"""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
#
########################################################################


lowercase__ 	=     16
lowercase__ 	=     32







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE = 16       )     ->Any:
		a__:					List[Any]      							=     AutoTokenizer.from_pretrained('bert-base-cased'       )
		a__:					Tuple      							=     load_dataset('glue'		,					'mrpc'       )

		def tokenize_function(_SCREAMING_SNAKE_CASE       ):
				# max_length=None => use the model max length (it's actually the default)
				a__:					int      							=     tokenizer(examples['sentence1']		,					examples['sentence2']		,					truncation=_SCREAMING_SNAKE_CASE		,					max_length=_SCREAMING_SNAKE_CASE       )
				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():
				a__:					Optional[Any]      							=     datasets.map(
				    _SCREAMING_SNAKE_CASE		,					batched=_SCREAMING_SNAKE_CASE		,					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
		a__:					Any      							=     tokenized_datasets.rename_column('label'		,					'labels'       )

		def collate_fn(_SCREAMING_SNAKE_CASE       ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				a__:					Union[str, Any]      							=     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":
						a__:					Dict      							=     16
				elif accelerator.mixed_precision != "no":
						a__:					List[Any]      							=     8
				else:
						a__:					List[Any]      							=     None

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

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

		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

	lowercase__ 	=     mocked_dataloaders  # noqa: F811







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->int:
		# For testing only
		if os.environ.get('TESTING_MOCKED_DATALOADERS'		,					_SCREAMING_SNAKE_CASE       ) == "1":
				a__:					Union[str, Any]      							=     2
		# New Code #
		a__:					Any      							=     int(args.gradient_accumulation_steps       )
		# Initialize accelerator
		a__:					str      							=     Accelerator(
		    cpu=args.cpu		,					mixed_precision=args.mixed_precision		,					gradient_accumulation_steps=_SCREAMING_SNAKE_CASE       )
		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
		a__:					Any      							=     config['lr']
		a__:					Dict      							=     int(config['num_epochs']       )
		a__:					Dict      							=     int(config['seed']       )
		a__:					Union[str, Any]      							=     int(config['batch_size']       )

		a__:					Optional[Any]      							=     evaluate.load('glue'		,					'mrpc'       )

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

		# 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).
		a__:					List[Any]      							=     model.to(accelerator.device       )

		# Instantiate optimizer
		a__:					Union[str, Any]      							=     AdamW(params=model.parameters()		,					lr=_SCREAMING_SNAKE_CASE       )

		# Instantiate scheduler
		a__:					Union[str, Any]      							=     get_linear_schedule_with_warmup(
		    optimizer=_SCREAMING_SNAKE_CASE		,					num_warmup_steps=100		,					num_training_steps=(len(_SCREAMING_SNAKE_CASE       ) * 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.
		a__:					List[Any]      							=     accelerator.prepare(
		    _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

		# Now we train the model
		for epoch in range(_SCREAMING_SNAKE_CASE       ):
				model.train()
				for step, batch in enumerate(_SCREAMING_SNAKE_CASE       ):
						# 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(_SCREAMING_SNAKE_CASE       ):
								a__:					List[Any]      							=     model(**_SCREAMING_SNAKE_CASE       )
								a__:					int      							=     output.loss
								accelerator.backward(_SCREAMING_SNAKE_CASE       )
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

				a__:					Union[str, Any]      							=     metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'epoch {epoch}:'		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
		a__:					Optional[int]      							=     argparse.ArgumentParser(description='Simple example of training script.'       )
		parser.add_argument(
		    '--mixed_precision'		,					type=_SCREAMING_SNAKE_CASE		,					default=_SCREAMING_SNAKE_CASE		,					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=_SCREAMING_SNAKE_CASE		,					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.'       )
		a__:					List[str]      							=     parser.parse_args()
		a__:					Union[str, Any]      							=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	main()



"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowercase__ 	=     [
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]




class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      lowercase = None)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     None
        a__:					Optional[Any]      							=     os.path.abspath(os.path.join('examples'   ,      'by_feature'))
        a__:					Optional[Any]      							=     os.path.abspath('examples')
        for item in os.listdir(lowercase):
          if item not in EXCLUDE_EXAMPLES:
            a__:					Dict      							=     os.path.join(lowercase   ,      lowercase)
            if os.path.isfile(lowercase) and ".py" in item_path:
              with self.subTest(
                  tested_script=lowercase   ,      feature_script=lowercase   ,      tested_section='main()' if parser_only else 'training_function()'   ,      ):
                a__:					List[Any]      							=     compare_against_test(
                    os.path.join(lowercase   ,      lowercase)   ,      lowercase   ,      lowercase   ,      lowercase)
                a__:					Dict      							=     '\n'.join(lowercase)
                if special_strings is not None:
                  for string in special_strings:
                    a__:					Union[str, Any]      							=     diff.replace(lowercase   ,      '')
                self.assertEqual(lowercase   ,      '')



