


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


__UpperCAmelCase       =							16
__UpperCAmelCase       =							32


def  _snake_case       (					A			,   A = 16	)						->   Tuple:
							lowerCAmelCase__	       =     AutoTokenizer.from_pretrained('''bert-base-cased'''	)
							lowerCAmelCase__	       =     load_dataset('''glue'''			,   '''mrpc'''	)

							def tokenize_function(A	):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__	       =     tokenizer(examples['''sentence1''']			,   examples['''sentence2''']			,   truncation=A			,   max_length=A	)
														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__	       =     datasets.map(
														    A			,   batched=A			,   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__	       =     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.
														lowerCAmelCase__	       =     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__	       =     16
														elif accelerator.mixed_precision != "no":
																					lowerCAmelCase__	       =     8
														else:
																					lowerCAmelCase__	       =     None

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

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

							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

		__UpperCAmelCase       =							mocked_dataloaders  # noqa: F811


def  _snake_case       (					A			,   A	)						->   List[Any]:
							# For testing only
							if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,   A	) == "1":
														lowerCAmelCase__	       =     2
							# New Code #
							lowerCAmelCase__	       =     int(args.gradient_accumulation_steps	)
							# Initialize accelerator
							lowerCAmelCase__	       =     Accelerator(
							    cpu=args.cpu			,   mixed_precision=args.mixed_precision			,   gradient_accumulation_steps=A	)
							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
							lowerCAmelCase__	       =     config['''lr''']
							lowerCAmelCase__	       =     int(config['''num_epochs''']	)
							lowerCAmelCase__	       =     int(config['''seed''']	)
							lowerCAmelCase__	       =     int(config['''batch_size''']	)

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

							set_seed(A	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     get_dataloaders(A			,   A	)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							lowerCAmelCase__	       =     AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,   return_dict=A	)

							# 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__	       =     model.to(accelerator.device	)

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

							# Instantiate scheduler
							lowerCAmelCase__	       =     get_linear_schedule_with_warmup(
							    optimizer=A			,   num_warmup_steps=100			,   num_training_steps=(len(A	) * 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__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     accelerator.prepare(
							    A			,   A			,   A			,   A			,   A	)

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

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

														lowerCAmelCase__	       =     metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"""epoch {epoch}:"""			,   A	)


def  _snake_case       (					)						->   Optional[Any]:
							lowerCAmelCase__	       =     argparse.ArgumentParser(description='''Simple example of training script.'''	)
							parser.add_argument(
							    '''--mixed_precision'''			,   type=A			,   default=A			,   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=A			,   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.'''	)
							lowerCAmelCase__	       =     parser.parse_args()
							lowerCAmelCase__	       =     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(A			,   A	)


if __name__ == "__main__":
		main()



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     predicted_token_bpe.replace('''\u2581'''			,   ''' '''	)
														if " {0}".format(A	) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(A	)			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(A			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
							return topk_filled_outputs


__UpperCAmelCase       =							CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase       =							CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__UpperCAmelCase       =							'''Le camembert est <mask> :)'''






print(fill_mask(masked_input, model, tokenizer, topk=3))