






"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a					:							str    =						logging.get_logger(__name__)


class        __UpperCAmelCase(  __A				):




						"""simple docstring"""




						__lowerCamelCase										=   ["input_values", "padding_mask"]




						def __init__(  self			,    snake_case__ = 1			,    snake_case__ = 24000			,    snake_case__ = 0.0			,    snake_case__ = None			,    snake_case__ = None			,    **snake_case__			,    ):
												'''simple docstring'''
												super().__init__(feature_size=lowerCAmelCase_			,    sampling_rate=lowerCAmelCase_			,    padding_value=lowerCAmelCase_			,    **lowerCAmelCase_     )
												lowercase__							: Tuple=			chunk_length_s
												lowercase__							: Union[str, Any]=			overlap




						@property
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate     )




						@property
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1			,    int((1.0 - self.overlap) * self.chunk_length     )     )




						def __call__(  self			,    snake_case__			,    snake_case__ = None			,    snake_case__ = False			,    snake_case__ = None			,    snake_case__ = None			,    snake_case__ = None			,    ):
												'''simple docstring'''
												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																								    F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
																								    F''' {self.sampling_rate} and not {sampling_rate}.'''     )
												else:
																		logger.warning(
																		    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																		    "Failing to do so can result in silent errors that might be hard to debug."     )

												if padding and truncation:
																		raise ValueError("Both padding and truncation were set. Make sure you only set one."     )
												elif padding is None:
																		# by default let's pad the inputs
																		lowercase__							: Tuple=			True

												lowercase__							: Union[str, Any]=			bool(
												    isinstance(lowerCAmelCase_			,    (list, tuple)     ) and (isinstance(raw_audio[0]			,    (np.ndarray, tuple, list)     ))     )

												if is_batched:
																		lowercase__							: List[str]=			[np.asarray(lowerCAmelCase_			,    dtype=np.floataa     ).T for audio in raw_audio]
												elif not is_batched and not isinstance(lowerCAmelCase_			,    np.ndarray     ):
																		lowercase__							: str=			np.asarray(lowerCAmelCase_			,    dtype=np.floataa     )
												elif isinstance(lowerCAmelCase_			,    np.ndarray     ) and raw_audio.dtype is np.dtype(np.floataa     ):
																		lowercase__							: str=			raw_audio.astype(np.floataa     )

												# always return batch
												if not is_batched:
																		lowercase__							: List[str]=			[np.asarray(lowerCAmelCase_     ).T]

												# verify inputs are valid
												for idx, example in enumerate(lowerCAmelCase_     ):
																		if example.ndim > 2:
																								raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}'''     )
																		if self.feature_size == 1 and example.ndim != 1:
																								raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels'''     )
																		if self.feature_size == 2 and example.shape[-1] != 2:
																								raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels'''     )

												lowercase__							: Optional[Any]=			None
												lowercase__							: List[str]=			BatchFeature({"input_values": raw_audio}     )
												if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																		if truncation:
																								lowercase__							: Optional[int]=			min(array.shape[0] for array in raw_audio     )
																								lowercase__							: Optional[Any]=			int(np.floor(max_length / self.chunk_stride     )     )
																								lowercase__							: Optional[int]=			(nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																								lowercase__							: int=			max(array.shape[0] for array in raw_audio     )
																								lowercase__							: Dict=			int(np.ceil(max_length / self.chunk_stride     )     )
																								lowercase__							: Optional[Any]=			(nb_step - 1) * self.chunk_stride + self.chunk_length
																								lowercase__							: Tuple=			"max_length"
																		else:
																								lowercase__							: Optional[int]=			input_values

        # normal padding on batch
												if padded_inputs is None:
																		lowercase__							: List[Any]=			self.pad(
																		    lowerCAmelCase_			,    max_length=lowerCAmelCase_			,    truncation=lowerCAmelCase_			,    padding=lowerCAmelCase_			,    return_attention_mask=lowerCAmelCase_			,    )
																		if padding:
																								lowercase__							: Any=			padded_inputs.pop("attention_mask"     )

												lowercase__							: List[Any]=			[]
												for example in padded_inputs.pop("input_values"     ):
																		if self.feature_size == 1:
																								lowercase__							: List[str]=			example[..., None]
																		input_values.append(example.T     )

												lowercase__							: Dict=			input_values
												if return_tensors is not None:
																		lowercase__							: Optional[int]=			padded_inputs.convert_to_tensors(lowerCAmelCase_     )

												return padded_inputs














"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      UpperCamelCase_			(__A	):





	def __init__( self   :					List[Any]      ,      lowerCAmelCase_   :					TransformeraDModel      ,      lowerCAmelCase_   :					AutoencoderKL      ,      lowerCAmelCase_   :					KarrasDiffusionSchedulers      ,      lowerCAmelCase_   :					Optional[Dict[int, str]] = None      ,      )		->     List[Any]:
				super().__init__()
				self.register_modules(transformer=lowerCAmelCase_      ,      vae=lowerCAmelCase_      ,      scheduler=lowerCAmelCase_		)

