




import operator as op


__UpperCamelCase   :			Optional[Any]          = "scaler.pt"
__UpperCamelCase   :			Optional[Any]          = "pytorch_model"
__UpperCamelCase   :			str          = "random_states"
__UpperCamelCase   :			Optional[int]          = "optimizer"
__UpperCamelCase   :			Optional[int]          = "scheduler"
__UpperCamelCase   :			str          = "pytorch_model.bin"
__UpperCamelCase   :			List[str]          = "pytorch_model.bin.index.json"
__UpperCamelCase   :			List[str]          = "model.safetensors"
__UpperCamelCase   :			Optional[int]          = "model.safetensors.index.json"
__UpperCamelCase   :			List[str]          = "1.10.2"
__UpperCamelCase   :			Dict          = "py38"
__UpperCamelCase   :			List[str]          = "4.17.0"
__UpperCamelCase   :			Any          = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
__UpperCamelCase   :			Any          = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
__UpperCamelCase   :			int          = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
__UpperCamelCase   :			Dict          = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
__UpperCamelCase   :			str          = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
__UpperCamelCase   :			List[Any]          = "2.0.1"
__UpperCamelCase   :			int          = ["pdsh", "standard", "openmpi", "mvapich"]
__UpperCamelCase   :			List[str]          = ["default", "reduce-overhead", "max-autotune"]

__UpperCamelCase   :			List[Any]          = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
__UpperCamelCase   :			List[Any]          = [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

__UpperCamelCase   :			List[str]          = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
__UpperCamelCase   :			int          = ["DEEPSPEED", "MULTI_XPU", "FSDP"]







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

