


import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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	:     Any   =16
_UpperCAmelCase	:     int   =32



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_ = 16       )->       List[Any]:
				lowerCAmelCase_       :					Dict			 = AutoTokenizer.from_pretrained('''bert-base-cased'''       )
				lowerCAmelCase_       :					List[str]			 = load_dataset('''glue'''       ,      '''mrpc'''       )

				def tokenize_function(lowerCAmelCase_       ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase_       :					Union[str, 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():
								lowerCAmelCase_       :					str			 = 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
				lowerCAmelCase_       :					str			 = 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.
								lowerCAmelCase_       :					Union[str, Any]			 = 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_       :					Any			 = 16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase_       :					Optional[int]			 = 8
								else:
												lowerCAmelCase_       :					List[str]			 = None

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

				# Instantiate dataloaders.
				lowerCAmelCase_       :					List[Any]			 = DataLoader(
				    tokenized_datasets['''train''']       ,      shuffle=snake_case__       ,      collate_fn=snake_case__       ,      batch_size=snake_case__       )
				lowerCAmelCase_       :					Optional[int]			 = 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

						_UpperCAmelCase	:     Tuple   =mocked_dataloaders  # noqa: F811



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Union[str, Any]:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,      snake_case__       ) == "1":
								lowerCAmelCase_       :					int			 = 2
				# Initialize accelerator
				lowerCAmelCase_       :					Dict			 = Accelerator(cpu=args.cpu       ,      mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase_       :					Union[str, Any]			 = config['''lr''']
				lowerCAmelCase_       :					Any			 = int(config['''num_epochs''']       )
				lowerCAmelCase_       :					List[str]			 = int(config['''seed''']       )
				lowerCAmelCase_       :					List[str]			 = int(config['''batch_size''']       )

				lowerCAmelCase_       :					Dict			 = evaluate.load('''glue'''       ,      '''mrpc'''       )

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=snake_case__       )
				def inner_training_loop(lowerCAmelCase_       ):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(snake_case__       )

								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								lowerCAmelCase_       :					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).
								lowerCAmelCase_       :					Union[str, Any]			 = model.to(accelerator.device       )

								# Instantiate optimizer
								lowerCAmelCase_       :					Optional[int]			 = AdamW(params=model.parameters()       ,      lr=snake_case__       )
								lowerCAmelCase_ ,     lowerCAmelCase_       :					Dict			 = get_dataloaders(snake_case__       ,      snake_case__       )

								# Instantiate scheduler
								lowerCAmelCase_       :					Any			 = 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.
								lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_       :					Optional[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()
												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       )
																lowerCAmelCase_       :					Tuple			 = model(**snake_case__       )
																lowerCAmelCase_       :					Any			 = outputs.loss
																accelerator.backward(snake_case__       )
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												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():
																				lowerCAmelCase_       :					Any			 = model(**snake_case__       )
																lowerCAmelCase_       :					Optional[int]			 = outputs.logits.argmax(dim=-1       )
																lowerCAmelCase_ ,     lowerCAmelCase_       :					Optional[int]			 = accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
																metric.add_batch(
																    predictions=snake_case__       ,      references=snake_case__       ,      )

												lowerCAmelCase_       :					Tuple			 = metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:"""       ,      snake_case__       )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()



def 	lowerCAmelCase       (						)->       int:
				lowerCAmelCase_       :					int			 = 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.'''       ,      )
				parser.add_argument('''--cpu'''       ,      action='''store_true'''       ,      help='''If passed, will train on the CPU.'''       )
				lowerCAmelCase_       :					Any			 = parser.parse_args()
				lowerCAmelCase_       :					Optional[Any]			 = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(snake_case__       ,      snake_case__       )


if __name__ == "__main__":
						main()

import logging

from transformers.configuration_utils import PretrainedConfig


__a  			=					logging.getLogger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'masked_bert'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			str=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Any=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="topK"       ,							SCREAMING_SNAKE_CASE_  :			Dict="constant"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.0       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_act
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					layer_norm_eps
					lowercase_       =					pruning_method
					lowercase_       =					mask_init
					lowercase_       =					mask_scale




