


'''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
#
########################################################################

a__    :   int								=			1_6
a__    :   Optional[Any]								=			3_2







def _UpperCamelCase			(	__A ,     __A = 16       )				->						Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							AutoTokenizer.from_pretrained("bert-base-cased"       )
      UpperCamelCase__      =							load_dataset("glue" ,     "mrpc"       )

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

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

            return tokenizer.pad(
                _lowerCamelCase ,     padding="longest" ,     max_length=_lowerCamelCase ,     pad_to_multiple_of=_lowerCamelCase ,     return_tensors="pt" ,     )

      # Instantiate dataloaders.
      UpperCamelCase__      =							DataLoader(
          tokenized_datasets["train"] ,     shuffle=_lowerCamelCase ,     collate_fn=_lowerCamelCase ,     batch_size=_lowerCamelCase       )
      UpperCamelCase__      =							DataLoader(
          tokenized_datasets["validation"] ,     shuffle=_lowerCamelCase ,     collate_fn=_lowerCamelCase ,     batch_size=_lowerCamelCase       )

      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

  a__    :   Tuple								=			mocked_dataloaders  # noqa: F811







def _UpperCamelCase			(	__A ,     __A       )				->						List[str]:


      '''simple docstring'''



      if os.environ.get("TESTING_MOCKED_DATALOADERS" ,     _lowerCamelCase       ) == "1":
            UpperCamelCase__      =							2
      # Initialize accelerator
      UpperCamelCase__      =							Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision       )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase__      =							config["lr"]
      UpperCamelCase__      =							int(config["num_epochs"]       )
      UpperCamelCase__      =							int(config["seed"]       )
      UpperCamelCase__      =							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":
                  UpperCamelCase__      =							args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  UpperCamelCase__      =							int(args.checkpointing_steps       )
            else:
                  raise ValueError(
                      F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
      else:
            UpperCamelCase__      =							None

      set_seed(_lowerCamelCase       )

      UpperCamelCase__      =							get_dataloaders(_lowerCamelCase ,     _lowerCamelCase       )
      UpperCamelCase__      =							evaluate.load("glue" ,     "mrpc"       )

      # If the batch size is too big we use gradient accumulation
      UpperCamelCase__      =							1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            UpperCamelCase__      =							batch_size // MAX_GPU_BATCH_SIZE
            UpperCamelCase__      =							MAX_GPU_BATCH_SIZE

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase__      =							AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,     return_dict=_lowerCamelCase       )

      # 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).
      UpperCamelCase__      =							model.to(accelerator.device       )

      # Instantiate optimizer
      UpperCamelCase__      =							AdamW(params=model.parameters() ,     lr=_lowerCamelCase       )

      # Instantiate scheduler
      UpperCamelCase__      =							get_linear_schedule_with_warmup(
          optimizer=_lowerCamelCase ,     num_warmup_steps=100 ,     num_training_steps=(len(_lowerCamelCase       ) * 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.
      UpperCamelCase__      =							accelerator.prepare(
          _lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase       )

      # New Code #
      # We need to keep track of how many total steps we have iterated over
      UpperCamelCase__      =							0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCamelCase__      =							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       )
                  UpperCamelCase__      =							os.path.basename(args.resume_from_checkpoint       )
            else:
                  # Get the most recent checkpoint
                  UpperCamelCase__      =							[f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
                  dirs.sort(key=os.path.getctime       )
                  UpperCamelCase__      =							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            UpperCamelCase__      =							os.path.splitext(_lowerCamelCase       )[0]

            if "epoch" in training_difference:
                  UpperCamelCase__      =							int(training_difference.replace("epoch_" ,     ""       )       ) + 1
                  UpperCamelCase__      =							None
            else:
                  UpperCamelCase__      =							int(training_difference.replace("step_" ,     ""       )       )
                  UpperCamelCase__      =							resume_step // len(_lowerCamelCase       )
                  resume_step -= starting_epoch * len(_lowerCamelCase       )

    # Now we train the model
      for epoch in range(_lowerCamelCase ,     _lowerCamelCase       ):
            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
                  UpperCamelCase__      =							accelerator.skip_first_batches(_lowerCamelCase ,     _lowerCamelCase       )
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  UpperCamelCase__      =							train_dataloader
            for step, batch in enumerate(_lowerCamelCase       ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device       )
                  UpperCamelCase__      =							model(**_lowerCamelCase       )
                  UpperCamelCase__      =							outputs.loss
                  UpperCamelCase__      =							loss / gradient_accumulation_steps
                  accelerator.backward(_lowerCamelCase       )
                  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(_lowerCamelCase ,     _lowerCamelCase       ):
                        UpperCamelCase__      =							F'''step_{overall_step}'''
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    UpperCamelCase__      =							os.path.join(args.output_dir ,     _lowerCamelCase       )
                              accelerator.save_state(_lowerCamelCase       )

