




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 			__lowerCamelCase	(   UpperCAmelCase_   ):




				"""simple docstring"""

				def __init__(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					str = None       ,     )				->	List[Any]:
									super().__init__()
									self.register_modules(transformer=__lowercase       ,     vae=__lowercase       ,     scheduler=__lowercase       )

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


				def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	List[int]:

									if not isinstance(__lowercase       ,     __lowercase       ):
														lowerCAmelCase__          =			list(__lowercase       )

									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					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any] = 4.0       ,     SCREAMING_SNAKE_CASE__					:					int = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int] = 50       ,     SCREAMING_SNAKE_CASE__					:					Dict = "pil"       ,     SCREAMING_SNAKE_CASE__					:					int = True       ,     )				->	Union[ImagePipelineOutput, Tuple]:
									lowerCAmelCase__          =			len(__lowercase       )
									lowerCAmelCase__          =			self.transformer.config.sample_size
									lowerCAmelCase__          =			self.transformer.config.in_channels

									lowerCAmelCase__          =			randn_tensor(
									    shape=(batch_size, latent_channels, latent_size, latent_size)       ,     generator=__lowercase       ,     device=self.device       ,     dtype=self.transformer.dtype       ,     )
									lowerCAmelCase__          =			torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

									lowerCAmelCase__          =			torch.tensor(__lowercase       ,     device=self.device       ).reshape(-1       )
									lowerCAmelCase__          =			torch.tensor([1_000] * batch_size       ,     device=self.device       )
									lowerCAmelCase__          =			torch.cat([class_labels, class_null]       ,     0       ) if guidance_scale > 1 else class_labels

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

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

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

														# perform guidance
														if guidance_scale > 1:
																			lowerCAmelCase__          =			noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			lowerCAmelCase__          =			torch.split(__lowercase       ,     len(__lowercase       ) // 2       ,     dim=0       )

																			lowerCAmelCase__          =			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			lowerCAmelCase__          =			torch.cat([half_eps, half_eps]       ,     dim=0       )

																			lowerCAmelCase__          =			torch.cat([eps, rest]       ,     dim=1       )

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

														# compute previous image: x_t -> x_t-1
														lowerCAmelCase__          =			self.scheduler.step(__lowercase       ,     __lowercase       ,     __lowercase       ).prev_sample

									if guidance_scale > 1:
														lowerCAmelCase__          =			latent_model_input.chunk(2       ,     dim=0       )
									else:
														lowerCAmelCase__          =			latent_model_input

									lowerCAmelCase__          =			1 / self.vae.config.scaling_factor * latents
									lowerCAmelCase__          =			self.vae.decode(__lowercase       ).sample

									lowerCAmelCase__          =			(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__          =			samples.cpu().permute(0       ,     2       ,     3       ,     1       ).float().numpy()

									if output_type == "pil":
														lowerCAmelCase__          =			self.numpy_to_pil(__lowercase       )

									if not return_dict:
														return (samples,)

									return ImagePipelineOutput(images=__lowercase       )






import argparse
from collections import defaultdict



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'{file}_{class_name}_{test_name}'
     done_test[_id] += 1

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     lowerCAmelCase__          =			F'class {class_name}('
     lowerCAmelCase__          =			F'{4 * " "}def {test_name}('
     lowerCAmelCase__          =			F'{8 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			F'{16 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			0
     lowerCAmelCase__          =			0

     lowerCAmelCase__          =			[]
     for line in lines:
          if line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and in_func and (line.startswith(lowerCAmelCase_      ) or line.startswith(lowerCAmelCase_      )):
               lowerCAmelCase__          =			len(line.split(correct_line.split()[0]      )[0]      )
               count += 1

               if count == done_test[_id]:
                    lowerCAmelCase__          =			True

          if in_class and in_func and in_line:
               if ")" not in line:
                    continue
               else:
                    lowerCAmelCase__          =			True

          if in_class and in_func and in_line and insert_line:
               new_lines.append(F'{spaces * " "}{correct_line}'      )
               lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			False
          else:
               new_lines.append(lowerCAmelCase_      )

     with open(lowerCAmelCase_       ,    "w"      ) as f:
          for line in new_lines:
               f.write(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     if fail is not None:
          with open(lowerCAmelCase_       ,    "r"      ) as f:
               lowerCAmelCase__          =			{l.strip() for l in f.readlines()}
     else:
          lowerCAmelCase__          =			None

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     lowerCAmelCase__          =			defaultdict(lowerCAmelCase_      )
     for line in correct_lines:
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			line.split(";"      )
          if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
               overwrite_file(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument('--correct_filename', help='filename of tests with expected result')
     parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
     UpperCamelCase								=					parser.parse_args()

     main(args.correct_filename, args.fail_filename)
