




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,
# specifically showcasing the checkpointing capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

__SCREAMING_SNAKE_CASE			:      Dict      			=16
__SCREAMING_SNAKE_CASE			:      List[Any]      			=32

def 	lowerCAmelCase_	(     lowerCamelCase:				Accelerator   , lowerCamelCase:				int = 16  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = AutoTokenizer.from_pretrained('bert-base-cased'  )
      lowerCamelCase__					     = load_dataset('glue'   , 'mrpc'  )

      def tokenize_function(lowerCamelCase:				Union[str, Any]  ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase__					     = tokenizer(examples['sentence1']   , examples['sentence2']   , truncation=lowerCamelCase   , max_length=lowerCamelCase  )
            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(
                lowerCamelCase   , batched=lowerCamelCase   , 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(lowerCamelCase:				List[Any]  ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowerCamelCase__					     = 1_28 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(
                lowerCamelCase   , padding='longest'   , max_length=lowerCamelCase   , pad_to_multiple_of=lowerCamelCase   , return_tensors='pt'   , )

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

      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

      __SCREAMING_SNAKE_CASE			:      List[str]      			=mocked_dataloaders  # noqa: F811

def 	lowerCAmelCase_	(     lowerCamelCase:				List[Any]   , lowerCamelCase:				int  )    ->	Dict:


      '''simple docstring'''




      if os.environ.get('TESTING_MOCKED_DATALOADERS'   , lowerCamelCase  ) == "1":
            lowerCamelCase__					     = 2
      # Initialize accelerator
      lowerCamelCase__					     = Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision  )
      # 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']  )

      # New Code #
      # Parse out whether we are saving every epoch or after a certain number of batches
      if hasattr(args.checkpointing_steps   , 'isdigit'  ):
            if args.checkpointing_steps == "epoch":
                  lowerCamelCase__					     = args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  lowerCamelCase__					     = int(args.checkpointing_steps  )
            else:
                  raise ValueError(
                      f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""  )
      else:
            lowerCamelCase__					     = None

      set_seed(lowerCamelCase  )

      lowerCamelCase__     ,						lowerCamelCase__					     = get_dataloaders(lowerCamelCase   , lowerCamelCase  )
      lowerCamelCase__					     = evaluate.load('glue'   , 'mrpc'  )

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

      # 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=lowerCamelCase  )

      # 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=lowerCamelCase  )

      # Instantiate scheduler
      lowerCamelCase__					     = get_linear_schedule_with_warmup(
          optimizer=lowerCamelCase   , num_warmup_steps=1_00   , num_training_steps=(len(lowerCamelCase  ) * num_epochs) // gradient_accumulation_steps   , )

      # 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(
          lowerCamelCase   , lowerCamelCase   , lowerCamelCase   , lowerCamelCase   , lowerCamelCase  )

      # New Code #
      # We need to keep track of how many total steps we have iterated over
      lowerCamelCase__					     = 0
      # We also need to keep track of the stating epoch so files are named properly
      lowerCamelCase__					     = 0

      # We need to load the checkpoint back in before training here with `load_state`
      # The total number of epochs is adjusted based on where the state is being loaded from,
      # as we assume continuation of the same training script
      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  )
                  lowerCamelCase__					     = os.path.basename(args.resume_from_checkpoint  )
            else:
                  # Get the most recent checkpoint
                  lowerCamelCase__					     = [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
                  dirs.sort(key=os.path.getctime  )
                  lowerCamelCase__					     = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            lowerCamelCase__					     = os.path.splitext(lowerCamelCase  )[0]

            if "epoch" in training_difference:
                  lowerCamelCase__					     = int(training_difference.replace('epoch_'   , ''  )  ) + 1
                  lowerCamelCase__					     = None
            else:
                  lowerCamelCase__					     = int(training_difference.replace('step_'   , ''  )  )
                  lowerCamelCase__					     = resume_step // len(lowerCamelCase  )
                  resume_step -= starting_epoch * len(lowerCamelCase  )

