
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							=       16
_A							=       32




def 					lowerCamelCase__					(    a__	:	List[str]  )		->						Union[str, Any]:
       return int(x / 2**20  )

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
        UpperCamelCase_											=							torch.cuda.memory_allocated()
        UpperCamelCase_											=							psutil.Process()

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self.process.memory_info().rss

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							-1

        while True:
               UpperCamelCase_											=							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  ,						*__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							False

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

        UpperCamelCase_											=							self.cpu_mem_used()
        UpperCamelCase_											=							bamb(self.cpu_end - self.cpu_begin  )
        UpperCamelCase_											=							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							=       mocked_dataloaders  # noqa: F811




def 					lowerCamelCase__					(    a__	:	List[str]						,   a__	:	List[Any]  )		->						List[Any]:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,   a__  ) == "1":
              UpperCamelCase_											=							2

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

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

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

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

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

       def tokenize_function(a__	:	Optional[Any]  ):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_											=							tokenizer(examples["""sentence1"""]						,   examples["""sentence2"""]						,   truncation=a__						,   max_length=a__  )
              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_											=							datasets.map(
                  a__						,   batched=a__						,   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_											=							tokenized_datasets.rename_column("""label"""						,   """labels"""  )

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

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

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

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

       set_seed(a__  )

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

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

       # 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
       UpperCamelCase_											=							torch.optim.AdamW(params=model.parameters()						,   lr=a__						,   weight_decay=2e-4  )

       # Instantiate scheduler
       UpperCamelCase_											=							get_linear_schedule_with_warmup(
           optimizer=a__						,   num_warmup_steps=10						,   num_training_steps=(len(a__  ) * 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.
       UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							accelerator.prepare(
           a__						,   a__						,   a__						,   a__  )

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

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

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

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

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

                     if checkpointing_steps == "epoch":
                            UpperCamelCase_											=							f'''epoch_{epoch}'''
                            if args.output_dir is not None:
                                   UpperCamelCase_											=							os.path.join(args.output_dir						,   a__  )
                            accelerator.save_state(a__  )
        # 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=a__						,   )

       if args.with_tracking:
              accelerator.end_training()




def 					lowerCamelCase__					(    )		->						List[Any]:
       UpperCamelCase_											=							argparse.ArgumentParser(description="""Simple example of training script."""  )
       parser.add_argument(
           """--mixed_precision"""						,   type=a__						,   default=a__						,   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=a__						,   default=a__						,   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=a__						,   default=a__						,   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=a__						,   default="""."""						,   help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""						,   )
       parser.add_argument(
           """--logging_dir"""						,   type=a__						,   default="""logs"""						,   help="""Location on where to store experiment tracking logs`"""						,   )
       parser.add_argument(
           """--model_name_or_path"""						,   type=a__						,   help="""Path to pretrained model or model identifier from huggingface.co/models."""						,   required=a__						,   )
       UpperCamelCase_											=							parser.parse_args()
       UpperCamelCase_											=							{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(a__						,   a__  )


if __name__ == "__main__":
      main()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A							=       logging.get_logger(__name__)

_A							=       {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Dict    =     """opt"""
 A__    :					int    =     ["""past_key_values"""]

 def __init__(   self  ,						__UpperCamelCase=5_0_2_7_2  ,						__UpperCamelCase=7_6_8  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=3_0_7_2  ,						__UpperCamelCase=2_0_4_8  ,						__UpperCamelCase=True  ,						__UpperCamelCase=False  ,						__UpperCamelCase=None  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase="relu"  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=True  ,						__UpperCamelCase=1  ,						__UpperCamelCase=2  ,						__UpperCamelCase=2  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            pad_token_id=__UpperCamelCase  ,						bos_token_id=__UpperCamelCase  ,						eos_token_id=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							max_position_embeddings
        UpperCamelCase_											=							num_attention_heads
        UpperCamelCase_											=							word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        UpperCamelCase_											=							ffn_dim
        UpperCamelCase_											=							hidden_size
        UpperCamelCase_											=							num_hidden_layers
        UpperCamelCase_											=							dropout
        UpperCamelCase_											=							attention_dropout
        UpperCamelCase_											=							activation_function
        UpperCamelCase_											=							init_std
        UpperCamelCase_											=							layerdrop
        UpperCamelCase_											=							use_cache
        UpperCamelCase_											=							do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        UpperCamelCase_											=							enable_bias
        UpperCamelCase_											=							layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        UpperCamelCase_											=							_remove_final_layer_norm



