


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
#
########################################################################


UpperCAmelCase__   	=							16
UpperCAmelCase__   	=							32
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case = 16						)     ->			str:







 """simple docstring"""


 _lowercase							      =AutoTokenizer.from_pretrained('''bert-base-cased'''						)
 _lowercase							      =load_dataset('''glue'''     ,						'''mrpc'''						)

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

 def collate_fn(__snake_case						):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  _lowercase							      =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							      =16
  elif accelerator.mixed_precision != "no":
   _lowercase							      =8
  else:
   _lowercase							      =None

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

 # Instantiate dataloaders.
 _lowercase							      =DataLoader(
     tokenized_datasets['''train''']     ,						shuffle=__snake_case     ,						collate_fn=__snake_case     ,						batch_size=__snake_case						)
 _lowercase							      =DataLoader(
     tokenized_datasets['''validation''']     ,						shuffle=__snake_case     ,						collate_fn=__snake_case     ,						batch_size=__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

  UpperCAmelCase__   	=							mocked_dataloaders  # noqa: F811
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,						__snake_case						) == "1":
  _lowercase							      =2
 # New Code #
 _lowercase							      =int(args.gradient_accumulation_steps						)
 _lowercase							      =int(args.local_sgd_steps						)
 # Initialize accelerator
 _lowercase							      =Accelerator(
     cpu=args.cpu     ,						mixed_precision=args.mixed_precision     ,						gradient_accumulation_steps=__snake_case						)
 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
 _lowercase							      =config['''lr''']
 _lowercase							      =int(config['''num_epochs''']						)
 _lowercase							      =int(config['''seed''']						)
 _lowercase							      =int(config['''batch_size''']						)

 _lowercase							      =evaluate.load('''glue'''     ,						'''mrpc'''						)

 set_seed(__snake_case						)
 _lowercase      ,    _lowercase							      =get_dataloaders(__snake_case     ,						__snake_case						)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _lowercase							      =AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,						return_dict=__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).
 _lowercase							      =model.to(accelerator.device						)

 # Instantiate optimizer
 _lowercase							      =AdamW(params=model.parameters()     ,						lr=__snake_case						)

 # Instantiate scheduler
 _lowercase							      =get_linear_schedule_with_warmup(
     optimizer=__snake_case     ,						num_warmup_steps=100     ,						num_training_steps=(len(__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.
 _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase							      =accelerator.prepare(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)

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

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

  _lowercase							      =metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"     ,						__snake_case						)
def 				UpperCAmelCase_					(							)     ->			Optional[int]:







 """simple docstring"""


 _lowercase							      =argparse.ArgumentParser(description='''Simple example of training script.'''						)
 parser.add_argument(
     '''--mixed_precision'''     ,						type=__snake_case     ,						default=__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=__snake_case     ,						default=1     ,						help='''The number of minibatches to be ran before gradients are accumulated.'''     ,						)
 parser.add_argument(
     '''--local_sgd_steps'''     ,						type=__snake_case     ,						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.'''						)
 _lowercase							      =parser.parse_args()
 _lowercase							      ={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  main()





from typing import Any
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			list:







 """simple docstring"""


 _validation(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 # Creates data structures and fill initial step
 _lowercase							      ={}
 _lowercase							      ={}
 for state in states_space:
  _lowercase							      =observations_space[0]
  _lowercase							      =(
      initial_probabilities[state] * emission_probabilities[state][observation]
  )
  _lowercase							      =None

 # Fills the data structure with the probabilities of
 # different transitions and pointers to previous states
 for o in range(1     ,						len(__snake_case						)						):
  _lowercase							      =observations_space[o]
  _lowercase							      =observations_space[o - 1]
  for state in states_space:
   # Calculates the argmax for probability function
   _lowercase							      =''''''
   _lowercase							      =-1
   for k_state in states_space:
    _lowercase							      =(
        probabilities[(k_state, prior_observation)]
        * transition_probabilities[k_state][state]
        * emission_probabilities[state][observation]
    )
    if probability > max_probability:
     _lowercase							      =probability
     _lowercase							      =k_state

            # Update probabilities and pointers dicts
   _lowercase							      =(
       probabilities[(arg_max, prior_observation)]
       * transition_probabilities[arg_max][state]
       * emission_probabilities[state][observation]
   )

   _lowercase							      =arg_max

    # The final observation
 _lowercase							      =observations_space[len(__snake_case						) - 1]

 # argmax for given final observation
 _lowercase							      =''''''
 _lowercase							      =-1
 for k_state in states_space:
  _lowercase							      =probabilities[(k_state, final_observation)]
  if probability > max_probability:
   _lowercase							      =probability
   _lowercase							      =k_state
 _lowercase							      =arg_max

 # Process pointers backwards
 _lowercase							      =last_state
 _lowercase							      =[]
 for o in range(len(__snake_case						) - 1     ,						-1     ,						-1						):
  result.append(__snake_case						)
  _lowercase							      =pointers[previous, observations_space[o]]
 result.reverse()

 return result
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_not_empty(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)
 _validate_lists(__snake_case     ,						__snake_case						)
 _validate_dicts(
     __snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 if not all(
     [
         observations_space,
         states_space,
         initial_probabilities,
         transition_probabilities,
         emission_probabilities,
     ]						):
  raise ValueError('''There\'s an empty parameter'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_list(__snake_case     ,						'''observations_space'''						)
 _validate_list(__snake_case     ,						'''states_space'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a list"
  raise ValueError(__snake_case						)
 else:
  for x in _object:
   if not isinstance(__snake_case     ,						__snake_case						):
    _lowercase							      =F"{var_name} must be a list of strings"
    raise ValueError(__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						)     ->			None:







 """simple docstring"""


 _validate_dict(__snake_case     ,						'''initial_probabilities'''     ,						__snake_case						)
 _validate_nested_dict(__snake_case     ,						'''transition_probabilities'''						)
 _validate_nested_dict(__snake_case     ,						'''emission_probabilities'''						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			None:







 """simple docstring"""


 _validate_dict(_object     ,						__snake_case     ,						__snake_case						)
 for x in _object.values():
  _validate_dict(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case = False						)     ->			None:







 """simple docstring"""


 if not isinstance(_object     ,						__snake_case						):
  _lowercase							      =F"{var_name} must be a dict"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object						):
  _lowercase							      =F"{var_name} all keys must be strings"
  raise ValueError(__snake_case						)
 if not all(isinstance(__snake_case     ,						__snake_case						) for x in _object.values()						):
  _lowercase							      ='''nested dictionary ''' if nested else ''''''
  _lowercase							      =F"{var_name} {nested_text}all values must be {value_type.__name__}"
  raise ValueError(__snake_case						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()

