
"""simple docstring"""







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__	(     _lowerCamelCase):
		A_						:	Dict           =     ['vqvae']

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

		def 							__lowerCamelCase							(				self			):
			return 50 if isinstance(self.scheduler	,		_SCREAMING_SNAKE_CASE			) else 10_00

		@torch.no_grad()
		def __call__(				self	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 0	,		_SCREAMING_SNAKE_CASE = 0	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 0	,		_SCREAMING_SNAKE_CASE = 0	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = 0	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE=True	,		):
			__lowerCAmelCase      :						Dict						      =				steps or self.get_default_steps()
			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				step_generator or generator
			# For backwards compatibility
			if type(self.unet.config.sample_size			) == int:
				__lowerCAmelCase      :						Dict						      =				(self.unet.config.sample_size, self.unet.config.sample_size)
			if noise is None:
				__lowerCAmelCase      :						Optional[int]						      =				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	,		)
			__lowerCAmelCase      :						str						      =				noise
			__lowerCAmelCase      :						Optional[Any]						      =				None

			if audio_file is not None or raw_audio is not None:
				self.mel.load_audio(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[str]						      =				self.mel.audio_slice_to_image(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[str]						      =				np.frombuffer(input_image.tobytes()	,		dtype='uint8'			).reshape(
				    (input_image.height, input_image.width)			)
				__lowerCAmelCase      :						int						      =				(input_image / 2_55) * 2 - 1
				__lowerCAmelCase      :						List[Any]						      =				torch.tensor(input_image[np.newaxis, :, :]	,		dtype=torch.float			).to(self.device			)

				if self.vqvae is not None:
					__lowerCAmelCase      :						int						      =				self.vqvae.encode(torch.unsqueeze(_SCREAMING_SNAKE_CASE	,		0			)			).latent_dist.sample(
					    generator=_SCREAMING_SNAKE_CASE			)[0]
					__lowerCAmelCase      :						Optional[Any]						      =				self.vqvae.config.scaling_factor * input_images

				if start_step > 0:
					__lowerCAmelCase      :						Union[str, Any]						      =				self.scheduler.add_noise(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		self.scheduler.timesteps[start_step - 1]			)

				__lowerCAmelCase      :						List[Any]						      =				(
				    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
				)
				__lowerCAmelCase      :						str						      =				int(mask_start_secs * pixels_per_second			)
				__lowerCAmelCase      :						Union[str, Any]						      =				int(mask_end_secs * pixels_per_second			)
				__lowerCAmelCase      :						int						      =				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			):
					__lowerCAmelCase      :						str						      =				self.unet(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)['sample']
				else:
					__lowerCAmelCase      :						Any						      =				self.unet(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)['sample']

				if isinstance(self.scheduler	,		_SCREAMING_SNAKE_CASE			):
					__lowerCAmelCase      :						Dict						      =				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:
					__lowerCAmelCase      :						Union[str, Any]						      =				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:
						__lowerCAmelCase      :						Tuple						      =				mask[:, step, :, :mask_start]
					if mask_end > 0:
						__lowerCAmelCase      :						List[Any]						      =				mask[:, step, :, -mask_end:]

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

			__lowerCAmelCase      :						str						      =				(images / 2 + 0.5).clamp(0	,		1			)
			__lowerCAmelCase      :						Union[str, Any]						      =				images.cpu().permute(0	,		2	,		3	,		1			).numpy()
			__lowerCAmelCase      :						int						      =				(images * 2_55).round().astype('uint8'			)
			__lowerCAmelCase      :						int						      =				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)			)

			__lowerCAmelCase      :						Union[str, Any]						      =				[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	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 50			):
			assert isinstance(self.scheduler	,		_SCREAMING_SNAKE_CASE			)
			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				np.array(
			    [np.frombuffer(image.tobytes()	,		dtype='uint8'			).reshape((1, image.height, image.width)			) for image in images]			)
			__lowerCAmelCase      :						Optional[Any]						      =				(sample / 2_55) * 2 - 1
			__lowerCAmelCase      :						int						      =				torch.Tensor(_SCREAMING_SNAKE_CASE			).to(self.device			)

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

			return sample







		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[Any]						      =				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			)

"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate




@dataclass(frozen=_lowerCamelCase)
class 			A__	(     _lowerCamelCase):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		A_						:	str           =     field(default='text-classification'	,					metadata={'include_in_asdict_even_if_is_default': True})
		A_						:	ClassVar[Features]           =     Features({'text': Value('string')})
		A_						:	ClassVar[Features]           =     Features({'labels': ClassLabel})
		A_						:	str           =     "text"
		A_						:	str           =     "labels"

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.label_column not in features:
				raise ValueError(f"Column {self.label_column} is not present in features."			)
			if not isinstance(features[self.label_column]	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
			__lowerCAmelCase      :						Any						      =				copy.deepcopy(self			)
			__lowerCAmelCase      :						Dict						      =				self.label_schema.copy()
			__lowerCAmelCase      :						List[Any]						      =				features[self.label_column]
			__lowerCAmelCase      :						Dict						      =				label_schema
			return task_template







		@property
		def 							__lowerCamelCase							(				self			):
			return {
			    self.text_column: "text",
			    self.label_column: "labels",
			}