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

            UpperCamelCase__      =							metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:''' ,     _lowerCamelCase       )

            # 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":
                  UpperCamelCase__      =							F'''epoch_{epoch}'''
                  if args.output_dir is not None:
                        UpperCamelCase__      =							os.path.join(args.output_dir ,     _lowerCamelCase       )
                  accelerator.save_state(_lowerCamelCase       )







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


      '''simple docstring'''



      UpperCamelCase__      =							argparse.ArgumentParser(description="Simple example of training script."       )
      parser.add_argument(
          "--mixed_precision" ,     type=_lowerCamelCase ,     default=_lowerCamelCase ,     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=_lowerCamelCase ,     default=_lowerCamelCase ,     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=_lowerCamelCase ,     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=_lowerCamelCase ,     default=_lowerCamelCase ,     help="If the training should continue from a checkpoint folder." ,     )
      UpperCamelCase__      =							parser.parse_args()
      UpperCamelCase__      =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(_lowerCamelCase ,     _lowerCamelCase       )


if __name__ == "__main__":
  main()










class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]			)   ->					Tuple:
									A				:       Dict														=    val
									# Number of nodes in left subtree
									A				:       List[Any]														=    0
									A				:       List[str]														=    None
									A				:       Union[str, Any]														=    None
									A				:       Dict														=    None







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      Union[str, Any]			)   ->					Optional[Any]:
									assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

									if self.val < other.val:
												A				:       List[str]														=    self.right
												A				:       Any														=    None
												if self.right:
															A				:       Dict														=    other
												A				:       Optional[int]														=    other
												A				:       int														=    self.left_tree_size * 2 + 1
												return self
									else:
												A				:       Optional[Any]														=    other.right
												A				:       Dict														=    None
												if other.right:
															A				:       str														=    self
												A				:       Any														=    self
												A				:       int														=    other.left_tree_size * 2 + 1
												return other







class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[Any]=None						,						__lowerCamelCase   :      Union[str, Any]=None						,						__lowerCamelCase   :      Union[str, Any]=0			)   ->					Optional[Any]:
									A				:       List[str]														=    heap_size
									A				:       Optional[int]														=    bottom_root
									A				:       str														=    min_node







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[int]			)   ->					Optional[int]:

									# Empty heaps corner cases
									if other.size == 0:
												return None
									if self.size == 0:
												A				:       int														=    other.size
												A				:       Dict														=    other.bottom_root
												A				:       Tuple														=    other.min_node
												return None
									# Update size
									A				:       Optional[Any]														=    self.size + other.size

									# Update min.node
									if self.min_node.val > other.min_node.val:
												A				:       Tuple														=    other.min_node
									# Merge

									# Order roots by left_subtree_size
									A				:       Tuple														=    []
									A							,			A				:       List[str]														=    self.bottom_root, other.bottom_root
									while i or j:
												if i and ((not j) or i.left_tree_size < j.left_tree_size):
															combined_roots_list.append((i, True)			)
															A				:       List[Any]														=    i.parent
												else:
															combined_roots_list.append((j, False)			)
															A				:       Optional[Any]														=    j.parent
        # Insert links between them
									for i in range(len(__lowerCamelCase			) - 1			):
												if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
															A				:       str														=    combined_roots_list[i + 1][0]
															A				:       Any														=    combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
									A				:       Dict														=    combined_roots_list[0][0]
									while i.parent:
												if (
												    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
												) or (
												    i.left_tree_size == i.parent.left_tree_size
												    and i.left_tree_size != i.parent.parent.left_tree_size
												):
															# Neighbouring Nodes
															A				:       Union[str, Any]														=    i.left
															A				:       Tuple														=    i.parent.parent

															# Merging trees
															A				:       Optional[int]														=    i.merge_trees(i.parent			)

															# Updating links
															A				:       List[str]														=    previous_node
															A				:       int														=    next_node
															if previous_node:
																		A				:       int														=    i
															if next_node:
																		A				:       Optional[int]														=    i
												else:
															A				:       str														=    i.parent
        # Updating self.bottom_root
									while i.left:
												A				:       List[str]														=    i.left
									A				:       List[str]														=    i

									# Update other
									A				:       Optional[Any]														=    self.size
									A				:       Any														=    self.bottom_root
									A				:       Optional[Any]														=    self.min_node

									# Return the merged heap
									return self







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      int			)   ->					Optional[Any]:
									if self.size == 0:
												A				:       Dict														=    Node(__lowerCamelCase			)
												A				:       List[Any]														=    1
												A				:       Optional[Any]														=    self.bottom_root
									else:
												# Create new node
												A				:       Tuple														=    Node(__lowerCamelCase			)

