




'''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
#
########################################################################


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




def 		a_( lowerCamelCase      )				->Any:






       '''simple docstring'''




       return int(x / 2**20      )

class   SCREAMING_SNAKE_CASE_		:



 def __enter__(  self ):






        '''simple docstring'''




        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowercase  :		Optional[int]						=      torch.cuda.memory_allocated()
        lowercase  :		Any						=      psutil.Process()

        lowercase  :		Union[str, Any]						=      self.cpu_mem_used()
        lowercase  :		str						=      True
        lowercase  :		List[str]						=      threading.Thread(target=self.peak_monitor_func )
        lowercase  :		List[Any]						=      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'''




        lowercase  :		Optional[int]						=      -1

        while True:
               lowercase  :		Optional[int]						=      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   ,		*A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      False

        gc.collect()
        torch.cuda.empty_cache()
        lowercase  :		int						=      torch.cuda.memory_allocated()
        lowercase  :		Tuple						=      torch.cuda.max_memory_allocated()
        lowercase  :		List[Any]						=      bamb(self.end - self.begin )
        lowercase  :		Optional[int]						=      bamb(self.peak - self.begin )

        lowercase  :		List[Any]						=      self.cpu_mem_used()
        lowercase  :		str						=      bamb(self.cpu_end - self.cpu_begin )
        lowercase  :		str						=      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

      lowercase	:      int				=       mocked_dataloaders  # noqa: F811




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->Any:






       '''simple docstring'''




       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,				lowerCamelCase      ) == "1":
              lowercase  :		Union[str, Any]						=      2

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

       # Initialize accelerator
       if args.with_tracking:
              lowercase  :		Dict						=      Accelerator(
                  cpu=args.cpu   ,				mixed_precision=args.mixed_precision   ,				log_with='''wandb'''   ,				project_dir=args.logging_dir   ,				fsdp_plugin=lowerCamelCase   ,				)
       else:
              lowercase  :		Optional[int]						=      Accelerator(fsdp_plugin=lowerCamelCase      )
       accelerator.print(accelerator.distributed_type      )

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

       # We need to initialize the trackers we use, and also store our configuration
       if args.with_tracking:
              lowercase  :		Optional[int]						=      vars(lowerCamelCase      )
              accelerator.init_trackers('''fsdp_glue_no_trainer'''   ,				lowerCamelCase      )

       lowercase  :		Tuple						=      AutoTokenizer.from_pretrained(args.model_name_or_path      )
       lowercase  :		Any						=      load_dataset('''glue'''   ,				'''mrpc'''      )
       lowercase  :		Dict						=      evaluate.load('''glue'''   ,				'''mrpc'''      )

       def tokenize_function(lowerCamelCase      ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase  :		List[str]						=      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():
              lowercase  :		List[Any]						=      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
       lowercase  :		Optional[Any]						=      tokenized_datasets.rename_column('''label'''   ,				'''labels'''      )

       # If the batch size is too big we use gradient accumulation
       lowercase  :		Dict						=      1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              lowercase  :		Optional[Any]						=      batch_size // MAX_GPU_BATCH_SIZE
              lowercase  :		Tuple						=      MAX_GPU_BATCH_SIZE

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

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

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

       set_seed(lowerCamelCase      )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase  :		Union[str, Any]						=      AutoModelForSequenceClassification.from_pretrained(
           args.model_name_or_path   ,				return_dict=lowerCamelCase   ,				low_cpu_mem_usage=lowerCamelCase      )

       # New Code #
       # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
       lowercase  :		List[str]						=      accelerator.prepare(lowerCamelCase      )
       accelerator.print(lowerCamelCase      )

       # 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
       lowercase  :		int						=      torch.optim.AdamW(params=model.parameters()   ,				lr=lowerCamelCase   ,				weight_decay=2E-4      )

