
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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = 16     )				->		Union[str, Any]:
			lowerCAmelCase__      :      Union[str, Any]         =		AutoTokenizer.from_pretrained('bert-base-cased'     )
			lowerCAmelCase__      :      Optional[Any]         =		load_dataset('glue'       ,							'mrpc'     )

			def tokenize_function(SCREAMING_SNAKE_CASE_     ):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase__      :      str         =		tokenizer(examples['sentence1']       ,							examples['sentence2']       ,							truncation=_SCREAMING_SNAKE_CASE       ,							max_length=_SCREAMING_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():
						lowerCAmelCase__      :      List[Any]         =		datasets.map(
						    _SCREAMING_SNAKE_CASE       ,							batched=_SCREAMING_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
			lowerCAmelCase__      :      List[str]         =		tokenized_datasets.rename_column('label'       ,							'labels'     )

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

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

			# Instantiate dataloaders.
			lowerCAmelCase__      :      Union[str, Any]         =		DataLoader(
			    tokenized_datasets['train']       ,							shuffle=_SCREAMING_SNAKE_CASE       ,							collate_fn=_SCREAMING_SNAKE_CASE       ,							batch_size=_SCREAMING_SNAKE_CASE     )
			lowerCAmelCase__      :      List[str]         =		DataLoader(
			    tokenized_datasets['validation']       ,							shuffle=_SCREAMING_SNAKE_CASE       ,							collate_fn=_SCREAMING_SNAKE_CASE       ,							batch_size=_SCREAMING_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

					lowerCamelCase__          =		mocked_dataloaders  # noqa: F811
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,							_SCREAMING_SNAKE_CASE     ) == "1":
						lowerCAmelCase__      :      Optional[int]         =		2
			# New Code #
			lowerCAmelCase__      :      Union[str, Any]         =		int(args.gradient_accumulation_steps     )
			lowerCAmelCase__      :      Dict         =		int(args.local_sgd_steps     )
			# Initialize accelerator
			lowerCAmelCase__      :      Tuple         =		Accelerator(
			    cpu=args.cpu       ,							mixed_precision=args.mixed_precision       ,							gradient_accumulation_steps=_SCREAMING_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
			lowerCAmelCase__      :      Dict         =		config['lr']
			lowerCAmelCase__      :      Any         =		int(config['num_epochs']     )
			lowerCAmelCase__      :      int         =		int(config['seed']     )
			lowerCAmelCase__      :      List[str]         =		int(config['batch_size']     )

			lowerCAmelCase__      :      Any         =		evaluate.load('glue'       ,							'mrpc'     )

			set_seed(_SCREAMING_SNAKE_CASE     )
			lowerCAmelCase__      :      int         =		get_dataloaders(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE     )
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCAmelCase__      :      List[str]         =		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,							return_dict=_SCREAMING_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).
			lowerCAmelCase__      :      Dict         =		model.to(accelerator.device     )

			# Instantiate optimizer
			lowerCAmelCase__      :      int         =		AdamW(params=model.parameters()       ,							lr=_SCREAMING_SNAKE_CASE     )

			# Instantiate scheduler
			lowerCAmelCase__      :      Any         =		get_linear_schedule_with_warmup(
			    optimizer=_SCREAMING_SNAKE_CASE       ,							num_warmup_steps=100       ,							num_training_steps=(len(_SCREAMING_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.
			lowerCAmelCase__      :      Any         =		accelerator.prepare(
			    _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE     )

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

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

						lowerCAmelCase__      :      int         =		metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''       ,							_SCREAMING_SNAKE_CASE     )
def  lowerCAmelCase__			(  )				->		Tuple:
			lowerCAmelCase__      :      Optional[Any]         =		argparse.ArgumentParser(description='Simple example of training script.'     )
			parser.add_argument(
			    '--mixed_precision'       ,							type=_SCREAMING_SNAKE_CASE       ,							default=_SCREAMING_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=_SCREAMING_SNAKE_CASE       ,							default=1       ,							help='The number of minibatches to be ran before gradients are accumulated.'       ,							)
			parser.add_argument(
			    '--local_sgd_steps'       ,							type=_SCREAMING_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.'     )
			lowerCAmelCase__      :      Optional[Any]         =		parser.parse_args()
			lowerCAmelCase__      :      str         =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
					main()



'''simple docstring'''



import torch

from transformers import AutoModel





class 							__UpperCamelCase				(		torch.nn.Module					):




	def __init__(   self						,				__a="sayef/fsner-bert-base-uncased"							):

						'''simple docstring'''





						super(__a						,				self							).__init__()

						__a			:       Tuple					    =  AutoModel.from_pretrained(__a						,				return_dict=__a							)
						__a			:       int					    =  torch.nn.CosineSimilarity(3						,				1E-0_8							)
						__a			:       Union[str, Any]					    =  torch.nn.Softmax(dim=1							)




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						return self.bert(**__a							).last_hidden_state




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return token_embeddings.sum(2						,				keepdim=__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=1							):

						'''simple docstring'''





						return self.softmax(T * self.cos(__a						,				__a							)							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  W_supports['sizes'].tolist()
						__a			:       Union[str, Any]					    =  W_supports['start_token_id'].item()
						__a			:       Any					    =  W_supports['end_token_id'].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						__a			:       Tuple					    =  self.BERT(**__a							)
						__a			:       str					    =  self.BERT(**__a							)

						__a			:       Any					    =  None
						__a			:       Dict					    =  None

						__a			:       Dict					    =  W_supports['input_ids'] == start_token_id
						__a			:       Union[str, Any]					    =  W_supports['input_ids'] == end_token_id

						for i, size in enumerate(__a							):
											if i == 0:
																__a			:       Optional[int]					    =  0
											else:
																__a			:       Union[str, Any]					    =  support_sizes[i - 1]

											__a			:       int					    =  S[s : s + size][start_token_masks[s : s + size]]
											__a			:       Union[str, Any]					    =  S[s : s + size][end_token_masks[s : s + size]]

											__a			:       Tuple					    =  torch.matmul(q[i]						,				s_start.T							).sum(1							).softmax(0							)
											__a			:       Dict					    =  torch.matmul(q[i]						,				s_end.T							).sum(1							).softmax(0							)

											if p_starts is not None:
																__a			:       str					    =  torch.vstack((p_starts, p_start)							)
																__a			:       str					    =  torch.vstack((p_ends, p_end)							)
											else:
																__a			:       List[str]					    =  p_start
																__a			:       int					    =  p_end

						return p_starts, p_ends