				# create a imagenet -> id dictionary for easier use
				UpperCAmelCase_	:					str     	=   {}
				if idalabel is not None:
							for key, value in idalabel.items():
										for label in value.split(","		):
													UpperCAmelCase_	:					Union[str, Any]     	=   int(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   dict(sorted(self.labels.items()		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Union[str, List[str]]		)		->     List[int]:

				if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					str     	=   list(lowerCAmelCase_		)

				for l in label:
							if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

				return [self.labels[l] for l in label]





	@torch.no_grad()
	def __call__( self   :					List[str]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					float = 4.0      ,      lowerCAmelCase_   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase_   :					int = 50      ,      lowerCAmelCase_   :					Optional[str] = "pil"      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Union[ImagePipelineOutput, Tuple]:
				UpperCAmelCase_	:					Optional[int]     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   self.transformer.config.sample_size
				UpperCAmelCase_	:					Optional[Any]     	=   self.transformer.config.in_channels

				UpperCAmelCase_	:					Tuple     	=   randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)      ,      generator=lowerCAmelCase_      ,      device=self.device      ,      dtype=self.transformer.dtype      ,      )
				UpperCAmelCase_	:					str     	=   torch.cat([latents] * 2		) if guidance_scale > 1 else latents

				UpperCAmelCase_	:					List[Any]     	=   torch.tensor(lowerCAmelCase_      ,      device=self.device		).reshape(-1		)
				UpperCAmelCase_	:					Optional[Any]     	=   torch.tensor([1_000] * batch_size      ,      device=self.device		)
				UpperCAmelCase_	:					int     	=   torch.cat([class_labels, class_null]      ,      0		) if guidance_scale > 1 else class_labels

				# set step values
				self.scheduler.set_timesteps(lowerCAmelCase_		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							if guidance_scale > 1:
										UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input[: len(lowerCAmelCase_		) // 2]
										UpperCAmelCase_	:					int     	=   torch.cat([half, half]      ,      dim=0		)
							UpperCAmelCase_	:					Optional[int]     	=   self.scheduler.scale_model_input(lowerCAmelCase_      ,      lowerCAmelCase_		)

							UpperCAmelCase_	:					Union[str, Any]     	=   t
							if not torch.is_tensor(lowerCAmelCase_		):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										UpperCAmelCase_	:					List[Any]     	=   latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
													UpperCAmelCase_	:					Optional[int]     	=   torch.floataa if is_mps else torch.floataa
										else:
													UpperCAmelCase_	:					int     	=   torch.intaa if is_mps else torch.intaa
										UpperCAmelCase_	:					Optional[int]     	=   torch.tensor([timesteps]      ,      dtype=lowerCAmelCase_      ,      device=latent_model_input.device		)
							elif len(timesteps.shape		) == 0:
										UpperCAmelCase_	:					List[Any]     	=   timesteps[None].to(latent_model_input.device		)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCAmelCase_	:					List[Any]     	=   timesteps.expand(latent_model_input.shape[0]		)
							# predict noise model_output
							UpperCAmelCase_	:					List[Any]     	=   self.transformer(
							    lowerCAmelCase_      ,      timestep=lowerCAmelCase_      ,      class_labels=lowerCAmelCase_		).sample

							# perform guidance
							if guidance_scale > 1:
										UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   torch.split(lowerCAmelCase_      ,      len(lowerCAmelCase_		) // 2      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										UpperCAmelCase_	:					Tuple     	=   torch.cat([half_eps, half_eps]      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   torch.cat([eps, rest]      ,      dim=1		)

							# learned sigma
							if self.transformer.config.out_channels // 2 == latent_channels:
										UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   torch.split(lowerCAmelCase_      ,      lowerCAmelCase_      ,      dim=1		)
							else:
										UpperCAmelCase_	:					Optional[Any]     	=   noise_pred

							# compute previous image: x_t -> x_t-1
							UpperCAmelCase_	:					Union[str, Any]     	=   self.scheduler.step(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		).prev_sample

				if guidance_scale > 1:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input.chunk(2      ,      dim=0		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   latent_model_input

				UpperCAmelCase_	:					Dict     	=   1 / self.vae.config.scaling_factor * latents
				UpperCAmelCase_	:					Dict     	=   self.vae.decode(lowerCAmelCase_		).sample

				UpperCAmelCase_	:					Tuple     	=   (samples / 2 + 0.5).clamp(0      ,      1		)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				UpperCAmelCase_	:					Optional[Any]     	=   samples.cpu().permute(0      ,      2      ,      3      ,      1		).float().numpy()

				if output_type == "pil":
							UpperCAmelCase_	:					Optional[int]     	=   self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase_		)



