'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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_     :   Tuple 				=				1_6
lowercase_     :   Optional[Any] 				=				3_2







def 			A__      (	snake_case_ :		Accelerator       ,				snake_case_ :		int = 16      ):
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      AutoTokenizer.from_pretrained('''bert-base-cased'''      )
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      load_dataset('''glue'''       ,				'''mrpc'''      )

 def tokenize_function(snake_case_ :		Optional[int]      ):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE__:   int=      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
 # starting with the main process first:
 with accelerator.main_process_first():
  SCREAMING_SNAKE_CASE__:   str=      datasets.map(
      __UpperCamelCase       ,				batched=__UpperCamelCase       ,				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
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      tokenized_datasets.rename_column('''label'''       ,				'''labels'''      )

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

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

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE__:   Any=      DataLoader(
     tokenized_datasets['''train''']       ,				shuffle=__UpperCamelCase       ,				collate_fn=__UpperCamelCase       ,				batch_size=__UpperCamelCase      )
 SCREAMING_SNAKE_CASE__:   str=      DataLoader(
     tokenized_datasets['''validation''']       ,				shuffle=__UpperCamelCase       ,				collate_fn=__UpperCamelCase       ,				batch_size=__UpperCamelCase      )

 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_     :   Optional[int] 				=				mocked_dataloaders  # noqa: F811







def 			A__      (	snake_case_ :		Tuple       ,				snake_case_ :		Optional[int]      ):







 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,				__UpperCamelCase      ) == "1":
  SCREAMING_SNAKE_CASE__:   Tuple=      2
 # New Code #
 SCREAMING_SNAKE_CASE__:   Optional[int]=      int(args.gradient_accumulation_steps      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      int(args.local_sgd_steps      )
 # Initialize accelerator
 SCREAMING_SNAKE_CASE__:   Tuple=      Accelerator(
     cpu=args.cpu       ,				mixed_precision=args.mixed_precision       ,				gradient_accumulation_steps=__UpperCamelCase      )
 if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
  raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''      )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 SCREAMING_SNAKE_CASE__:   Tuple=      config['''lr''']
 SCREAMING_SNAKE_CASE__:   List[Any]=      int(config['''num_epochs''']      )
 SCREAMING_SNAKE_CASE__:   int=      int(config['''seed''']      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      int(config['''batch_size''']      )

 SCREAMING_SNAKE_CASE__:   List[Any]=      evaluate.load('''glue'''       ,				'''mrpc'''      )

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

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

 # Instantiate optimizer
 SCREAMING_SNAKE_CASE__:   Dict=      AdamW(params=model.parameters()       ,				lr=__UpperCamelCase      )

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE__:   Optional[int]=      get_linear_schedule_with_warmup(
     optimizer=__UpperCamelCase       ,				num_warmup_steps=100       ,				num_training_steps=(len(__UpperCamelCase      ) * 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.
 SCREAMING_SNAKE_CASE__:   Dict=      accelerator.prepare(
     __UpperCamelCase       ,				__UpperCamelCase       ,				__UpperCamelCase       ,				__UpperCamelCase       ,				__UpperCamelCase      )

 # Now we train the model
 for epoch in range(__UpperCamelCase      ):
  model.train()
  with LocalSGD(
      accelerator=__UpperCamelCase       ,				model=__UpperCamelCase       ,				local_sgd_steps=__UpperCamelCase       ,				enabled=local_sgd_steps is not None      ) as local_sgd:
   for step, batch in enumerate(__UpperCamelCase      ):
    # 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(__UpperCamelCase      ):
     SCREAMING_SNAKE_CASE__:   Tuple=      model(**__UpperCamelCase      )
     SCREAMING_SNAKE_CASE__:   str=      output.loss
     accelerator.backward(__UpperCamelCase      )
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # LocalSGD-specific line
     local_sgd.step()

  model.eval()
  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():
    SCREAMING_SNAKE_CASE__:   str=      model(**__UpperCamelCase      )
   SCREAMING_SNAKE_CASE__:   Dict=      outputs.logits.argmax(dim=-1      )
   SCREAMING_SNAKE_CASE__:   int=      accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
   metric.add_batch(
       predictions=__UpperCamelCase       ,				references=__UpperCamelCase       ,				)

