





# 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							:       Union[str, Any]					 =	"Run commands across TPU VMs for initial setup before running `accelerate launch`."






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple=None					)      ->    int:

						"""simple docstring"""





						if subparsers is not None:
												__SCREAMING_SNAKE_CASE:				Dict           =   subparsers.add_parser('''tpu-config'''       ,			description=_description					)
						else:
												__SCREAMING_SNAKE_CASE:				Tuple           =   argparse.ArgumentParser('''Accelerate tpu-config command'''       ,			description=_description					)
						# Core arguments
						__SCREAMING_SNAKE_CASE:				List[str]           =   parser.add_argument_group(
						    '''Config Arguments'''       ,			'''Arguments that can be configured through `accelerate config`.'''					)
						config_args.add_argument(
						    '''--config_file'''       ,			type=lowerCamelCase__       ,			default=lowerCamelCase__       ,			help='''Path to the config file to use for accelerate.'''       ,			)
						config_args.add_argument(
						    '''--tpu_name'''       ,			default=lowerCamelCase__       ,			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=lowerCamelCase__       ,			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=lowerCamelCase__       ,			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=lowerCamelCase__					)
						return parser






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]					)      ->    Optional[int]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   None

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

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

						# Then send it to gcloud
						# Eventually try to use google-api-core to do this instead of subprocess
						__SCREAMING_SNAKE_CASE:				List[Any]           =   ['''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(lowerCamelCase__					)}"""					)
												return
						subprocess.run(lowerCamelCase__					)
						print('''Successfully setup pod.'''					)






def 		lowerCAmelCase  (	)      ->    List[str]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   tpu_command_parser()
						__SCREAMING_SNAKE_CASE:				List[Any]           =   parser.parse_args()

						tpu_command_launcher(lowerCamelCase__					)







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







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







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

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

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