       # Instantiate scheduler
       lowercase  :		Any						=      get_linear_schedule_with_warmup(
           optimizer=lowerCamelCase   ,				num_warmup_steps=10   ,				num_training_steps=(len(lowerCamelCase      ) * 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.
       lowercase,     lowercase,     lowercase,     lowercase  :		Optional[Any]						=      accelerator.prepare(
           lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )

       lowercase  :		List[str]						=      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      )
                     lowercase  :		int						=      os.path.basename(args.resume_from_checkpoint      )
              else:
                     # Get the most recent checkpoint
                     lowercase  :		Any						=      [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime      )
                     lowercase  :		int						=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              lowercase  :		Tuple						=      os.path.splitext(lowerCamelCase      )[0]

              if "epoch" in training_difference:
                     num_epochs -= int(training_difference.replace('''epoch_'''   ,				''''''      )      )
                     lowercase  :		Optional[Any]						=      None
              else:
                     lowercase  :		Tuple						=      int(training_difference.replace('''step_'''   ,				''''''      )      )
                     num_epochs -= resume_step // len(lowerCamelCase      )
                     # If resuming by step, we also need to know exactly how far into the DataLoader we went
                     lowercase  :		str						=      (num_epochs * len(lowerCamelCase      )) - resume_step

    # Now we train the model
       for epoch in range(lowerCamelCase      ):
              # New Code #
              # context manager to track the peak memory usage during the training epoch
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     if args.with_tracking:
                            lowercase  :		List[Any]						=      0
                     for step, batch in enumerate(lowerCamelCase      ):
                            # 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      )
                            lowercase  :		Optional[Any]						=      model(**lowerCamelCase      )
                            lowercase  :		Union[str, Any]						=      outputs.loss
                            lowercase  :		int						=      loss / gradient_accumulation_steps
                            # We keep track of the loss at each epoch
                            if args.with_tracking:
                                   total_loss += loss.detach().float()
                            accelerator.backward(lowerCamelCase      )
                            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(lowerCamelCase   ,				lowerCamelCase      ):
                                   lowercase  :		Optional[Any]						=      f"""step_{overall_step}"""
                                   if overall_step % checkpointing_steps == 0:
                                          if args.output_dir is not None:
                                                 lowercase  :		Optional[int]						=      os.path.join(args.output_dir   ,				lowerCamelCase      )
                                          accelerator.save_state(lowerCamelCase      )
        # 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=lowerCamelCase   ,				)

              # New Code #
              # context manager to track the peak memory usage during the evaluation
              with TorchTracemalloc() as tracemalloc:
                     model.eval()
                     for step, batch in enumerate(lowerCamelCase      ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device      )
                            with torch.no_grad():
                                   lowercase  :		List[str]						=      model(**lowerCamelCase      )
                            lowercase  :		Optional[Any]						=      outputs.logits.argmax(dim=-1      )
                            lowercase,     lowercase  :		Union[str, Any]						=      accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
                            metric.add_batch(
                                predictions=lowerCamelCase   ,				references=lowerCamelCase   ,				)

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

                     if checkpointing_steps == "epoch":
                            lowercase  :		str						=      f"""epoch_{epoch}"""
                            if args.output_dir is not None:
                                   lowercase  :		Optional[Any]						=      os.path.join(args.output_dir   ,				lowerCamelCase      )
                            accelerator.save_state(lowerCamelCase      )
        # 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=lowerCamelCase   ,				)

       if args.with_tracking:
              accelerator.end_training()




def 		a_( )				->Optional[Any]:






       '''simple docstring'''




       lowercase  :		Dict						=      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(
           '''--resume_from_checkpoint'''   ,				type=lowerCamelCase   ,				default=lowerCamelCase   ,				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=lowerCamelCase   ,				default='''.'''   ,				help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,				)
       parser.add_argument(
           '''--logging_dir'''   ,				type=lowerCamelCase   ,				default='''logs'''   ,				help='''Location on where to store experiment tracking logs`'''   ,				)
       parser.add_argument(
           '''--model_name_or_path'''   ,				type=lowerCamelCase   ,				help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,				required=lowerCamelCase   ,				)
       lowercase  :		List[str]						=      parser.parse_args()
       lowercase  :		Optional[int]						=      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowerCamelCase   ,				lowerCamelCase      )


if __name__ == "__main__":
      main()











'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