    # Now we train the model
      for epoch in range(lowerCamelCase   , lowerCamelCase  ):
            model.train()
            # New Code #
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                  # We need to skip steps until we reach the resumed step
                  lowerCamelCase__					     = accelerator.skip_first_batches(lowerCamelCase   , lowerCamelCase  )
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  lowerCamelCase__					     = train_dataloader
            for step, batch in enumerate(lowerCamelCase  ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device  )
                  lowerCamelCase__					     = model(**lowerCamelCase  )
                  lowerCamelCase__					     = outputs.loss
                  lowerCamelCase__					     = loss / gradient_accumulation_steps
                  accelerator.backward(lowerCamelCase  )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()
                  # New Code #
                  overall_step += 1

                  # New Code #
                  # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                  # These are saved to folders named `step_{overall_step}`
                  # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                  # If mixed precision was used, will also save a "scalar.bin" file
                  if isinstance(lowerCamelCase   , lowerCamelCase  ):
                        lowerCamelCase__					     = f"""step_{overall_step}"""
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    lowerCamelCase__					     = os.path.join(args.output_dir   , lowerCamelCase  )
                              accelerator.save_state(lowerCamelCase  )

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

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

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `epoch_{epoch}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if checkpointing_steps == "epoch":
                  lowerCamelCase__					     = f"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCamelCase__					     = os.path.join(args.output_dir   , lowerCamelCase  )
                  accelerator.save_state(lowerCamelCase  )

def 	lowerCAmelCase_	(     )    ->	List[Any]:


      '''simple docstring'''




      lowerCamelCase__					     = argparse.ArgumentParser(description='Simple example of training script.'  )
      parser.add_argument(
          '--mixed_precision'   , type=lowerCamelCase   , default=lowerCamelCase   , 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=lowerCamelCase   , default=lowerCamelCase   , help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'   , )
      parser.add_argument(
          '--output_dir'   , type=lowerCamelCase   , default='.'   , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'   , )
      parser.add_argument(
          '--resume_from_checkpoint'   , type=lowerCamelCase   , default=lowerCamelCase   , help='If the training should continue from a checkpoint folder.'   , )
      lowerCamelCase__					     = parser.parse_args()
      lowerCamelCase__					     = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(lowerCamelCase   , lowerCamelCase  )


if __name__ == "__main__":
      main()





from __future__ import annotations

import sys






class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      str		,     A__     :      int	) ->				str:
           lowerCamelCase__					     = letter
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = {}




     def __repr__(			self     :      Any	) ->				str:
           return F"""{self.letter}:{self.freq}"""






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Optional[Any]		,     A__     :      int		,     A__     :      Letter | TreeNode		,     A__     :      Letter | TreeNode	) ->				Any:
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = left
           lowerCamelCase__					     = right

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      lowerCamelCase__					     = {}
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  lowerCamelCase__					     = chars[c] + 1 if c in chars else 1
      return sorted((Letter(lowerCamelCase   , lowerCamelCase  ) for c, f in chars.items())   , key=lambda lowerCamelCase  : x.freq  )

def 	lowerCAmelCase_	(     lowerCamelCase:				list[Letter]  )    ->	Letter | TreeNode:


      '''simple docstring'''




      lowerCamelCase__					     = letters  # type: ignore
      while len(lowerCamelCase  ) > 1:
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = left.freq + right.freq
            lowerCamelCase__					     = TreeNode(lowerCamelCase   , lowerCamelCase   , lowerCamelCase  )
            response.append(lowerCamelCase  )
            response.sort(key=lambda lowerCamelCase  : x.freq  )
      return response[0]

def 	lowerCAmelCase_	(     lowerCamelCase:				Letter | TreeNode   , lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      if isinstance(lowerCamelCase   , lowerCamelCase  ):
            lowerCamelCase__					     = bitstring
            return [root]
      lowerCamelCase__					     = root  # type: ignore
      lowerCamelCase__					     = []
      letters += traverse_tree(treenode.left   , bitstring + '0'  )
      letters += traverse_tree(treenode.right   , bitstring + '1'  )
      return letters

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	None:


      '''simple docstring'''




      lowerCamelCase__					     = parse_file(lowerCamelCase  )
      lowerCamelCase__					     = build_tree(lowerCamelCase  )
      lowerCamelCase__					     = {
          k: v for letter in traverse_tree(lowerCamelCase   , ''  ) for k, v in letter.bitstring.items()
      }
      print(f"""Huffman Coding  of {file_path}: """  )
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  print(letters[c]   , end=' '  )
      print()


if __name__ == "__main__":
      # pass the file path to the huffman function
      huffman(sys.argv[1])