"""simple docstring"""





import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCamelCase  :		Optional[Any]           =       16
_UpperCamelCase  :		Union[str, Any]           =       32





def 							a_	( _lowerCAmelCase    :					Accelerator  ,					_lowerCAmelCase    :					int = 16	):





       '''simple docstring'''

       lowercase__     :						Any         =      AutoTokenizer.from_pretrained('bert-base-cased'	)
       lowercase__     :						List[str]         =      load_dataset('glue'  ,					'mrpc'	)

       def tokenize_function(_lowerCAmelCase    :					Union[str, Any]	):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__     :						Optional[Any]         =      tokenizer(examples['sentence1']  ,					examples['sentence2']  ,					truncation=__A  ,					max_length=__A	)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              lowercase__     :						Tuple         =      datasets.map(
                  __A  ,					batched=__A  ,					remove_columns=['idx', 'sentence1', 'sentence2']  ,					)

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

       def collate_fn(_lowerCAmelCase    :					Any	):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase__     :						int         =      128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowercase__     :						Dict         =      16
              elif accelerator.mixed_precision != "no":
                     lowercase__     :						int         =      8
              else:
                     lowercase__     :						str         =      None

              return tokenizer.pad(
                  __A  ,					padding='longest'  ,					max_length=__A  ,					pad_to_multiple_of=__A  ,					return_tensors='pt'  ,					)

       # Instantiate dataloaders.
       lowercase__     :						int         =      DataLoader(
           tokenized_datasets['train']  ,					shuffle=__A  ,					collate_fn=__A  ,					batch_size=__A	)
       lowercase__     :						Any         =      DataLoader(
           tokenized_datasets['validation']  ,					shuffle=__A  ,					collate_fn=__A  ,					batch_size=__A	)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       _UpperCamelCase  :		Dict           =       mocked_dataloaders  # noqa: F811





def 							a_	( _lowerCAmelCase    :					Any  ,					_lowerCAmelCase    :					Optional[Any]	):





       '''simple docstring'''

       if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,					__A	) == "1":
              lowercase__     :						Dict         =      2
       # New Code #
       lowercase__     :						Optional[int]         =      int(args.gradient_accumulation_steps	)
       # Initialize accelerator
       lowercase__     :						int         =      Accelerator(
           cpu=args.cpu  ,					mixed_precision=args.mixed_precision  ,					gradient_accumulation_steps=__A	)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'	)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__     :						Tuple         =      config['lr']
       lowercase__     :						str         =      int(config['num_epochs']	)
       lowercase__     :						Optional[Any]         =      int(config['seed']	)
       lowercase__     :						Any         =      int(config['batch_size']	)

       lowercase__     :						Tuple         =      evaluate.load('glue'  ,					'mrpc'	)

       set_seed(__A	)
       lowercase__       , lowercase__     :						Any         =      get_dataloaders(__A  ,					__A	)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__     :						Optional[int]         =      AutoModelForSequenceClassification.from_pretrained('bert-base-cased'  ,					return_dict=__A	)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       lowercase__     :						List[str]         =      model.to(accelerator.device	)

       # Instantiate optimizer
       lowercase__     :						Optional[Any]         =      AdamW(params=model.parameters()  ,					lr=__A	)

       # Instantiate scheduler
       lowercase__     :						Union[str, Any]         =      get_linear_schedule_with_warmup(
           optimizer=__A  ,					num_warmup_steps=100  ,					num_training_steps=(len(__A	) * num_epochs)  ,					)

       # 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.
       lowercase__       , lowercase__       , lowercase__       , lowercase__       , lowercase__     :						Tuple         =      accelerator.prepare(
           __A  ,					__A  ,					__A  ,					__A  ,					__A	)

       # Now we train the model
       for epoch in range(__A	):
              model.train()
              for step, batch in enumerate(__A	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(__A	):
                            lowercase__     :						Dict         =      model(**__A	)
                            lowercase__     :						Dict         =      output.loss
                            accelerator.backward(__A	)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(__A	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     with torch.no_grad():
                            lowercase__     :						int         =      model(**__A	)
                     lowercase__     :						Dict         =      outputs.logits.argmax(dim=-1	)
                     lowercase__       , lowercase__     :						int         =      accelerator.gather_for_metrics((predictions, batch['labels'])	)
                     metric.add_batch(
                         predictions=__A  ,					references=__A  ,					)

              lowercase__     :						List[Any]         =      metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""  ,					__A	)





def 							a_	( ):





       '''simple docstring'''

       lowercase__     :						Optional[Any]         =      argparse.ArgumentParser(description='Simple example of training script.'	)
       parser.add_argument(
           '--mixed_precision'  ,					type=__A  ,					default=__A  ,					choices=['no', 'fp16', 'bf16', 'fp8']  ,					help='Whether to use mixed precision. Choose'
           'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
           'and an Nvidia Ampere GPU.'  ,					)
       # New Code #
       parser.add_argument(
           '--gradient_accumulation_steps'  ,					type=__A  ,					default=1  ,					help='The number of minibatches to be ran before gradients are accumulated.'  ,					)
       parser.add_argument('--cpu'  ,					action='store_true'  ,					help='If passed, will train on the CPU.'	)
       lowercase__     :						str         =      parser.parse_args()
       lowercase__     :						Dict         =      {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(__A  ,					__A	)


if __name__ == "__main__":
       main()




# Copyright 2023 The HuggingFace Inc. 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 re

from ..utils import cached_file


# docstyle-ignore
_lowerCamelCase      :					Optional[int]					    =      '''
Human: <<task>>

Assistant: '''


_lowerCamelCase      :					str					    =      '''huggingface-tools/default-prompts'''
_lowerCamelCase      :					Optional[int]					    =      {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}





def      A__					(			__A  :						Dict     ,				__A  :						Dict     ,				__A  :						Optional[Any]="run"			)					->Any:
      if prompt_or_repo_id is None:
            __A    =DEFAULT_PROMPTS_REPO

      # prompt is considered a repo ID when it does not contain any kind of space
      if re.search('''\\s'''     ,				__A			) is not None:
            return prompt_or_repo_id

      __A    =cached_file(
          __A     ,				PROMPT_FILES[mode]     ,				repo_type='''dataset'''     ,				user_agent={'''agent''': agent_name}			)
      with open(__A     ,				'''r'''     ,				encoding='''utf-8'''			) as f:
            return f.read()


