



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


A__								=						16
A__								=						32



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase = 16      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Dict     		=	AutoTokenizer.from_pretrained('''bert-base-cased'''      )
				snake_case__	:      Union[str, Any]     		=	load_dataset('''glue'''  ,  '''mrpc'''      )

				def tokenize_function(__lowerCAmelCase      ):
								# max_length=None => use the model max length (it's actually the default)
								snake_case__	:      Optional[Any]     		=	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():
								snake_case__	:      Optional[Any]     		=	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
				snake_case__	:      Tuple     		=	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.
								snake_case__	:      Tuple     		=	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__	:      int     		=	16
								elif accelerator.mixed_precision != "no":
												snake_case__	:      Tuple     		=	8
								else:
												snake_case__	:      Any     		=	None

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

				# Instantiate dataloaders.
				snake_case__	:      List[str]     		=	DataLoader(
				    tokenized_datasets['''train''']  ,  shuffle=__snake_case  ,  collate_fn=__snake_case  ,  batch_size=__snake_case      )
				snake_case__	:      Tuple     		=	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

				A__								=						mocked_dataloaders  # noqa: F811



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Dict:


				"""simple docstring"""

				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,  __snake_case      ) == "1":
								snake_case__	:      Optional[int]     		=	2
				# New Code #
				snake_case__	:      str     		=	int(args.gradient_accumulation_steps      )
				snake_case__	:      Optional[int]     		=	int(args.local_sgd_steps      )
				# Initialize accelerator
				snake_case__	:      str     		=	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
				snake_case__	:      List[Any]     		=	config['''lr''']
				snake_case__	:      int     		=	int(config['''num_epochs''']      )
				snake_case__	:      Dict     		=	int(config['''seed''']      )
				snake_case__	:      List[str]     		=	int(config['''batch_size''']      )

				snake_case__	:      Optional[Any]     		=	evaluate.load('''glue'''  ,  '''mrpc'''      )

				set_seed(__snake_case      )
				snake_case__		, snake_case__	:      List[Any]     		=	get_dataloaders(__snake_case  ,  __snake_case      )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case__	:      Tuple     		=	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).
				snake_case__	:      Tuple     		=	model.to(accelerator.device      )

				# Instantiate optimizer
				snake_case__	:      Tuple     		=	AdamW(params=model.parameters()  ,  lr=__snake_case      )

				# Instantiate scheduler
				snake_case__	:      str     		=	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.
				snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__	:      Any     		=	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      ):
																				snake_case__	:      Optional[int]     		=	model(**__snake_case      )
																				snake_case__	:      List[Any]     		=	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():
																snake_case__	:      Union[str, Any]     		=	model(**__snake_case      )
												snake_case__	:      int     		=	outputs.logits.argmax(dim=-1      )
												snake_case__		, snake_case__	:      Tuple     		=	accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
												metric.add_batch(
												    predictions=__snake_case  ,  references=__snake_case  ,  )

								snake_case__	:      List[str]     		=	metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:"""  ,  __snake_case      )



def 			_lowerCAmelCase       (					)   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	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.'''      )
				snake_case__	:      Tuple     		=	parser.parse_args()
				snake_case__	:      List[str]     		=	{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__snake_case  ,  __snake_case      )


if __name__ == "__main__":
				main()









"""simple docstring"""



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_UpperCamelCase		:  Optional[Any]						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase = True       ):
      lowercase							    =       {}  # dictionary of lists
      lowercase							    =       directed







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):

      if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
          self.adj_list[destination_vertex].append(_lowerCamelCase       )
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       [source_vertex]
      else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
          self.adj_list[source_vertex].append(_lowerCamelCase       )
          lowercase							    =       []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
          lowercase							    =       [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
          lowercase							    =       [destination_vertex]
          lowercase							    =       []

      return self







    def __repr__(			self       ):
      return pformat(self.adj_list       )