  SCREAMING_SNAKE_CASE__:   List[str]=      metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'epoch {epoch}:'       ,				__UpperCamelCase      )







def 			A__      (	):
 SCREAMING_SNAKE_CASE__:   List[Any]=      argparse.ArgumentParser(description='''Simple example of training script.'''      )
 parser.add_argument(
     '''--mixed_precision'''       ,				type=__UpperCamelCase       ,				default=__UpperCamelCase       ,				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=__UpperCamelCase       ,				default=1       ,				help='''The number of minibatches to be ran before gradients are accumulated.'''       ,				)
 parser.add_argument(
     '''--local_sgd_steps'''       ,				type=__UpperCamelCase       ,				default=8       ,				help='''Number of local SGD steps or None to disable local SGD'''      )
 parser.add_argument('''--cpu'''       ,				action='''store_true'''       ,				help='''If passed, will train on the CPU.'''      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      parser.parse_args()
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__UpperCamelCase       ,				__UpperCamelCase      )


if __name__ == "__main__":
      main()


import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[Any]=      [
         '''safety_checker/pytorch_model.bin''',
         '''safety_checker/model.safetensors''',
         '''vae/diffusion_pytorch_model.bin''',
         '''vae/diffusion_pytorch_model.safetensors''',
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         '''safety_checker/pytorch_model.bin''',
         '''safety_checker/model.safetensors''',
         '''vae/diffusion_pytorch_model.bin''',
         '''vae/diffusion_pytorch_model.safetensors''',
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
         '''unet/diffusion_pytorch_model.bin''',
         # Removed: 'unet/diffusion_pytorch_model.safetensors',
     ]
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   str=      [
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
     ]
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''safety_checker/pytorch_model.bin''',
         '''safety_checker/model.safetensors''',
         '''vae/diffusion_pytorch_model.bin''',
         '''vae/diffusion_pytorch_model.safetensors''',
         '''text_encoder/pytorch_model.bin''',
         # Removed: 'text_encoder/model.safetensors',
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         '''safety_checker/pytorch_model.fp16.bin''',
         '''safety_checker/model.fp16.safetensors''',
         '''vae/diffusion_pytorch_model.fp16.bin''',
         '''vae/diffusion_pytorch_model.fp16.safetensors''',
         '''text_encoder/pytorch_model.fp16.bin''',
         '''text_encoder/model.fp16.safetensors''',
         '''unet/diffusion_pytorch_model.fp16.bin''',
         '''unet/diffusion_pytorch_model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Any=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''unet/diffusion_pytorch_model.fp16.bin''',
         '''unet/diffusion_pytorch_model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Dict=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     # pass variant but use the non-variant filenames
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      [
         '''safety_checker/pytorch_model.fp16.bin''',
         '''safety_checker/model.fp16.safetensors''',
         '''vae/diffusion_pytorch_model.fp16.bin''',
         '''vae/diffusion_pytorch_model.fp16.safetensors''',
         '''text_encoder/pytorch_model.fp16.bin''',
         '''text_encoder/model.fp16.safetensors''',
         '''unet/diffusion_pytorch_model.fp16.bin''',
         # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
     ]
     SCREAMING_SNAKE_CASE__:   List[Any]=      '''fp16'''
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   int=      [
         '''text_encoder/pytorch_model.fp16.bin''',
         '''text_encoder/model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Any=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     # pass variant but use the non-variant filenames
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   str=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      [
         '''safety_checker/pytorch_model.fp16.bin''',
         '''safety_checker/model.fp16.safetensors''',
         '''vae/diffusion_pytorch_model.fp16.bin''',
         '''vae/diffusion_pytorch_model.fp16.safetensors''',
         '''text_encoder/pytorch_model.fp16.bin''',
         # 'text_encoder/model.fp16.safetensors',
         '''unet/diffusion_pytorch_model.fp16.bin''',
         '''unet/diffusion_pytorch_model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   str=      '''fp16'''
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )

