:
				lowercase__						=				True
				if overwrite:
						lowercase__						=				new_scheduler_doc

		if diff:
				if overwrite:
						lowercase__						=				api_doc
						with open(SCREAMING_SNAKE_CASE						,    '''w'''						,    encoding='''utf-8'''					) as f:
								f.write(yaml.dump(SCREAMING_SNAKE_CASE						,    allow_unicode=SCREAMING_SNAKE_CASE					)					)
				else:
						raise ValueError(
						    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)
def       _a			(				SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		with open(SCREAMING_SNAKE_CASE						,    encoding='''utf-8'''					) as f:
				lowercase__						=				yaml.safe_load(f.read()					)

		# Get to the API doc
		lowercase__						=				0
		while content[api_idx]["title"] != "API":
				api_idx += 1
		lowercase__						=				content[api_idx]['''sections''']

		# Then to the model doc
		lowercase__						=				0
		while api_doc[pipeline_idx]["title"] != "Pipelines":
				pipeline_idx += 1

		lowercase__						=				False
		lowercase__						=				api_doc[pipeline_idx]['''sections''']
		lowercase__						=				[]

		# sort sub pipeline docs
		for pipeline_doc in pipeline_docs:
				if "section" in pipeline_doc:
						lowercase__						=				pipeline_doc['''section''']
						lowercase__						=				clean_doc_toc(SCREAMING_SNAKE_CASE					)
						if overwrite:
								lowercase__						=				new_sub_pipeline_doc
				new_pipeline_docs.append(SCREAMING_SNAKE_CASE					)

		# sort overall pipeline doc
		lowercase__						=				clean_doc_toc(SCREAMING_SNAKE_CASE					)

		if new_pipeline_docs != pipeline_docs:
				lowercase__						=				True
				if overwrite:
						lowercase__						=				new_pipeline_docs

		if diff:
				if overwrite:
						lowercase__						=				api_doc
						with open(SCREAMING_SNAKE_CASE						,    '''w'''						,    encoding='''utf-8'''					) as f:
								f.write(yaml.dump(SCREAMING_SNAKE_CASE						,    allow_unicode=SCREAMING_SNAKE_CASE					)					)
				else:
						raise ValueError(
						    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''					)


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()
			parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
			lowerCAmelCase	=							parser.parse_args()

			check_scheduler_doc(args.fix_and_overwrite)
			check_pipeline_doc(args.fix_and_overwrite)


