


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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_ = 16			)		->    Any:




						'''simple docstring'''







						__UpperCAmelCase :       Tuple  						=  AutoTokenizer.from_pretrained('''bert-base-cased'''			)
						__UpperCAmelCase :       Tuple  						=  load_dataset('''glue'''			,					'''mrpc'''			)

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

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

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

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

						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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,					lowercase_			) == "1":
												__UpperCAmelCase :       List[str]  						=  2
						# New Code #
						__UpperCAmelCase :       Tuple  						=  int(args.gradient_accumulation_steps			)
						__UpperCAmelCase :       int  						=  int(args.local_sgd_steps			)
						# Initialize accelerator
						__UpperCAmelCase :       Tuple  						=  Accelerator(
						    cpu=args.cpu			,					mixed_precision=args.mixed_precision			,					gradient_accumulation_steps=lowercase_			)
						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
						__UpperCAmelCase :       Tuple  						=  config['''lr''']
						__UpperCAmelCase :       Dict  						=  int(config['''num_epochs''']			)
						__UpperCAmelCase :       Union[str, Any]  						=  int(config['''seed''']			)
						__UpperCAmelCase :       Dict  						=  int(config['''batch_size''']			)

						__UpperCAmelCase :       List[Any]  						=  evaluate.load('''glue'''			,					'''mrpc'''			)

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

						# 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).
						__UpperCAmelCase :       Union[str, Any]  						=  model.to(accelerator.device			)

						# Instantiate optimizer
						__UpperCAmelCase :       int  						=  AdamW(params=model.parameters()			,					lr=lowercase_			)

						# Instantiate scheduler
						__UpperCAmelCase :       Optional[Any]  						=  get_linear_schedule_with_warmup(
						    optimizer=lowercase_			,					num_warmup_steps=100			,					num_training_steps=(len(lowercase_			) * 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.
						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       List[Any]  						=  accelerator.prepare(
						    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)

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

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

												__UpperCAmelCase :       Optional[int]  						=  metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"			,					lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  argparse.ArgumentParser(description='''Simple example of training script.'''			)
						parser.add_argument(
						    '''--mixed_precision'''			,					type=lowercase_			,					default=lowercase_			,					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=lowercase_			,					default=1			,					help='''The number of minibatches to be ran before gradients are accumulated.'''			,					)
						parser.add_argument(
						    '''--local_sgd_steps'''			,					type=lowercase_			,					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.'''			)
						__UpperCAmelCase :       List[str]  						=  parser.parse_args()
						__UpperCAmelCase :       Optional[Any]  						=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowercase_			,					lowercase_			)


if __name__ == "__main__":
				main()





lowerCAmelCase							=						256
# Modulus to hash a string
lowerCAmelCase							=						1_000_003




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    bool:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  len(lowercase_			)
						__UpperCAmelCase :       Tuple  						=  len(lowercase_			)
						if p_len > t_len:
												return False

						__UpperCAmelCase :       Any  						=  0
						__UpperCAmelCase :       List[Any]  						=  0
						__UpperCAmelCase :       List[Any]  						=  1

						# Calculating the hash of pattern and substring of text
						for i in range(lowercase_			):
												__UpperCAmelCase :       List[str]  						=  (ord(pattern[i]			) + p_hash * alphabet_size) % modulus
												__UpperCAmelCase :       List[Any]  						=  (ord(text[i]			) + text_hash * alphabet_size) % modulus
												if i == p_len - 1:
																		continue
												__UpperCAmelCase :       Any  						=  (modulus_power * alphabet_size) % modulus

						for i in range(0			,					t_len - p_len + 1			):
												if text_hash == p_hash and text[i : i + p_len] == pattern:
																		return True
												if i == t_len - p_len:
																		continue
												# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
												__UpperCAmelCase :       int  						=  (
												    (text_hash - ord(text[i]			) * modulus_power) * alphabet_size
												    + ord(text[i + p_len]			)
												) % modulus
						return False




def 						__SCREAMING_SNAKE_CASE       (					)		->    None:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  '''abc1abc12'''
						__UpperCAmelCase :       List[str]  						=  '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
						__UpperCAmelCase :       Any  						=  '''alskfjaldsk23adsfabcabc'''
						assert rabin_karp(lowercase_			,					lowercase_			) and not rabin_karp(lowercase_			,					lowercase_			)

						# Test 2)
						__UpperCAmelCase :       Union[str, Any]  						=  '''ABABX'''
						__UpperCAmelCase :       List[Any]  						=  '''ABABZABABYABABX'''
						assert rabin_karp(lowercase_			,					lowercase_			)

						# Test 3)
						__UpperCAmelCase :       str  						=  '''AAAB'''
						__UpperCAmelCase :       List[Any]  						=  '''ABAAAAAB'''
						assert rabin_karp(lowercase_			,					lowercase_			)

						# Test 4)
						__UpperCAmelCase :       Optional[Any]  						=  '''abcdabcy'''
						__UpperCAmelCase :       Any  						=  '''abcxabcdabxabcdabcdabcy'''
						assert rabin_karp(lowercase_			,					lowercase_			)

						# Test 5)
						__UpperCAmelCase :       Any  						=  '''Lü'''
						__UpperCAmelCase :       Optional[int]  						=  '''Lüsai'''
						assert rabin_karp(lowercase_			,					lowercase_			)
						__UpperCAmelCase :       List[Any]  						=  '''Lue'''
						assert not rabin_karp(lowercase_			,					lowercase_			)
						print('''Success.'''			)


if __name__ == "__main__":
				test_rabin_karp()

