



'''simple docstring'''



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


SCREAMING_SNAKE_CASE						=	'Run commands across TPU VMs for initial setup before running `accelerate launch`.'


def 							lowercase_				(    __A :       str=None			)       ->      List[str]:



 """simple docstring"""




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


def 							lowercase_				(    __A :       Dict			)       ->      List[str]:



 """simple docstring"""




 lowercase					:   List[str]				  =None

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

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

 # Then send it to gcloud
 # Eventually try to use google-api-core to do this instead of subprocess
 lowercase					:   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(__A			)}'			)
  return
 subprocess.run(__A			)
 print('''Successfully setup pod.'''			)





def 							lowercase_				(    )       ->      Any:



 """simple docstring"""




 lowercase					:   str				  =tpu_command_parser()
 lowercase					:   Any				  =parser.parse_args()

 tpu_command_launcher(__A			)











'''simple docstring'''



import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      @require_torch
      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Any				  ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       lowercase					:   Optional[int]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       lowercase					:   Any				  ='''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       lowercase					:   Tuple				  ='''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(UpperCAmelCase		)
       BertModel.from_pretrained(UpperCAmelCase		)
       BertTokenizer.from_pretrained(UpperCAmelCase		)
       pipeline(task='''fill-mask'''     ,      model=UpperCAmelCase		)

       # baseline - just load from_pretrained with normal network
       lowercase					:   List[str]				  =[sys.executable, '''-c''', '''\n'''.join([load, run, mock]		)]

       # should succeed
       lowercase					:   Tuple				  =self.get_env()
       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   Optional[Any]				  ='''1'''
       lowercase					:   Any				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		str		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  ='''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        '''

       lowercase					:   Optional[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
        '''

       lowercase					:   Optional[int]				  ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
        '''

       # Force fetching the files so that we can use the cache
       lowercase					:   Optional[Any]				  ='''hf-internal-testing/tiny-random-bert'''
       BertConfig.from_pretrained(UpperCAmelCase		)
       BertModel.from_pretrained(UpperCAmelCase		)
       BertTokenizer.from_pretrained(UpperCAmelCase		)
       pipeline(task='''fill-mask'''     ,      model=UpperCAmelCase		)

       # baseline - just load from_pretrained with normal network
       lowercase					:   Optional[Any]				  =[sys.executable, '''-c''', '''\n'''.join([load, run, mock]		)]

       # should succeed
       lowercase					:   str				  =self.get_env()
       lowercase					:   Any				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		Any		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  ='''
from transformers import BertConfig, BertModel, BertTokenizer
        '''

       lowercase					:   List[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
        '''

       lowercase					:   int				  ='''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
        '''

       # baseline - just load from_pretrained with normal network
       lowercase					:   Tuple				  =[sys.executable, '''-c''', '''\n'''.join([load, run]		)]

       # should succeed
       lowercase					:   Optional[Any]				  =self.get_env()
       lowercase					:   List[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)

       # next emulate no network
       lowercase					:   Tuple				  =[sys.executable, '''-c''', '''\n'''.join([load, mock, run]		)]

       # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
       # env["TRANSFORMERS_OFFLINE"] = "0"
       # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
       # self.assertEqual(result.returncode, 1, result.stderr)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   Any				  ='''1'''
       lowercase					:   Optional[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)






      @require_torch
      def 				A__      ( self			:		Tuple		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  ='''
from transformers import pipeline
        '''
       lowercase					:   List[Any]				  ='''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
        '''

       lowercase					:   Tuple				  ='''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
        '''
       lowercase					:   Tuple				  =self.get_env()
       lowercase					:   Optional[int]				  ='''1'''
       lowercase					:   Union[str, Any]				  =[sys.executable, '''-c''', '''\n'''.join([load, mock, run]		)]
       lowercase					:   Dict				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      1     ,      result.stderr		)
       self.assertIn(
           '''You cannot infer task automatically within `pipeline` when using offline mode'''     ,      result.stderr.decode().replace('''\n'''     ,      ''''''		)     ,      )






      @require_torch
      def 				A__      ( self			:		int		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[str]				  ='''
from transformers import AutoModel
        '''
       lowercase					:   Dict				  ='''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
        '''

       # baseline - just load from_pretrained with normal network
       lowercase					:   Dict				  =[sys.executable, '''-c''', '''\n'''.join([load, run]		)]

       # should succeed
       lowercase					:   Optional[Any]				  =self.get_env()
       lowercase					:   int				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)

       # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
       lowercase					:   List[str]				  ='''1'''
       lowercase					:   List[Any]				  =subprocess.run(UpperCAmelCase     ,      env=UpperCAmelCase     ,      check=UpperCAmelCase     ,      capture_output=UpperCAmelCase		)
       self.assertEqual(result.returncode     ,      0     ,      result.stderr		)
       self.assertIn('''success'''     ,      result.stdout.decode()		)



