





"""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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   _A = None						,   ):






									'''simple docstring'''







									super().__init__()
									self.register_modules(transformer=_A						,   vae=_A						,   scheduler=_A    )

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







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







									if not isinstance(_A						,   _A    ):
															UpperCamelCase				:		Optional[int]				   =			list(_A    )

									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						,   _A						,   _A = 4.0						,   _A = None						,   _A = 5_0						,   _A = "pil"						,   _A = True						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			len(_A    )
									UpperCamelCase				:		Optional[Any]				   =			self.transformer.config.sample_size
									UpperCamelCase				:		Tuple				   =			self.transformer.config.in_channels

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

									UpperCamelCase				:		List[str]				   =			torch.tensor(_A						,   device=self.device    ).reshape(-1    )
									UpperCamelCase				:		Union[str, Any]				   =			torch.tensor([1_0_0_0] * batch_size						,   device=self.device    )
									UpperCamelCase				:		Optional[Any]				   =			torch.cat([class_labels, class_null]						,   0    ) if guidance_scale > 1 else class_labels

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

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

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

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

																					UpperCamelCase				:		Tuple				   =			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																					UpperCamelCase				:		Optional[Any]				   =			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(_A						,   _A						,   dim=1    )
															else:
																					UpperCamelCase				:		Tuple				   =			noise_pred

															# compute previous image: x_t -> x_t-1
															UpperCamelCase				:		List[Any]				   =			self.scheduler.step(_A						,   _A						,   _A    ).prev_sample

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

									UpperCamelCase				:		Optional[Any]				   =			1 / self.vae.config.scaling_factor * latents
									UpperCamelCase				:		Any				   =			self.vae.decode(_A    ).sample

									UpperCamelCase				:		List[Any]				   =			(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				:		int				   =			samples.cpu().permute(0						,   2						,   3						,   1    ).float().numpy()

									if output_type == "pil":
															UpperCamelCase				:		Tuple				   =			self.numpy_to_pil(_A    )

									if not return_dict:
															return (samples,)

									return ImagePipelineOutput(images=_A    )










"""simple docstring"""


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A = None						,   _A = None						,   _A = True						,   _A = None						,   _A = False						,   _A = None						,   _A = True						,   _A = "arrow"						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(
									    split=_A						,   features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   streaming=_A						,   **_A						,   )
									UpperCamelCase				:		Optional[Any]				   =			load_from_cache_file
									UpperCamelCase				:		List[str]				   =			file_format
									UpperCamelCase				:		Optional[Any]				   =			Spark(
									    df=_A						,   features=_A						,   cache_dir=_A						,   working_dir=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split    )
									UpperCamelCase				:		Dict				   =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_A						,   file_format=self._file_format						,   )
									return self.builder.as_dataset(split=self.split    )



