





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 							SCREAMING_SNAKE_CASE__						(    __a=None   ):
 if subparsers is not None:
  snake_case_		:			Dict											=	subparsers.add_parser('tpu-config'   , description=_description   )
 else:
  snake_case_		:			str											=	argparse.ArgumentParser('Accelerate tpu-config command'   , description=_description   )
 # Core arguments
 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.'   , )
 snake_case_		:			List[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=__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 							SCREAMING_SNAKE_CASE__						(    __a   ):
 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   ):
  snake_case_		:			Optional[Any]											=	load_config_from_file(args.config_file   )
  if not args.command_file and defaults.command_file is not None and not args.command:
   snake_case_		:			Optional[int]											=	defaults.command_file
  if not args.command and defaults.commands is not None:
   snake_case_		:			str											=	defaults.commands
  if not args.tpu_name:
   snake_case_		:			str											=	defaults.tpu_name
  if not args.tpu_zone:
   snake_case_		:			Optional[Any]											=	defaults.tpu_zone
 if args.accelerate_version == "dev":
  snake_case_		:			Tuple											=	'git+https://github.com/huggingface/accelerate.git'
 elif args.accelerate_version == "latest":
  snake_case_		:			Dict											=	'accelerate -U'
 elif isinstance(parse(args.accelerate_version   )   , __lowerCAmelCase   ):
  snake_case_		:			int											=	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:
   snake_case_		:			List[Any]											=	[f.read().splitlines()]

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

 # Then send it to gcloud
 # Eventually try to use google-api-core to do this instead of subprocess
 snake_case_		:			Union[str, 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 							SCREAMING_SNAKE_CASE__						(    ):
 snake_case_		:			Union[str, Any]											=	tpu_command_parser()
 snake_case_		:			Union[str, Any]											=	parser.parse_args()

 tpu_command_launcher(__lowerCAmelCase   )








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_SCREAMING_SNAKE_CASE      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

							snake_case_		:			Optional[int]											=	tokenizer(_A		,   return_tensors='pt'		,   truncation=_A		,   padding='longest'	).to(_A	)
							snake_case_		:			List[Any]											=	model.generate(
							    input_ids=batch.input_ids		,   num_beams=8		,   )
							snake_case_		:			Dict											=	tokenizer.batch_decode(
							    _A		,   skip_special_tokens=_A		,   clean_up_tokenization_spaces=_A	)
							snake_case_		:			Tuple											=	calculate_bleu(_A		,   _A	)
							print(_A	)
							self.assertGreaterEqual(scores['bleu']		,   _A	)

