






"""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
# and perform 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
#
########################################################################


SCREAMING_SNAKE_CASE						:						Optional[int]					   =       16
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       32



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 16				):
				A__							 =       AutoTokenizer.from_pretrained('bert-base-cased'				)
				A__							 =       load_dataset('glue'       ,'mrpc'				)

				def tokenize_function(lowerCAmelCase__				):
								# max_length=None => use the model max length (it's actually the default)
								A__							 =       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():
								A__							 =       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
				A__							 =       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.
								A__							 =       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":
												A__							 =       16
								elif accelerator.mixed_precision != "no":
												A__							 =       8
								else:
												A__							 =       None

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

				# Instantiate dataloaders.
				A__							 =       DataLoader(
				    tokenized_datasets['train']       ,shuffle=lowerCAmelCase__       ,collate_fn=lowerCAmelCase__       ,batch_size=lowerCAmelCase__				)
				A__							 =       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

					SCREAMING_SNAKE_CASE						:						List[Any]					   =       mocked_dataloaders  # noqa: F811



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				# For testing only
				if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,lowerCAmelCase__				) == "1":
								A__							 =       2
				# New Code #
				A__							 =       int(args.gradient_accumulation_steps				)
				# Initialize accelerator
				A__							 =       Accelerator(
				    cpu=args.cpu       ,mixed_precision=args.mixed_precision       ,gradient_accumulation_steps=lowerCAmelCase__				)
				if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
								raise NotImplementedError(
								    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__							 =       config['lr']
				A__							 =       int(config['num_epochs']				)
				A__							 =       int(config['seed']				)
				A__							 =       int(config['batch_size']				)

				A__							 =       evaluate.load('glue'       ,'mrpc'				)

				set_seed(lowerCAmelCase__				)
				A__ ,			A__							 =       get_dataloaders(lowerCAmelCase__       ,lowerCAmelCase__				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__							 =       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).
				A__							 =       model.to(accelerator.device				)

				# Instantiate optimizer
				A__							 =       AdamW(params=model.parameters()       ,lr=lowerCAmelCase__				)

				# Instantiate scheduler
				A__							 =       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.
				A__ ,			A__ ,			A__ ,			A__ ,			A__							 =       accelerator.prepare(
				    lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				# Now we train the model
				for epoch in range(lowerCAmelCase__				):
								model.train()
								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__				):
																A__							 =       model(**lowerCAmelCase__				)
																A__							 =       output.loss
																accelerator.backward(lowerCAmelCase__				)
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								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():
																A__							 =       model(**lowerCAmelCase__				)
												A__							 =       outputs.logits.argmax(dim=-1				)
												A__ ,			A__							 =       accelerator.gather_for_metrics((predictions, batch['labels'])				)
												metric.add_batch(
												    predictions=lowerCAmelCase__       ,references=lowerCAmelCase__       ,)

								A__							 =       metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					):
				A__							 =       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('--cpu'       ,action='store_true'       ,help='If passed, will train on the CPU.'				)
				A__							 =       parser.parse_args()
				A__							 =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowerCAmelCase__       ,lowerCAmelCase__				)


if __name__ == "__main__":
					main()










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

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


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       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__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

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

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