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        self.one_complete_example('complete_nlp_example.py'   ,      lowercase)
        self.one_complete_example('complete_nlp_example.py'   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Dict      							=     os.path.abspath(os.path.join('examples'   ,      'cv_example.py'))
        a__:					Tuple      							=     [
            ' ' * 16 + '{\n\n',
            ' ' * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
            ' ' * 20 + '"f1": eval_metric["f1"],\n\n',
            ' ' * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
            ' ' * 20 + '"epoch": epoch,\n\n',
            ' ' * 16 + '},\n\n',
            ' ' * 16 + 'step=epoch,\n',
            ' ' * 12,
            ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
        ]
        self.one_complete_example('complete_cv_example.py'   ,      lowercase   ,      lowercase   ,      lowercase)
        self.one_complete_example('complete_cv_example.py'   ,      lowercase   ,      lowercase   ,      lowercase)







@mock.patch.dict(os.environ	,				{"""TESTING_MOCKED_DATALOADERS""": """1"""}    )
class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		False



      @classmethod
      def   lowerCamelCase_       (						cls)     ->      List[str]:





        '''simple docstring'''





        super().setUpClass()
        a__:					Dict      							=     tempfile.mkdtemp()
        a__:					Optional[int]      							=     os.path.join(cls._tmpdir   ,      'default_config.yml')

        write_basic_config(save_location=cls.configPath)
        a__:					List[str]      							=     ['accelerate', 'launch', '--config_file', cls.configPath]



      @classmethod
      def   lowerCamelCase_       (						cls)     ->      List[str]:





        '''simple docstring'''





        super().tearDownClass()
        shutil.rmtree(cls._tmpdir)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
        run_command(self._launch_args + testargs)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir   ,      'epoch_0')))



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Any      							=     f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
        a__:					Tuple      							=     run_command(self._launch_args + testargs)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir   ,      'step_2')))



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Optional[Any]      							=     f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir   ,      "epoch_0")}\n        '.split()
        a__:					Optional[int]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
        self.assertNotIn('epoch 0:'   ,      lowercase)
        self.assertIn('epoch 1:'   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir   ,      "step_2")}\n        '.split()
        a__:					List[str]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
        if torch.cuda.is_available():
          a__:					Union[str, Any]      							=     torch.cuda.device_count()
        else:
          a__:					Optional[int]      							=     1
        if num_processes > 1:
          self.assertNotIn('epoch 0:'   ,      lowercase)
          self.assertIn('epoch 1:'   ,      lowercase)
        else:
          self.assertIn('epoch 0:'   ,      lowercase)
          self.assertIn('epoch 1:'   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Dict      							=     '\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
        with mock.patch.dict(os.environ   ,      {'TESTING_MOCKED_DATALOADERS': '0'}):
          a__:					Union[str, Any]      							=     run_command(self._launch_args + testargs   ,      return_stdout=lowercase)
          a__:					Union[str, Any]      							=     re.findall('({.+})'   ,      lowercase)
          a__:					Dict      							=     [r for r in results if 'accuracy' in r][-1]
          a__:					Optional[Any]      							=     ast.literal_eval(lowercase)
          self.assertGreaterEqual(results['accuracy']   ,      0.75)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					str      							=     ['examples/by_feature/multi_process_metrics.py']
        run_command(self._launch_args + testargs)



      @require_trackers
      @mock.patch.dict(os.environ   ,      {'WANDB_MODE': 'offline'})
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        with tempfile.TemporaryDirectory() as tmpdir:
          a__:					List[str]      							=     f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
          run_command(self._launch_args + testargs)
          self.assertTrue(os.path.exists(os.path.join(lowercase   ,      'tracking')))



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					int      							=     ['examples/by_feature/gradient_accumulation.py']
        run_command(self._launch_args + testargs)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     ['examples/by_feature/local_sgd.py']
        run_command(self._launch_args + testargs)


