



'''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 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""vqvae"""]





	def __init__(					self   :							Tuple					,lowerCamelCase__   :							AutoencoderKL					,lowerCamelCase__   :							UNetaDConditionModel					,lowerCamelCase__   :							Mel					,lowerCamelCase__   :							Union[DDIMScheduler, DDPMScheduler]					,):







							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=lowerCamelCase__					,scheduler=lowerCamelCase__					,mel=lowerCamelCase__					,vqvae=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							return 50 if isinstance(self.scheduler					,lowerCamelCase__					) else 1000





	@torch.no_grad()
	def __call__(					self   :							Union[str, Any]					,lowerCamelCase__   :							int = 1					,lowerCamelCase__   :							str = None					,lowerCamelCase__   :							np.ndarray = None					,lowerCamelCase__   :							int = 0					,lowerCamelCase__   :							int = 0					,lowerCamelCase__   :							int = None					,lowerCamelCase__   :							torch.Generator = None					,lowerCamelCase__   :							float = 0					,lowerCamelCase__   :							float = 0					,lowerCamelCase__   :							torch.Generator = None					,lowerCamelCase__   :							float = 0					,lowerCamelCase__   :							torch.Tensor = None					,lowerCamelCase__   :							torch.Tensor = None					,lowerCamelCase__   :							Tuple=True					,):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  steps or self.get_default_steps()
							self.scheduler.set_timesteps(lowerCamelCase__					)
							_UpperCamelCase   :						int												=  step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													_UpperCamelCase   :						List[str]												=  (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													_UpperCamelCase   :						int												=  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					,)
							_UpperCamelCase   :						List[Any]												=  noise
							_UpperCamelCase   :						Dict												=  None

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

													if self.vqvae is not None:
																			_UpperCamelCase   :						Union[str, Any]												=  self.vqvae.encode(torch.unsqueeze(lowerCamelCase__					,0					)					).latent_dist.sample(
																			    generator=lowerCamelCase__					)[0]
																			_UpperCamelCase   :						str												=  self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			_UpperCamelCase   :						List[Any]												=  self.scheduler.add_noise(lowerCamelCase__					,lowerCamelCase__					,self.scheduler.timesteps[start_step - 1]					)

													_UpperCamelCase   :						Optional[int]												=  (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													_UpperCamelCase   :						Optional[int]												=  int(mask_start_secs * pixels_per_second					)
													_UpperCamelCase   :						Any												=  int(mask_end_secs * pixels_per_second					)
													_UpperCamelCase   :						str												=  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__					):
																			_UpperCamelCase   :						Optional[int]												=  self.unet(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)['sample']
													else:
																			_UpperCamelCase   :						Optional[int]												=  self.unet(lowerCamelCase__					,lowerCamelCase__					)['sample']

													if isinstance(self.scheduler					,lowerCamelCase__					):
																			_UpperCamelCase   :						Dict												=  self.scheduler.step(
																			    model_output=lowerCamelCase__					,timestep=lowerCamelCase__					,sample=lowerCamelCase__					,eta=lowerCamelCase__					,generator=lowerCamelCase__					,)['prev_sample']
													else:
																			_UpperCamelCase   :						Tuple												=  self.scheduler.step(
																			    model_output=lowerCamelCase__					,timestep=lowerCamelCase__					,sample=lowerCamelCase__					,generator=lowerCamelCase__					,)['prev_sample']

													if mask is not None:
																			if mask_start > 0:
																									_UpperCamelCase   :						int												=  mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									_UpperCamelCase   :						List[Any]												=  mask[:, step, :, -mask_end:]

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

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

							_UpperCamelCase   :						Tuple												=  [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       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							List[Image.Image]					,lowerCamelCase__   :							int = 50					):







							'''simple docstring'''
							assert isinstance(self.scheduler					,lowerCamelCase__					)
							self.scheduler.set_timesteps(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  np.array(
							    [np.frombuffer(image.tobytes()					,dtype='uint8'					).reshape((1, image.height, image.width)					) for image in images]					)
							_UpperCamelCase   :						Optional[Any]												=  (sample / 255) * 2 - 1
							_UpperCamelCase   :						Union[str, Any]												=  torch.Tensor(lowerCamelCase__					).to(self.device					)

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

							return sample





	@staticmethod
	def       UpperCamelCase_    (					lowerCamelCase__   :							torch.Tensor					,lowerCamelCase__   :							torch.Tensor					,lowerCamelCase__   :							float					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  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__					)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_					:      Tuple    			=    {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      int    			=    ['LayoutLMv2TokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Union[str, Any]    			=    ['LayoutLMv2FeatureExtractor']
					snake_case_					:      str    			=    ['LayoutLMv2ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Optional[int]    			=    [
					    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMv2ForQuestionAnswering',
					    'LayoutLMv2ForSequenceClassification',
					    'LayoutLMv2ForTokenClassification',
					    'LayoutLMv2Layer',
					    'LayoutLMv2Model',
					    'LayoutLMv2PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaLayer,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)
else:
					import sys

					snake_case_					:      int    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


