





"""simple docstring"""


import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


a__    :   str    						= 1_6
a__    :   List[Any]    						= 3_2





def 							a__	(     _UpperCAmelCase		)			->     Union[str, Any]:

 '''simple docstring'''






 return int(x / 2**2_0		)



class    lowercase_ :

       def __enter__(	self   ):






        '''simple docstring'''

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        snake_case__										= torch.cuda.memory_allocated()
        snake_case__										= psutil.Process()

        snake_case__										= self.cpu_mem_used()
        snake_case__										= True
        snake_case__										= threading.Thread(target=self.peak_monitor_func   )
        snake_case__										= True
        peak_monitor_thread.start()
        return self

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return self.process.memory_info().rss

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= -1

        while True:
         snake_case__										= max(self.cpu_mem_used()      ,    self.cpu_peak   )

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         # time.sleep(0.001) # 1msec

         if not self.peak_monitoring:
          break

       def __exit__(	self      ,    *__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= False

        gc.collect()
        torch.cuda.empty_cache()
        snake_case__										= torch.cuda.memory_allocated()
        snake_case__										= torch.cuda.max_memory_allocated()
        snake_case__										= bamb(self.end - self.begin   )
        snake_case__										= bamb(self.peak - self.begin   )

        snake_case__										= self.cpu_mem_used()
        snake_case__										= bamb(self.cpu_end - self.cpu_begin   )
        snake_case__										= bamb(self.cpu_peak - self.cpu_begin   )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   a__    :   Union[str, Any]    						= mocked_dataloaders  # noqa: F811





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 if os.environ.get("TESTING_MOCKED_DATALOADERS"		,   _UpperCAmelCase		) == "1":
  snake_case__										= 2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 snake_case__										= FullyShardedDataParallelPlugin(
     state_dict_config=FullStateDictConfig(offload_to_cpu=_UpperCAmelCase		,   ranka_only=_UpperCAmelCase		)		,   optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_UpperCAmelCase		,   ranka_only=_UpperCAmelCase		)		,   )

 # Initialize accelerator
 if args.with_tracking:
  snake_case__										= Accelerator(
      cpu=args.cpu		,   mixed_precision=args.mixed_precision		,   log_with="wandb"		,   project_dir=args.logging_dir		,   fsdp_plugin=_UpperCAmelCase		,   )
 else:
  snake_case__										= Accelerator(fsdp_plugin=_UpperCAmelCase		)
 accelerator.print(accelerator.distributed_type		)

 if hasattr(args.checkpointing_steps		,   "isdigit"		):
  if args.checkpointing_steps == "epoch":
   snake_case__										= args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   snake_case__										= int(args.checkpointing_steps		)
  else:
   raise ValueError(
       f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."		)
 else:
  snake_case__										= None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 snake_case__										= config["lr"]
 snake_case__										= int(config["num_epochs"]		)
 snake_case__										= int(config["seed"]		)
 snake_case__										= int(config["batch_size"]		)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  snake_case__										= vars(_UpperCAmelCase		)
  accelerator.init_trackers("fsdp_glue_no_trainer"		,   _UpperCAmelCase		)

 snake_case__										= AutoTokenizer.from_pretrained(args.model_name_or_path		)
 snake_case__										= load_dataset("glue"		,   "mrpc"		)
 snake_case__										= evaluate.load("glue"		,   "mrpc"		)

 def tokenize_function(_UpperCAmelCase		):
  # max_length=None => use the model max length (it's actually the default)
  snake_case__										= 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():
  snake_case__										= 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
 snake_case__										= tokenized_datasets.rename_column("label"		,   "labels"		)

 # If the batch size is too big we use gradient accumulation
 snake_case__										= 1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  snake_case__										= batch_size // MAX_GPU_BATCH_SIZE
  snake_case__										= MAX_GPU_BATCH_SIZE

 def collate_fn(_UpperCAmelCase		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  snake_case__										= 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":
   snake_case__										= 1_6
  elif accelerator.mixed_precision != "no":
   snake_case__										= 8
  else:
   snake_case__										= None

  return tokenizer.pad(
      _UpperCAmelCase		,   padding="longest"		,   max_length=_UpperCAmelCase		,   pad_to_multiple_of=_UpperCAmelCase		,   return_tensors="pt"		,   )

 # Instantiate dataloaders.
 snake_case__										= DataLoader(
     tokenized_datasets["train"]		,   shuffle=_UpperCAmelCase		,   collate_fn=_UpperCAmelCase		,   batch_size=_UpperCAmelCase		)
 snake_case__										= DataLoader(
     tokenized_datasets["validation"]		,   shuffle=_UpperCAmelCase		,   collate_fn=_UpperCAmelCase		,   batch_size=_UpperCAmelCase		)

 set_seed(_UpperCAmelCase		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 snake_case__										= AutoModelForSequenceClassification.from_pretrained(
     args.model_name_or_path		,   return_dict=_UpperCAmelCase		,   low_cpu_mem_usage=_UpperCAmelCase		)

