
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
#
########################################################################


_lowercase											=    16
_lowercase											=    32
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Accelerator			,							UpperCAmelCase_	:     int = 1_6				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				str		=AutoTokenizer.from_pretrained('''bert-base-cased'''				)
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=load_dataset('''glue'''			,							'''mrpc'''				)

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

       def collate_fn(UpperCAmelCase_	:     str				):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     SCREAMING_SNAKE_CASE_							:				List[str]		=1_6
              elif accelerator.mixed_precision != "no":
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              else:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=None

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

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

       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

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

       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=evaluate.load('''glue'''			,							'''mrpc'''				)

       set_seed(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=get_dataloaders(UpperCAmelCase_			,							UpperCAmelCase_				)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       SCREAMING_SNAKE_CASE_							:				Dict		=AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,							return_dict=UpperCAmelCase_				)

       # 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).
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=model.to(accelerator.device				)

       # Instantiate optimizer
       SCREAMING_SNAKE_CASE_							:				str		=AdamW(params=model.parameters()			,							lr=UpperCAmelCase_				)

       # Instantiate scheduler
       SCREAMING_SNAKE_CASE_							:				str		=get_linear_schedule_with_warmup(
           optimizer=UpperCAmelCase_			,							num_warmup_steps=1_0_0			,							num_training_steps=(len(UpperCAmelCase_				) * 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.
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=accelerator.prepare(
           UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)

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

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

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'epoch {epoch}:'			,							UpperCAmelCase_				)
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=argparse.ArgumentParser(description='''Simple example of training script.'''				)
       parser.add_argument(
           '''--mixed_precision'''			,							type=UpperCAmelCase_			,							default=UpperCAmelCase_			,							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=UpperCAmelCase_			,							default=1			,							help='''The number of minibatches to be ran before gradients are accumulated.'''			,							)
       parser.add_argument(
           '''--local_sgd_steps'''			,							type=UpperCAmelCase_			,							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.'''				)
       SCREAMING_SNAKE_CASE_							:				Dict		=parser.parse_args()
       SCREAMING_SNAKE_CASE_							:				int		={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
       training_function(UpperCAmelCase_			,							UpperCAmelCase_				)


if __name__ == "__main__":
 main()


import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8				)			->						str:
       SCREAMING_SNAKE_CASE_							:				str		=ascii_letters + digits + punctuation
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       # Password Generator = full boot with random_number, random_letters, and
       # random_character FUNCTIONS
       # Put your code here...
       i -= len(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Any		=i // 3
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=i % 3
       # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
       #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
       SCREAMING_SNAKE_CASE_							:				int		=(
           chars_incl
           + random(UpperCAmelCase_			,							quotient + remainder				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
           + random(UpperCAmelCase_			,							UpperCAmelCase_				)
       )
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(UpperCAmelCase_				)
       shuffle(UpperCAmelCase_				)
       return "".join(UpperCAmelCase_				)

       # random is a generalised function for letters, characters and numbers
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int				)			->						str:
       return "".join(secrets.choice(UpperCAmelCase_				) for _ in range(UpperCAmelCase_				)				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Tuple			,							UpperCAmelCase_	:     Tuple				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Tuple				)			->						Dict:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Dict			,							UpperCAmelCase_	:     List[str]				)			->						Optional[int]:
       pass  # Put your code here...
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int = 8				)			->						bool:

       if len(UpperCAmelCase_				) < min_length:
              # Your Password must be at least 8 characters long
              return False

       SCREAMING_SNAKE_CASE_							:				List[str]		=any(char in ascii_uppercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=any(char in ascii_lowercase for char in password				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=any(char in digits for char in password				)
       SCREAMING_SNAKE_CASE_							:				str		=any(char in punctuation for char in password				)

       return upper and lower and num and spec_char
       # Passwords should contain UPPERCASE, lowerase
       # numbers, and special characters
def 							SCREAMING_SNAKE_CASE_	(					)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Any		=int(input('''Please indicate the max length of your password: '''				).strip()				)
       SCREAMING_SNAKE_CASE_							:				Any		=input(
           '''Please indicate the characters that must be in your password: '''				).strip()
       print('''Password generated:'''			,							password_generator(UpperCAmelCase_				)				)
       print(
           '''Alternative Password generated:'''			,							alternative_password_generator(UpperCAmelCase_			,							UpperCAmelCase_				)			,							)
       print('''[If you are thinking of using this passsword, You better save it.]'''				)


if __name__ == "__main__":
 main()
