

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							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=['vqvae']
			def __init__(	self							:       Dict					,					a							:       AutoencoderKL					,					a							:       UNetaDConditionModel					,					a							:       Mel					,					a							:       Union[DDIMScheduler, DDPMScheduler]					,					)							->				str:

				"""simple docstring"""

				super().__init__()
				self.register_modules(unet=a					,					scheduler=a					,					mel=a					,					vqvae=a		)
			def 							__UpperCamelCase       (	self							:       str		)							->				int:

				"""simple docstring"""

				return 50 if isinstance(self.scheduler					,					a		) else 1000
			@torch.no_grad()
			def __call__(	self							:       str					,					a							:       int = 1					,					a							:       str = None					,					a							:       np.ndarray = None					,					a							:       int = 0					,					a							:       int = 0					,					a							:       int = None					,					a							:       torch.Generator = None					,					a							:       float = 0					,					a							:       float = 0					,					a							:       torch.Generator = None					,					a							:       float = 0					,					a							:       torch.Tensor = None					,					a							:       torch.Tensor = None					,					a							:       Dict=True					,					)							->				Union[
			    Union[AudioPipelineOutput, ImagePipelineOutput],
			    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
			]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				steps or self.get_default_steps()
				self.scheduler.set_timesteps(a		)
				SCREAMING_SNAKE_CASE      :					str    =				step_generator or generator
				# For backwards compatibility
				if type(self.unet.config.sample_size		) == int:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(self.unet.config.sample_size, self.unet.config.sample_size)
				if noise is None:
					SCREAMING_SNAKE_CASE      :					Dict    =				randn_tensor(
					    (
					        batch_size,
					        self.unet.config.in_channels,
					        self.unet.config.sample_size[0],
					        self.unet.config.sample_size[1],
					    )					,					generator=a					,					device=self.device					,					)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				noise
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				None

				if audio_file is not None or raw_audio is not None:
					self.mel.load_audio(a					,					a		)
					SCREAMING_SNAKE_CASE      :					Dict    =				self.mel.audio_slice_to_image(a		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				np.frombuffer(input_image.tobytes()					,					dtype="uint8"		).reshape(
					    (input_image.height, input_image.width)		)
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(input_image / 255) * 2 - 1
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.tensor(input_image[np.newaxis, :, :]					,					dtype=torch.float		).to(self.device		)

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

					if start_step > 0:
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.scheduler.add_noise(a					,					a					,					self.scheduler.timesteps[start_step - 1]		)

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

				for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
					if isinstance(self.unet					,					a		):
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.unet(a					,					a					,					a		)["sample"]
					else:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				self.unet(a					,					a		)["sample"]

					if isinstance(self.scheduler					,					a		):
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.scheduler.step(
						    model_output=a					,					timestep=a					,					sample=a					,					eta=a					,					generator=a					,					)["prev_sample"]
					else:
						SCREAMING_SNAKE_CASE      :					Dict    =				self.scheduler.step(
						    model_output=a					,					timestep=a					,					sample=a					,					generator=a					,					)["prev_sample"]

					if mask is not None:
						if mask_start > 0:
							SCREAMING_SNAKE_CASE      :					Optional[int]    =				mask[:, step, :, :mask_start]
						if mask_end > 0:
							SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				mask[:, step, :, -mask_end:]

				if self.vqvae is not None:
					# 0.18215 was scaling factor used in training to ensure unit variance
					SCREAMING_SNAKE_CASE      :					int    =				1 / self.vqvae.config.scaling_factor * images
					SCREAMING_SNAKE_CASE      :					List[str]    =				self.vqvae.decode(a		)["sample"]

				SCREAMING_SNAKE_CASE      :					Tuple    =				(images / 2 + 0.5).clamp(0					,					1		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				images.cpu().permute(0					,					2					,					3					,					1		).numpy()
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(images * 255).round().astype("uint8"		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				list(
				    (Image.fromarray(_[:, :, 0]		) for _ in images)
				    if images.shape[3] == 1
				    else (Image.fromarray(a					,					mode="RGB"		).convert("L"		) for _ in images)		)

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

				return BaseOutput(**AudioPipelineOutput(np.array(a		)[:, np.newaxis, :]		)					,					**ImagePipelineOutput(a		)		)
			@torch.no_grad()
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       List[Image.Image]					,					a							:       int = 50		)							->				np.ndarray:

				"""simple docstring"""

				assert isinstance(self.scheduler					,					a		)
				self.scheduler.set_timesteps(a		)
				SCREAMING_SNAKE_CASE      :					str    =				np.array(
				    [np.frombuffer(image.tobytes()					,					dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(sample / 255) * 2 - 1
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.Tensor(a		).to(self.device		)

				for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,					(0,)		)		):
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
					SCREAMING_SNAKE_CASE      :					int    =				self.scheduler.alphas_cumprod[t]
					SCREAMING_SNAKE_CASE      :					Any    =				(
					    self.scheduler.alphas_cumprod[prev_timestep]
					    if prev_timestep >= 0
					    else self.scheduler.final_alpha_cumprod
					)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				1 - alpha_prod_t
					SCREAMING_SNAKE_CASE      :					Dict    =				self.unet(a					,					a		)["sample"]
					SCREAMING_SNAKE_CASE      :					Dict    =				(1 - alpha_prod_t_prev) ** 0.5 * model_output
					SCREAMING_SNAKE_CASE      :					List[str]    =				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

				return sample






			@staticmethod
			def 							__UpperCamelCase       (	a							:       torch.Tensor					,					a							:       torch.Tensor					,					a							:       float		)							->				torch.Tensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				acos(torch.dot(torch.flatten(a		)					,					torch.flatten(a		)		) / torch.norm(a		) / torch.norm(a		)		)
				return sin((1 - alpha) * theta		) * xa / sin(a		) + sin(alpha * theta		) * xa / sin(a		)




"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					None:
         warnings.warn(
             '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use DonutImageProcessor instead.'''       ,	A       ,	)
         super().__init__(*A       ,	**A		)


