
'''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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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   A			(							A_				:       Accelerator     ,				A_				:       int = 16							):
						snake_case							:  Tuple			=     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						snake_case							:  List[str]			=     load_dataset('''glue'''     ,				'''mrpc'''							)

						def tokenize_function(A_				:       Dict							):
												# max_length=None => use the model max length (it's actually the default)
												snake_case							:  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():
												snake_case							:  Any			=     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
						snake_case							:  int			=     tokenized_datasets.rename_column('''label'''     ,				'''labels'''							)

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

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

						# Instantiate dataloaders.
						snake_case							:  Any			=     DataLoader(
						    tokenized_datasets['''train''']     ,				shuffle=A_     ,				collate_fn=A_     ,				batch_size=A_							)
						snake_case							:  Tuple			=     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            = mocked_dataloaders  # noqa: F811

def   A			(							A_				:       Optional[Any]     ,				A_				:       int							):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,				A_							) == "1":
												snake_case							:  List[str]			=     2
						# Initialize accelerator
						snake_case							:  Optional[int]			=     Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case							:  Dict			=     config['''lr''']
						snake_case							:  Dict			=     int(config['''num_epochs''']							)
						snake_case							:  str			=     int(config['''seed''']							)
						snake_case							:  Optional[Any]			=     int(config['''batch_size''']							)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps     ,				'''isdigit'''							):
												if args.checkpointing_steps == "epoch":
																		snake_case							:  int			=     args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case							:  Tuple			=     int(args.checkpointing_steps							)
												else:
																		raise ValueError(
																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""							)
						else:
												snake_case							:  Optional[int]			=     None

						set_seed(A_							)

						snake_case,       snake_case							:  str			=     get_dataloaders(A_     ,				A_							)
						snake_case							:  Any			=     evaluate.load('''glue'''     ,				'''mrpc'''							)

						# If the batch size is too big we use gradient accumulation
						snake_case							:  Optional[Any]			=     1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case							:  Dict			=     batch_size // MAX_GPU_BATCH_SIZE
												snake_case							:  List[str]			=     MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case							:  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).
						snake_case							:  List[str]			=     model.to(accelerator.device							)

						# Instantiate optimizer
						snake_case							:  Tuple			=     AdamW(params=model.parameters()     ,				lr=A_							)

						# Instantiate scheduler
						snake_case							:  Any			=     get_linear_schedule_with_warmup(
						    optimizer=A_     ,				num_warmup_steps=100     ,				num_training_steps=(len(A_							) * num_epochs) // gradient_accumulation_steps     ,				)

						# 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.
						snake_case,       snake_case,       snake_case,       snake_case,       snake_case							:  str			=     accelerator.prepare(
						    A_     ,				A_     ,				A_     ,				A_     ,				A_							)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						snake_case							:  List[Any]			=     0
						# We also need to keep track of the stating epoch so files are named properly
						snake_case							:  List[str]			=     0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""							)
																		accelerator.load_state(args.resume_from_checkpoint							)
																		snake_case							:  str			=     os.path.basename(args.resume_from_checkpoint							)
												else:
																		# Get the most recent checkpoint
																		snake_case							:  Optional[int]			=     [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
																		dirs.sort(key=os.path.getctime							)
																		snake_case							:  Optional[Any]			=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case							:  List[Any]			=     os.path.splitext(A_							)[0]

												if "epoch" in training_difference:
																		snake_case							:  str			=     int(training_difference.replace('''epoch_'''     ,				''''''							)							) + 1
																		snake_case							:  Union[str, Any]			=     None
												else:
																		snake_case							:  int			=     int(training_difference.replace('''step_'''     ,				''''''							)							)
																		snake_case							:  Any			=     resume_step // len(A_							)
																		resume_step -= starting_epoch * len(A_							)