												# Update size
												self.size += 1

												# update min_node
												if val < self.min_node.val:
															A				:       List[Any]														=    new_node
												# Put new_node as a bottom_root in heap
												A				:       int														=    new_node
												A				:       Tuple														=    self.bottom_root
												A				:       Any														=    new_node

												# Consecutively merge roots with same left_tree_size
												while (
												    self.bottom_root.parent
												    and self.bottom_root.left_tree_size
												    == self.bottom_root.parent.left_tree_size
												):
															# Next node
															A				:       List[Any]														=    self.bottom_root.parent.parent

															# Merge
															A				:       Optional[Any]														=    self.bottom_root.merge_trees(self.bottom_root.parent			)

															# Update Links
															A				:       List[Any]														=    next_node
															A				:       List[str]														=    None
															if next_node:
																		A				:       List[Any]														=    self.bottom_root







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									return self.min_node.val







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Tuple:
									return self.size == 0







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Optional[int]:
									A				:       List[str]														=    self.min_node.val

									# Last element in heap corner case
									if self.size == 1:
												# Update size
												A				:       Union[str, Any]														=    0

												# Update bottom root
												A				:       int														=    None

												# Update min_node
												A				:       Optional[int]														=    None

												return min_value
									# No right subtree corner case
									# The structure of the tree implies that this should be the bottom root
									# and there is at least one other root
									if self.min_node.right is None:
												# Update size
												self.size -= 1

												# Update bottom root
												A				:       List[str]														=    self.bottom_root.parent
												A				:       Dict														=    None

												# Update min_node
												A				:       List[str]														=    self.bottom_root
												A				:       Dict														=    self.bottom_root.parent
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       List[Any]														=    i.parent
												return min_value
									# General case
									# Find the BinomialHeap of the right subtree of min_node
									A				:       List[str]														=    self.min_node.right
									A				:       List[Any]														=    None
									A				:       Tuple														=    bottom_of_new
									A				:       Optional[Any]														=    1

									# Size, min_node and bottom_root
									while bottom_of_new.left:
												A				:       List[str]														=    size_of_new * 2 + 1
												A				:       int														=    bottom_of_new.left
												if bottom_of_new.val < min_of_new.val:
															A				:       int														=    bottom_of_new
        # Corner case of single root on top left path
									if (not self.min_node.left) and (not self.min_node.parent):
												A				:       Optional[Any]														=    size_of_new
												A				:       List[str]														=    bottom_of_new
												A				:       Optional[int]														=    min_of_new
												# print("Single root, multiple nodes case")
												return min_value
									# Remaining cases
									# Construct heap of right subtree
									A				:       List[str]														=    BinomialHeap(
									    bottom_root=__lowerCamelCase						,						min_node=__lowerCamelCase						,						heap_size=__lowerCamelCase			)

									# Update size
									A				:       Tuple														=    self.size - 1 - size_of_new

									# Neighbour nodes
									A				:       List[str]														=    self.min_node.left
									A				:       List[str]														=    self.min_node.parent

									# Initialize new bottom_root and min_node
									A				:       List[Any]														=    previous_node or next_node
									A				:       int														=    next_node

									# Update links of previous_node and search below for new min_node and
									# bottom_root
									if previous_node:
												A				:       Optional[int]														=    next_node

												# Update bottom_root and search for min_node below
												A				:       Optional[Any]														=    previous_node
												A				:       Dict														=    previous_node
												while self.bottom_root.left:
															A				:       List[str]														=    self.bottom_root.left
															if self.bottom_root.val < self.min_node.val:
																		A				:       Optional[Any]														=    self.bottom_root
									if next_node:
												A				:       Any														=    previous_node

												# Search for new min_node above min_node
												A				:       int														=    next_node
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       Any														=    i.parent
        # Merge heaps
									self.merge_heaps(__lowerCamelCase			)

									return min_value







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Optional[Any]:
									A				:       Tuple														=    self.bottom_root
									while top_root.parent:
												A				:       Optional[int]														=    top_root.parent
									# preorder
									A				:       Union[str, Any]														=    []
									self.__traversal(__lowerCamelCase						,						__lowerCamelCase			)
									return heap_pre_order







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]						,						__lowerCamelCase   :      Optional[int]						,						__lowerCamelCase   :      Any=0			)   ->					List[Any]:
									if curr_node:
												preorder.append((curr_node.val, level)			)
												self.__traversal(curr_node.left						,						__lowerCamelCase						,						level + 1			)
												self.__traversal(curr_node.right						,						__lowerCamelCase						,						level + 1			)
									else:
												preorder.append(("#", level)			)







						def __str__(						self   :      List[str]			)   ->					Optional[Any]:
									if self.is_empty():
												return ""
									A				:       Optional[int]														=    self.pre_order()

									return "\n".join(("-" * level + str(__lowerCamelCase			)) for value, level in preorder_heap			)


# Unit Tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()