





'''simple docstring'''

# 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


lowercase__						:      List[str]	=    'Run commands across TPU VMs for initial setup before running `accelerate launch`.'







def  a__    (    lowercase :					List[str]=None				)		->		Optional[int]:





     """simple docstring"""

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







def  a__    (    lowercase :					Union[str, Any]				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase       =	None

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

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

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







def  a__    (    )		->		List[str]:





     """simple docstring"""

     _UpperCamelCase       =	tpu_command_parser()
     _UpperCamelCase       =	parser.parse_args()

     tpu_command_launcher(lowercase				)










'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__						:      Any	=    logging.getLogger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					Tuple				)		->		Any:





     """simple docstring"""

     return (preds == labels).mean()
@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,						)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()			)}			)
       _snake_case     :					str						    =   field(metadata={'help': 'Should contain the data files for the task.'}			)
       _snake_case     :					int						    =   field(
           default=1_2_8						,						metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }						,						)
       _snake_case     :					bool						    =   field(
           default=__magic_name__						,						metadata={'help': 'Overwrite the cached training and evaluation sets'}			)







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir				)
         and os.listdir(training_args.output_dir				)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
              ''' --overwrite_output_dir to overcome.'''				)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1				), training_args.fpaa, )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank				):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''', lowercase				)

     # Set seed
     set_seed(training_args.seed				)

     try:
          _UpperCamelCase       =	processors[data_args.task_name]()
          _UpperCamelCase       =	processor.get_labels()
          _UpperCamelCase       =	len(lowercase				)
     except KeyError:
          raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _UpperCamelCase       =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path				), config=lowercase, cache_dir=model_args.cache_dir, )

     # Get datasets
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
         if training_args.do_train
         else None
     )
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
         if training_args.do_eval
         else None
     )

     def compute_metrics(lowercase :					EvalPrediction				) -> Dict:
          _UpperCamelCase       =	np.argmax(p.predictions, axis=1				)
          return {"acc": simple_accuracy(lowercase, p.label_ids				)}

     # Data collator
     _UpperCamelCase       =	DataCollatorWithPadding(lowercase, pad_to_multiple_of=8				) if training_args.fpaa else None

     # Initialize our Trainer
     _UpperCamelCase       =	Trainer(
         model=lowercase, args=lowercase, train_dataset=lowercase, eval_dataset=lowercase, compute_metrics=lowercase, data_collator=lowercase, )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
     _UpperCamelCase       =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''				)

          _UpperCamelCase       =	trainer.evaluate()

          _UpperCamelCase       =	os.path.join(training_args.output_dir, '''eval_results.txt'''				)
          if trainer.is_world_master():
               with open(lowercase, '''w'''				) as writer:
                    logger.info('''***** Eval results *****'''				)
                    for key, value in result.items():
                         logger.info('''  %s = %s''', lowercase, lowercase				)
                         writer.write('''%s = %s\n''' % (key, value)				)

                    results.update(lowercase				)

     return results







def  a__    (    lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()