 # New Code #
 # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
 snake_case__										= accelerator.prepare(_UpperCAmelCase		)
 accelerator.print(_UpperCAmelCase		)

 # Instantiate optimizer
 # New Code #
 # For FSDP feature, at present it doesn't support multiple parameter groups,
 # so we need to create a single parameter group for the whole model
 snake_case__										= torch.optim.AdamW(params=model.parameters()		,   lr=_UpperCAmelCase		,   weight_decay=2e-4		)

 # Instantiate scheduler
 snake_case__										= get_linear_schedule_with_warmup(
     optimizer=_UpperCAmelCase		,   num_warmup_steps=1_0		,   num_training_steps=(len(_UpperCAmelCase		) * num_epochs) // gradient_accumulation_steps		,   )

 # New Code #
 # For FSDP feature, prepare everything except the model as we have already prepared the model
 # before creating the optimizer
 # 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.
 snake_case__							,      snake_case__							,      snake_case__							,      snake_case__										= accelerator.prepare(
     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)

 snake_case__										= 0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"		)
   accelerator.load_state(args.resume_from_checkpoint		)
   snake_case__										= os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   snake_case__										= [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   snake_case__										= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  snake_case__										= os.path.splitext(_UpperCAmelCase		)[0]

  if "epoch" in training_difference:
   num_epochs -= int(training_difference.replace("epoch_"		,   ""		)		)
   snake_case__										= None
  else:
   snake_case__										= int(training_difference.replace("step_"		,   ""		)		)
   num_epochs -= resume_step // len(_UpperCAmelCase		)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   snake_case__										= (num_epochs * len(_UpperCAmelCase		)) - resume_step

    # Now we train the model
 for epoch in range(_UpperCAmelCase		):
  # New Code #
  # context manager to track the peak memory usage during the training epoch
  with TorchTracemalloc() as tracemalloc:
   model.train()
   if args.with_tracking:
    snake_case__										= 0
   for step, batch in enumerate(_UpperCAmelCase		):
    # We need to skip steps until we reach the resumed step
    if args.resume_from_checkpoint and epoch == 0:
     if resume_step is not None and step < resume_step:
      pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device		)
    snake_case__										= model(**_UpperCAmelCase		)
    snake_case__										= outputs.loss
    snake_case__										= loss / gradient_accumulation_steps
    # We keep track of the loss at each epoch
    if args.with_tracking:
     total_loss += loss.detach().float()
    accelerator.backward(_UpperCAmelCase		)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # accelerator.print(lr_scheduler.get_lr())

    overall_step += 1

    if isinstance(_UpperCAmelCase		,   _UpperCAmelCase		):
     snake_case__										= f"step_{overall_step}"
     if overall_step % checkpointing_steps == 0:
      if args.output_dir is not None:
       snake_case__										= os.path.join(args.output_dir		,   _UpperCAmelCase		)
      accelerator.save_state(_UpperCAmelCase		)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin		)		)		)
  accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used		)		)
  accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked		)		)
  accelerator.print(
      "Total Peak Memory consumed during the train (max): {}".format(
          tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin		),
       }		,   step=_UpperCAmelCase		,   )

  # New Code #
  # context manager to track the peak memory usage during the evaluation
  with TorchTracemalloc() as tracemalloc:
   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():
     snake_case__										= model(**_UpperCAmelCase		)
    snake_case__										= outputs.logits.argmax(dim=-1		)
    snake_case__							,      snake_case__										= accelerator.gather_for_metrics((predictions, batch["labels"])		)
    metric.add_batch(
        predictions=_UpperCAmelCase		,   references=_UpperCAmelCase		,   )

   snake_case__										= metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f"epoch {epoch}:"		,   _UpperCAmelCase		)
   if args.with_tracking:
    accelerator.log(
        {
            "accuracy": eval_metric["accuracy"],
            "f1": eval_metric["f1"],
            "train_loss": total_loss.item() / len(_UpperCAmelCase		),
        }		,   step=_UpperCAmelCase		,   )

   if checkpointing_steps == "epoch":
    snake_case__										= f"epoch_{epoch}"
    if args.output_dir is not None:
     snake_case__										= os.path.join(args.output_dir		,   _UpperCAmelCase		)
    accelerator.save_state(_UpperCAmelCase		)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin		)		)		)
  accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used		)		)
  accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked		)		)
  accelerator.print(
      "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin		),
       }		,   step=_UpperCAmelCase		,   )

 if args.with_tracking:
  accelerator.end_training()





def 							a__	(     )			->     Dict:

 '''simple docstring'''






 snake_case__										= 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."		,   )
 parser.add_argument("--cpu"		,   action="store_true"		,   help="If passed, will train on the CPU."		)
 parser.add_argument(
     "--checkpointing_steps"		,   type=_UpperCAmelCase		,   default=_UpperCAmelCase		,   help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."		,   )
 parser.add_argument(
     "--resume_from_checkpoint"		,   type=_UpperCAmelCase		,   default=_UpperCAmelCase		,   help="If the training should continue from a checkpoint folder."		,   )
 parser.add_argument(
     "--with_tracking"		,   action="store_true"		,   help="Whether to load in all available experiment trackers from the environment and use them for logging."		,   )
 parser.add_argument(
     "--output_dir"		,   type=_UpperCAmelCase		,   default="."		,   help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,   )
 parser.add_argument(
     "--logging_dir"		,   type=_UpperCAmelCase		,   default="logs"		,   help="Location on where to store experiment tracking logs`"		,   )
 parser.add_argument(
     "--model_name_or_path"		,   type=_UpperCAmelCase		,   help="Path to pretrained model or model identifier from huggingface.co/models."		,   required=_UpperCAmelCase		,   )
 snake_case__										= parser.parse_args()
 snake_case__										= {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
 training_function(_UpperCAmelCase		,   _UpperCAmelCase		)


if __name__ == "__main__":
   main()








"""simple docstring"""


import random
import sys

from . import cryptomath_module as cryptomath

a__    :   int    						= (
    r""" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"""
    r"""abcdefghijklmnopqrstuvwxyz{|}~"""
)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     None:

 '''simple docstring'''






 if mode == "encrypt":
  if key_a == 1:
   sys.exit(
       "The affine cipher becomes weak when key "
       "A is set to 1. Choose different key"		)
  if key_b == 0:
   sys.exit(
       "The affine cipher becomes weak when key "
       "B is set to 0. Choose different key"		)
 if key_a < 0 or key_b < 0 or key_b > len(_UpperCAmelCase		) - 1:
  sys.exit(
      "Key A must be greater than 0 and key B must "
      f"be between 0 and {len(_UpperCAmelCase		) - 1}."		)
 if cryptomath.gcd(_UpperCAmelCase		,   len(_UpperCAmelCase		)		) != 1:
  sys.exit(
      f"Key A {key_a} and the symbol set size {len(_UpperCAmelCase		)} "
      "are not relatively prime. Choose a different key."		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     str:

 '''simple docstring'''






 snake_case__							,      snake_case__										= divmod(_UpperCAmelCase		,   len(_UpperCAmelCase		)		)
 check_keys(_UpperCAmelCase		,   _UpperCAmelCase		,   "encrypt"		)
 snake_case__										= ""
 for symbol in message:
  if symbol in SYMBOLS:
   snake_case__										= SYMBOLS.find(_UpperCAmelCase		)
   cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_UpperCAmelCase		)]
  else:
   cipher_text += symbol
 return cipher_text





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     str:

 '''simple docstring'''






 snake_case__							,      snake_case__										= divmod(_UpperCAmelCase		,   len(_UpperCAmelCase		)		)
 check_keys(_UpperCAmelCase		,   _UpperCAmelCase		,   "decrypt"		)
 snake_case__										= ""
 snake_case__										= cryptomath.find_mod_inverse(_UpperCAmelCase		,   len(_UpperCAmelCase		)		)
 for symbol in message:
  if symbol in SYMBOLS:
   snake_case__										= SYMBOLS.find(_UpperCAmelCase		)
   plain_text += SYMBOLS[
       (sym_index - key_b) * mod_inverse_of_key_a % len(_UpperCAmelCase		)
   ]
  else:
   plain_text += symbol
 return plain_text





def 							a__	(     )			->     int:

 '''simple docstring'''






 while True:
  snake_case__										= random.randint(2		,   len(_UpperCAmelCase		)		)
  snake_case__										= random.randint(2		,   len(_UpperCAmelCase		)		)
  if cryptomath.gcd(_UpperCAmelCase		,   len(_UpperCAmelCase		)		) == 1 and key_b % len(_UpperCAmelCase		) != 0:
   return key_b * len(_UpperCAmelCase		) + key_b





def 							a__	(     )			->     None:

 '''simple docstring'''






 snake_case__										= input("Enter message: "		).strip()
 snake_case__										= int(input("Enter key [2000 - 9000]: "		).strip()		)
 snake_case__										= input("Encrypt/Decrypt [E/D]: "		).strip().lower()

 if mode.startswith("e"		):
  snake_case__										= "encrypt"
  snake_case__										= encrypt_message(_UpperCAmelCase		,   _UpperCAmelCase		)
 elif mode.startswith("d"		):
  snake_case__										= "decrypt"
  snake_case__										= decrypt_message(_UpperCAmelCase		,   _UpperCAmelCase		)
 print(f"\n{mode.title()}ed text: \n{translated}"		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # main()

