"""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  =				logging.get_logger(__name__)
class  _snake_case     (					a__		):
						snake_case__			  =			["input_values", "padding_mask"]






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			int = 1							,					UpperCAmelCase      :			int = 24000							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			float = None							,					**UpperCAmelCase      :			int							,					):
													super().__init__(feature_size=UpperCAmelCase							,					sampling_rate=UpperCAmelCase							,					padding_value=UpperCAmelCase							,					**UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     chunk_length_s
													__lowerCamelCase					:		str		   =     overlap






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													if self.chunk_length_s is None:
																				return None
													else:
																				return int(self.chunk_length_s * self.sampling_rate    )






						@property
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													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      :			Dict							,					UpperCAmelCase      :			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,					UpperCAmelCase      :			Optional[Union[bool, str, PaddingStrategy]] = None							,					UpperCAmelCase      :			Optional[bool] = False							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[Union[str, TensorType]] = None							,					UpperCAmelCase      :			Optional[int] = None							,					):
													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
																				__lowerCamelCase					:		List[Any]		   =     True

													__lowerCamelCase					:		Any		   =     bool(
													    isinstance(UpperCAmelCase							,					(list, tuple)    ) and (isinstance(raw_audio[0]							,					(np.ndarray, tuple, list)    ))    )

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

													# always return batch
													if not is_batched:
																				__lowerCamelCase					:		int		   =     [np.asarray(UpperCAmelCase    ).T]

													# verify inputs are valid
													for idx, example in enumerate(UpperCAmelCase    ):
																				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"""    )

													__lowerCamelCase					:		List[str]		   =     None
													__lowerCamelCase					:		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:
																											__lowerCamelCase					:		Dict		   =     min(array.shape[0] for array in raw_audio    )
																											__lowerCamelCase					:		Optional[int]		   =     int(np.floor(max_length / self.chunk_stride    )    )
																											__lowerCamelCase					:		Any		   =     (nb_step - 1) * self.chunk_stride + self.chunk_length
																				elif padding:
																											__lowerCamelCase					:		List[str]		   =     max(array.shape[0] for array in raw_audio    )
																											__lowerCamelCase					:		Dict		   =     int(np.ceil(max_length / self.chunk_stride    )    )
																											__lowerCamelCase					:		str		   =     (nb_step - 1) * self.chunk_stride + self.chunk_length
																											__lowerCamelCase					:		Tuple		   =     "max_length"
																				else:
																											__lowerCamelCase					:		int		   =     input_values

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

													__lowerCamelCase					:		Optional[int]		   =     []
													for example in padded_inputs.pop("input_values"    ):
																				if self.feature_size == 1:
																											__lowerCamelCase					:		Dict		   =     example[..., None]
																				input_values.append(example.T    )

													__lowerCamelCase					:		Any		   =     input_values
													if return_tensors is not None:
																				__lowerCamelCase					:		int		   =     padded_inputs.convert_to_tensors(UpperCAmelCase    )

													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  _snake_case     (					a__		):






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

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






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )

													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      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

													__lowerCamelCase					:		Any		   =     randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)							,					generator=UpperCAmelCase							,					device=self.device							,					dtype=self.transformer.dtype							,					)
													__lowerCamelCase					:		Optional[Any]		   =     torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     torch.cat([class_labels, class_null]							,					0    ) if guidance_scale > 1 else class_labels

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

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

																				__lowerCamelCase					:		Dict		   =     t
																				if not torch.is_tensor(UpperCAmelCase    ):
																											# 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+)
																											__lowerCamelCase					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

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

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

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

																				# learned sigma
																				if self.transformer.config.out_channels // 2 == latent_channels:
																											__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     torch.split(UpperCAmelCase							,					UpperCAmelCase							,					dim=1    )
																				else:
																											__lowerCamelCase					:		int		   =     noise_pred

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

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

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		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
													__lowerCamelCase					:		Optional[int]		   =     samples.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )