



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


lowerCamelCase  			=		16
lowerCamelCase  			=		32
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ = 16 ):
      UpperCAmelCase_     					=				AutoTokenizer.from_pretrained("bert-base-cased" )
      UpperCAmelCase_     					=				load_dataset("glue"      ,     "mrpc" )

      def tokenize_function(lowerCAmelCase__ ):
            # 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(lowerCAmelCase__ ):
            # 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

  lowerCamelCase  			=		mocked_dataloaders  # noqa: F811
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,     lowerCAmelCase__ ) == "1":
            UpperCAmelCase_     					=				2
      # New Code #
      UpperCAmelCase_     					=				int(args.gradient_accumulation_steps )
      UpperCAmelCase_     					=				int(args.local_sgd_steps )
      # Initialize accelerator
      UpperCAmelCase_     					=				Accelerator(
          cpu=args.cpu      ,     mixed_precision=args.mixed_precision      ,     gradient_accumulation_steps=lowerCAmelCase__ )
      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
      UpperCAmelCase_     					=				config["lr"]
      UpperCAmelCase_     					=				int(config["num_epochs"] )
      UpperCAmelCase_     					=				int(config["seed"] )
      UpperCAmelCase_     					=				int(config["batch_size"] )

      UpperCAmelCase_     					=				evaluate.load("glue"      ,     "mrpc" )

      set_seed(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				get_dataloaders(lowerCAmelCase__      ,     lowerCAmelCase__ )
      # 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)      ,     )

      # 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_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				accelerator.prepare(
          lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ )

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

            model.eval()
            for step, batch in enumerate(lowerCAmelCase__ ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device )
                  with torch.no_grad():
                        UpperCAmelCase_     					=				model(**lowerCAmelCase__ )
                  UpperCAmelCase_     					=				outputs.logits.argmax(dim=-1 )
                  UpperCAmelCase_					,       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__ )
def   a__      ( ):
      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."      ,     )
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps"      ,     type=lowerCAmelCase__      ,     default=1      ,     help="The number of minibatches to be ran before gradients are accumulated."      ,     )
      parser.add_argument(
          "--local_sgd_steps"      ,     type=lowerCAmelCase__      ,     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." )
      UpperCAmelCase_     					=				parser.parse_args()
      UpperCAmelCase_     					=				{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(lowerCAmelCase__      ,     lowerCAmelCase__ )


if __name__ == "__main__":
  main()











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   TypeVar('''T''')



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (position - 1) // 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 1



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 2

class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Any   )       -> None:

			'''simple docstring'''




			__lowercase											=     []
			__lowercase											=     {}
			__lowercase											=     0




	def __len__(						self							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			return self.elements




	def __repr__(						self							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			return str(self.heap   )




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

			'''simple docstring'''




			return self.elements == 0




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.heap.append((elem, weight)   )
			__lowercase											=     self.elements
			self.elements += 1
			self._bubble_up(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> T:

			'''simple docstring'''




			if self.elements > 1:
					self._swap_nodes(0					,      self.elements - 1   )
			__lowercase						,      __lowercase											=     self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
					__lowercase						,      __lowercase											=     self.heap[0]
					self._bubble_down(__lowerCamelCase   )
			return elem




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase											=     (elem, weight)
			if position > 0:
					__lowercase											=     get_parent_position(__lowerCamelCase   )
					__lowercase						,      __lowercase											=     self.heap[parent_position]
					if parent_weight > weight:
							self._bubble_up(__lowerCamelCase   )
					else:
							self._bubble_down(__lowerCamelCase   )
			else:
					self._bubble_down(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			if curr_pos == 0:
					return None
			__lowercase											=     get_parent_position(__lowerCamelCase   )
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase						,      __lowercase											=     self.heap[parent_position]
			if parent_weight > weight:
					self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
					return self._bubble_up(__lowerCamelCase   )
			return None




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase											=     get_child_left_position(__lowerCamelCase   )
			__lowercase											=     get_child_right_position(__lowerCamelCase   )
			if child_left_position < self.elements and child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < child_left_weight and child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			if child_left_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					if child_left_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			else:
					return None
			if child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			return None






	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase						,      __lowercase											=     (
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__lowercase											=     nodea_pos
			__lowercase											=     nodea_pos





class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]   )       -> None:

			'''simple docstring'''




			__lowercase											=     {}
			__lowercase											=     0




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

			'''simple docstring'''




			return str(self.connections   )




	def __len__(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.nodes




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			if node not in self.connections:
					__lowercase											=     {}
					self.nodes += 1






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.add_node(__lowerCamelCase   )
			self.add_node(__lowerCamelCase   )
			__lowercase											=     weight
			__lowercase											=     weight





def  SCREAMING_SNAKE_CASE				(      snake_case						,   )		->    tuple[dict[T, int], dict[T, T | None]]:
		__lowercase											=     {node: maxsize for node in graph.connections}
		__lowercase											=     {node: None for node in graph.connections}

		__lowercase											=     MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(snake_case						,   snake_case							)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		__lowercase											=     priority_queue.extract_min()
		__lowercase											=     0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						__lowercase											=     dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(snake_case						,   dist[neighbour]							)
						__lowercase											=     node

    # running prim's algorithm
		while not priority_queue.is_empty():
				__lowercase											=     priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								__lowercase											=     dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(snake_case						,   dist[neighbour]							)
								__lowercase											=     node
		return dist, parent




