






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    (			_a     ):




			"""simple docstring"""







			def __init__(			self  :					int       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					int = None       ,						):
									super().__init__()
									self.register_modules(transformer=lowerCAmelCase__       ,						vae=lowerCAmelCase__       ,						scheduler=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]):

									if not isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			List[str]  		=	list(lowerCAmelCase__)

									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  :					List[str]       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Tuple = 4.0       ,						lowerCAmelCase__  :					List[Any] = None       ,						lowerCAmelCase__  :					List[Any] = 50       ,						lowerCAmelCase__  :					List[str] = "pil"       ,						lowerCAmelCase__  :					Union[str, Any] = True       ,						):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	len(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.transformer.config.sample_size
									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.transformer.config.in_channels

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

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

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

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

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

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

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

																					SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.cat([eps, rest]       ,						dim=1)

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

															# compute previous image: x_t -> x_t-1
															SCREAMING_SNAKE_CASE_:			List[str]  		=	self.scheduler.step(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__).prev_sample

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

									SCREAMING_SNAKE_CASE_:			int  		=	1 / self.vae.config.scaling_factor * latents
									SCREAMING_SNAKE_CASE_:			int  		=	self.vae.decode(lowerCAmelCase__).sample

									SCREAMING_SNAKE_CASE_:			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
									SCREAMING_SNAKE_CASE_:			List[str]  		=	samples.cpu().permute(0       ,						2       ,						3       ,						1).float().numpy()

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

									if not return_dict:
															return (samples,)

									return ImagePipelineOutput(images=lowerCAmelCase__)









# Copyright 2022 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
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase							:  List[str]            =						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def       A_				(   _UpperCAmelCase=None   ):
						if subparsers is not None:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	subparsers.add_parser("tpu-config"    ,     description=_description   )
						else:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	argparse.ArgumentParser("Accelerate tpu-config command"    ,     description=_description   )
						# Core arguments
						SCREAMING_SNAKE_CASE_:			Any  		=	parser.add_argument_group(
						    "Config Arguments"    ,     "Arguments that can be configured through `accelerate config`."   )
						config_args.add_argument(
						    "--config_file"    ,     type=_UpperCAmelCase    ,     default=_UpperCAmelCase    ,     help="Path to the config file to use for accelerate."    ,     )
						config_args.add_argument(
						    "--tpu_name"    ,     default=_UpperCAmelCase    ,     help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."    ,     )
						config_args.add_argument(
						    "--tpu_zone"    ,     default=_UpperCAmelCase    ,     help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."    ,     )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	parser.add_argument_group("TPU Arguments"    ,     "Arguments for options ran inside the TPU."   )
						pod_args.add_argument(
						    "--use_alpha"    ,     action="store_true"    ,     help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."    ,     )
						pod_args.add_argument(
						    "--command_file"    ,     default=_UpperCAmelCase    ,     help="The path to the file containing the commands to run on the pod on startup."    ,     )
						pod_args.add_argument(
						    "--command"    ,     action="append"    ,     nargs="+"    ,     help="A command to run on the pod. Can be passed multiple times."    ,     )
						pod_args.add_argument(
						    "--install_accelerate"    ,     action="store_true"    ,     help="Whether to install accelerate on the pod. Defaults to False."    ,     )
						pod_args.add_argument(
						    "--accelerate_version"    ,     default="latest"    ,     help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."    ,     )
						pod_args.add_argument(
						    "--debug"    ,     action="store_true"    ,     help="If set, will print the command that would be run instead of running it."   )

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







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	None

						# Get the default from the config file if it exists.
						if args.config_file is not None or os.path.isfile(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_config_from_file(args.config_file   )
												if not args.command_file and defaults.command_file is not None and not args.command:
																		SCREAMING_SNAKE_CASE_:			Any  		=	defaults.command_file
												if not args.command and defaults.commands is not None:
																		SCREAMING_SNAKE_CASE_:			int  		=	defaults.commands
												if not args.tpu_name:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	defaults.tpu_name
												if not args.tpu_zone:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	defaults.tpu_zone
						if args.accelerate_version == "dev":
												SCREAMING_SNAKE_CASE_:			Tuple  		=	"git+https://github.com/huggingface/accelerate.git"
						elif args.accelerate_version == "latest":
												SCREAMING_SNAKE_CASE_:			str  		=	"accelerate -U"
						elif isinstance(parse(args.accelerate_version   )    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	f"accelerate=={args.accelerate_version}"

						if not args.command_file and not args.command:
												raise ValueError("You must specify either a command file or a command to run on the pod."   )

						if args.command_file:
												with open(args.command_file    ,     "r"   ) as f:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	[f.read().splitlines()]

    # To turn list of lists into list of strings
						if isinstance(args.command[0]    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	[line for cmd in args.command for line in cmd]
						# Default to the shared folder and install accelerate
						SCREAMING_SNAKE_CASE_:			Dict  		=	["cd /usr/share"]
						if args.install_accelerate:
												new_cmd += [f"pip install {args.accelerate_version}"]
						new_cmd += args.command
						SCREAMING_SNAKE_CASE_:			List[str]  		=	"; ".join(_UpperCAmelCase   )

						# Then send it to gcloud
						# Eventually try to use google-api-core to do this instead of subprocess
						SCREAMING_SNAKE_CASE_:			int  		=	["gcloud"]
						if args.use_alpha:
												cmd += ["alpha"]
						cmd += [
						    "compute",
						    "tpus",
						    "tpu-vm",
						    "ssh",
						    args.tpu_name,
						    "--zone",
						    args.tpu_zone,
						    "--command",
						    args.command,
						    "--worker",
						    "all",
						]
						if args.debug:
												print(f"Running {' '.join(_UpperCAmelCase   )}"   )
												return
						subprocess.run(_UpperCAmelCase   )
						print("Successfully setup pod."   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	tpu_command_parser()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	parser.parse_args()

						tpu_command_launcher(_UpperCAmelCase   )






