

'''simple docstring'''







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowerCamelCase									=     16
__lowerCamelCase									=     32
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__ = 16,							UpperCAmelCase__ = "bert-base-cased"					)      ->      Tuple:
       A_						       =   AutoTokenizer.from_pretrained(UpperCAmelCase__					)
       A_						       =   load_dataset("""glue""",							"""mrpc"""					)

       def tokenize_function(UpperCAmelCase__					):
              # max_length=None => use the model max length (it's actually the default)
              A_						       =   tokenizer(examples["""sentence1"""],							examples["""sentence2"""],							truncation=UpperCAmelCase__,							max_length=UpperCAmelCase__					)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       A_						       =   datasets.map(
           UpperCAmelCase__,							batched=UpperCAmelCase__,							remove_columns=["""idx""", """sentence1""", """sentence2"""],							load_from_cache_file=UpperCAmelCase__					)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A_						       =   tokenized_datasets.rename_column("""label""",							"""labels"""					)

       def collate_fn(UpperCAmelCase__					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(UpperCAmelCase__,							padding="""max_length""",							max_length=1_28,							return_tensors="""pt"""					)
              return tokenizer.pad(UpperCAmelCase__,							padding="""longest""",							return_tensors="""pt"""					)

       # Instantiate dataloaders.
       A_						       =   DataLoader(
           tokenized_datasets["""train"""],							shuffle=UpperCAmelCase__,							collate_fn=UpperCAmelCase__,							batch_size=UpperCAmelCase__					)
       A_						       =   DataLoader(
           tokenized_datasets["""validation"""],							shuffle=UpperCAmelCase__,							collate_fn=UpperCAmelCase__,							batch_size=UpperCAmelCase__					)

       return train_dataloader, eval_dataloader
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      str:
       # Initialize accelerator
       A_						       =   Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A_						       =   config["""lr"""]
       A_						       =   int(config["""num_epochs"""]					)
       A_						       =   int(config["""seed"""]					)
       A_						       =   int(config["""batch_size"""]					)
       A_						       =   args.model_name_or_path

       set_seed(UpperCAmelCase__					)
       A_       ,      A_						       =   get_dataloaders(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A_						       =   AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__,							return_dict=UpperCAmelCase__					)

       # Instantiate optimizer
       A_						       =   (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A_						       =   optimizer_cls(params=model.parameters(),							lr=UpperCAmelCase__					)

       if accelerator.state.deepspeed_plugin is not None:
              A_						       =   accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              A_						       =   1
       A_						       =   (len(UpperCAmelCase__					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A_						       =   get_linear_schedule_with_warmup(
                  optimizer=UpperCAmelCase__,							num_warmup_steps=0,							num_training_steps=UpperCAmelCase__,							)
       else:
              A_						       =   DummyScheduler(UpperCAmelCase__,							total_num_steps=UpperCAmelCase__,							warmup_num_steps=0					)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       A_       ,      A_       ,      A_       ,      A_       ,      A_						       =   accelerator.prepare(
           UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)

       # We need to keep track of how many total steps we have iterated over
       A_						       =   0
       # We also need to keep track of the stating epoch so files are named properly
       A_						       =   0

       # Now we train the model
       A_						       =   evaluate.load("""glue""",							"""mrpc"""					)
       A_						       =   0
       A_						       =   {}
       for epoch in range(UpperCAmelCase__,							UpperCAmelCase__					):
              model.train()
              for step, batch in enumerate(UpperCAmelCase__					):
                     A_						       =   model(**UpperCAmelCase__					)
                     A_						       =   outputs.loss
                     A_						       =   loss / gradient_accumulation_steps
                     accelerator.backward(UpperCAmelCase__					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              A_						       =   0
              for step, batch in enumerate(UpperCAmelCase__					):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device					)
                     with torch.no_grad():
                            A_						       =   model(**UpperCAmelCase__					)
                     A_						       =   outputs.logits.argmax(dim=-1					)
                     # It is slightly faster to call this once, than multiple times
                     A_       ,      A_						       =   accelerator.gather(
                         (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(UpperCAmelCase__					) - 1:
                                   A_						       =   predictions[: len(eval_dataloader.dataset					) - samples_seen]
                                   A_						       =   references[: len(eval_dataloader.dataset					) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=UpperCAmelCase__,							references=UpperCAmelCase__,							)

              A_						       =   metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:''',							UpperCAmelCase__					)
              A_						       =   eval_metric["""accuracy"""]

              if best_performance < eval_metric["accuracy"]:
                     A_						       =   eval_metric["""accuracy"""]

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir,							"""all_results.json"""					),							"""w"""					) as f:
                     json.dump(UpperCAmelCase__,							UpperCAmelCase__					)
def    UpperCAmelCase__     (   )      ->      List[Any]:
       A_						       =   argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
       parser.add_argument(
           """--model_name_or_path""",							type=UpperCAmelCase__,							default="""bert-base-cased""",							help="""Path to pretrained model or model identifier from huggingface.co/models.""",							required=UpperCAmelCase__,							)
       parser.add_argument(
           """--output_dir""",							type=UpperCAmelCase__,							default=""".""",							help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""",							)
       parser.add_argument(
           """--performance_lower_bound""",							type=UpperCAmelCase__,							default=UpperCAmelCase__,							help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""",							)
       parser.add_argument(
           """--num_epochs""",							type=UpperCAmelCase__,							default=3,							help="""Number of train epochs.""",							)
       A_						       =   parser.parse_args()
       A_						       =   {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(UpperCAmelCase__,							UpperCAmelCase__					)


if __name__ == "__main__":
      main()



'''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


__lowerCamelCase									=     '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def    UpperCAmelCase__     (   UpperCAmelCase__=None					)      ->      List[str]:
       if subparsers is not None:
              A_						       =   subparsers.add_parser("""tpu-config""",							description=_description					)
       else:
              A_						       =   argparse.ArgumentParser("""Accelerate tpu-config command""",							description=_description					)
       # Core arguments
       A_						       =   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.""",							)
       A_						       =   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    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   None

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

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

       # Then send it to gcloud
       # Eventually try to use google-api-core to do this instead of subprocess
       A_						       =   ["""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    UpperCAmelCase__     (   )      ->      int:
       A_						       =   tpu_command_parser()
       A_						       =   parser.parse_args()

       tpu_command_launcher(UpperCAmelCase__					)
