


# 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


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

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[Any]=None     ):







 """simple docstring"""



 if subparsers is not None:
  UpperCamelCase__					:    Union[str, Any]          =   subparsers.add_parser('''tpu-config'''    , description=_description     )
 else:
  UpperCamelCase__					:    Any          =   argparse.ArgumentParser('''Accelerate tpu-config command'''    , description=_description     )
 # Core arguments
 UpperCamelCase__					:    str          =   parser.add_argument_group(
     '''Config Arguments'''    , '''Arguments that can be configured through `accelerate config`.'''     )
 config_args.add_argument(
     '''--config_file'''    , type=SCREAMING_SNAKE_CASE    , default=SCREAMING_SNAKE_CASE    , help='''Path to the config file to use for accelerate.'''    , )
 config_args.add_argument(
     '''--tpu_name'''    , default=SCREAMING_SNAKE_CASE    , 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=SCREAMING_SNAKE_CASE    , help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''    , )
 UpperCamelCase__					:    Optional[Any]          =   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=SCREAMING_SNAKE_CASE    , 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=SCREAMING_SNAKE_CASE     )
 return parser

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]     ):







 """simple docstring"""



 UpperCamelCase__					:    Union[str, Any]          =   None

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

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

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






def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   tpu_command_parser()
 UpperCamelCase__					:    Tuple          =   parser.parse_args()

 tpu_command_launcher(SCREAMING_SNAKE_CASE     )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase       :       Optional[int]						       =				{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "RagModel",
           "RagPreTrainedModel",
           "RagSequenceForGeneration",
           "RagTokenForGeneration",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       List[Any]						       =				[
           "TFRagModel",
           "TFRagPreTrainedModel",
           "TFRagSequenceForGeneration",
           "TFRagTokenForGeneration",
       ]


if TYPE_CHECKING:
       from .configuration_rag import RagConfig
       from .retrieval_rag import RagRetriever
       from .tokenization_rag import RagTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rag import (
                  TFRagModel,
                  TFRagPreTrainedModel,
                  TFRagSequenceForGeneration,
                  TFRagTokenForGeneration,
              )

else:
       import sys

       __UpperCamelCase       :       List[Any]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