    # Now we train the model
						for epoch in range(A_     ,				A_							):
												model.train()
												# New Code #
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		snake_case							:  Optional[int]			=     accelerator.skip_first_batches(A_     ,				A_							)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case							:  Optional[int]			=     train_dataloader
												for step, batch in enumerate(A_							):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device							)
																		snake_case							:  Union[str, Any]			=     model(**A_							)
																		snake_case							:  Dict			=     outputs.loss
																		snake_case							:  Dict			=     loss / gradient_accumulation_steps
																		accelerator.backward(A_							)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(A_     ,				A_							):
																								snake_case							:  Any			=     F"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case							:  List[str]			=     os.path.join(args.output_dir     ,				A_							)
																														accelerator.save_state(A_							)

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

												snake_case							:  Dict			=     metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""     ,				A_							)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		snake_case							:  Any			=     F"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								snake_case							:  Dict			=     os.path.join(args.output_dir     ,				A_							)
																		accelerator.save_state(A_							)

def   A			(							):
						snake_case							:  str			=     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.'''     ,				)
						parser.add_argument('''--cpu'''     ,				action='''store_true'''     ,				help='''If passed, will train on the CPU.'''							)
						parser.add_argument(
						    '''--checkpointing_steps'''     ,				type=A_     ,				default=A_     ,				help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''     ,				)
						parser.add_argument(
						    '''--output_dir'''     ,				type=A_     ,				default='''.'''     ,				help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''     ,				)
						parser.add_argument(
						    '''--resume_from_checkpoint'''     ,				type=A_     ,				default=A_     ,				help='''If the training should continue from a checkpoint folder.'''     ,				)
						snake_case							:  Optional[int]			=     parser.parse_args()
						snake_case							:  Tuple			=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A_     ,				A_							)


if __name__ == "__main__":
						main()









"""simple docstring"""
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
      import transformer_engine.pytorch as te






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase=True     ,  _lowerCamelCase=True     ,  _lowerCamelCase=True							):
 if not is_fpa_available():
  raise ImportError('Using `convert_model` requires transformer_engine to be installed.'							)
 for name, module in model.named_children():
  if isinstance(_lowerCamelCase     ,  nn.Linear							) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape							):
    return
   lowerCamelCase__	:     List[Any]								=		module.bias is not None
   lowerCamelCase__	:     Tuple								=		te.Linear(
       module.in_features     ,  module.out_features     ,  bias=_lowerCamelCase     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Optional[Any]								=		module.weight.data.clone()
   if has_bias:
    lowerCamelCase__	:     List[str]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  nn.LayerNorm							) and to_transformer_engine and _convert_ln:
   lowerCamelCase__	:     Tuple								=		te.LayerNorm(module.normalized_shape[0]     ,  eps=module.eps     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Tuple								=		module.weight.data.clone()
   lowerCamelCase__	:     Optional[Any]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  te.Linear							) and not to_transformer_engine and _convert_linear:
   lowerCamelCase__	:     Optional[Any]								=		module.bias is not None
   lowerCamelCase__	:     Union[str, Any]								=		nn.Linear(
       module.in_features     ,  module.out_features     ,  bias=_lowerCamelCase     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     str								=		module.weight.data.clone()
   if has_bias:
    lowerCamelCase__	:     Optional[int]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  te.LayerNorm							) and not to_transformer_engine and _convert_ln:
   lowerCamelCase__	:     str								=		nn.LayerNorm(module.normalized_shape[0]     ,  eps=module.eps     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Dict								=		module.weight.data.clone()
   lowerCamelCase__	:     Dict								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  else:
   convert_model(
       _lowerCamelCase     ,  to_transformer_engine=_lowerCamelCase     ,  _convert_linear=_convert_linear     ,  _convert_ln=_convert_ln     ,  )






def 			lowerCamelCase_    (		_lowerCamelCase							):
 if not is_fpa_available():
  raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'							)
 for m in model.modules():
  if isinstance(_lowerCamelCase     ,  (te.LayerNorm, te.Linear)							):
   return True
 return False

