

"""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 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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

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







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Union[str, List[str]]		):

						if not isinstance(lowercase_   ,lowercase_		):
							lowerCAmelCase__	:						Any  				=	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			:				int   ,lowercase_			:				List[int]   ,lowercase_			:				float = 4.0   ,lowercase_			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,lowercase_			:				int = 5_0   ,lowercase_			:				Optional[str] = "pil"   ,lowercase_			:				bool = True   ,):
						lowerCAmelCase__	:						Optional[Any]  				=	len(lowercase_		)
						lowerCAmelCase__	:						Any  				=	self.transformer.config.sample_size
						lowerCAmelCase__	:						int  				=	self.transformer.config.in_channels

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

						lowerCAmelCase__	:						List[Any]  				=	torch.tensor(lowercase_   ,device=self.device		).reshape(-1		)
						lowerCAmelCase__	:						Dict  				=	torch.tensor([1_0_0_0] * batch_size   ,device=self.device		)
						lowerCAmelCase__	:						List[Any]  				=	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__	:						List[Any]  				=	latent_model_input[: len(lowercase_		) // 2]
								lowerCAmelCase__	:						Optional[Any]  				=	torch.cat([half, half]   ,dim=0		)
							lowerCAmelCase__	:						List[str]  				=	self.scheduler.scale_model_input(lowercase_   ,lowercase_		)

							lowerCAmelCase__	:						Optional[int]  				=	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__	:						Any  				=	latent_model_input.device.type == '''mps'''
								if isinstance(lowercase_   ,lowercase_		):
									lowerCAmelCase__	:						Dict  				=	torch.floataa if is_mps else torch.floataa
								else:
									lowerCAmelCase__	:						List[str]  				=	torch.intaa if is_mps else torch.intaa
								lowerCAmelCase__	:						List[Any]  				=	torch.tensor([timesteps]   ,dtype=lowercase_   ,device=latent_model_input.device		)
							elif len(timesteps.shape		) == 0:
								lowerCAmelCase__	:						List[Any]  				=	timesteps[None].to(latent_model_input.device		)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							lowerCAmelCase__	:						List[str]  				=	timesteps.expand(latent_model_input.shape[0]		)
							# predict noise model_output
							lowerCAmelCase__	:						Dict  				=	self.transformer(
							    lowercase_   ,timestep=lowercase_   ,class_labels=lowercase_		).sample

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

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

								lowerCAmelCase__	:						Union[str, Any]  				=	torch.cat([eps, rest]   ,dim=1		)

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

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

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

						lowerCAmelCase__	:						List[Any]  				=	1 / self.vae.config.scaling_factor * latents
						lowerCAmelCase__	:						List[Any]  				=	self.vae.decode(lowercase_		).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__	:						Tuple  				=	samples.cpu().permute(0   ,2   ,3   ,1		).float().numpy()

						if output_type == "pil":
							lowerCAmelCase__	:						List[str]  				=	self.numpy_to_pil(lowercase_		)

						if not return_dict:
							return (samples,)

						return ImagePipelineOutput(images=lowercase_		)







"""simple docstring"""





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def   __SCREAMING_SNAKE_CASE  (      A_=None ):
	if subparsers is not None:
		lowerCAmelCase__	:						Optional[Any]  				=	subparsers.add_parser('''test''' )
	else:
		lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser('''Accelerate test command''' )

	parser.add_argument(
	    '''--config_file'''						,     default=A_						,     help=(
	        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
	        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
	        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
	        '''with \'huggingface\'.'''
	    )						,     )

	if subparsers is not None:
		parser.set_defaults(func=A_ )
	return parser

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )

	if args.config_file is None:
		lowerCAmelCase__	:						Optional[Any]  				=	script_name
	else:
		lowerCAmelCase__	:						Any  				=	f'--config_file={args.config_file} {script_name}'

	lowerCAmelCase__	:						List[Any]  				=	['''accelerate-launch'''] + test_args.split()
	lowerCAmelCase__	:						int  				=	execute_subprocess_async(A_						,     env=os.environ.copy() )
	if result.returncode == 0:
		print('''Test is a success! You are ready for your distributed training!''' )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	test_command_parser()
	lowerCAmelCase__	:						List[Any]  				=	parser.parse_args()
	test_command(A_ )


if __name__ == "__main__":
							main()




