

from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
						import torch

if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm


lowerCAmelCase							:  str            =						logging.get_logger(__name__)



@dataclass
class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					[
			    '''no_inference''',
			    '''no_cuda''',
			    '''no_tpu''',
			    '''no_speed''',
			    '''no_memory''',
			    '''no_env_print''',
			    '''no_multi_process''',
			]



			def __init__(			self  :					str       ,						**lowerCAmelCase__  :					str):
									for deprecated_arg in self.deprecated_args:
															if deprecated_arg in kwargs:
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	deprecated_arg[3:]
																					setattr(self       ,						lowerCAmelCase__       ,						not kwargs.pop(lowerCAmelCase__))
																					logger.warning(
																					    F"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
																					    F" {positive_arg}={kwargs[positive_arg]}")

									SCREAMING_SNAKE_CASE_:			Any  		=	kwargs.pop("torchscript"       ,						self.torchscript)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	kwargs.pop("torch_xla_tpu_print_metrics"       ,						self.torch_xla_tpu_print_metrics)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	kwargs.pop("fp16_opt_level"       ,						self.fpaa_opt_level)
									super().__init__(**lowerCAmelCase__)

			_UpperCAmelCase       :							bool      							=					field(default=UpperCAmelCase_		,	metadata={'''help''': '''Trace the models using torchscript'''}     )
			_UpperCAmelCase       :							bool      							=					field(default=UpperCAmelCase_		,	metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}     )
			_UpperCAmelCase       :							str      							=					field(
			    default='''O1'''		,	metadata={
			        '''help''': (
			            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
			            '''See details at https://nvidia.github.io/apex/amp.html'''
			        )
			    }		,	)



			@cached_property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									requires_backends(self       ,						["torch"])
									logger.info("PyTorch: setting up devices")
									if not self.cuda:
															SCREAMING_SNAKE_CASE_:			int  		=	torch.device("cpu")
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	0
									elif is_torch_tpu_available():
															SCREAMING_SNAKE_CASE_:			int  		=	xm.xla_device()
															SCREAMING_SNAKE_CASE_:			int  		=	0
									else:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.device("cuda" if torch.cuda.is_available() else "cpu")
															SCREAMING_SNAKE_CASE_:			Dict  		=	torch.cuda.device_count()
									return device, n_gpu



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									return is_torch_tpu_available() and self.tpu



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									requires_backends(self       ,						["torch"])
									# TODO(PVP): currently only single GPU is supported
									return torch.cuda.current_device()



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									requires_backends(self       ,						["torch"])
									return self._setup_devices[0]



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									requires_backends(self       ,						["torch"])
									return self._setup_devices[1]




			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self.n_gpu > 0









# 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   )






