


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







def _UpperCamelCase	(					__A   )							->		int:


      '''simple docstring'''



      return int(x / 2**20   )
class  lowercase_					:


    def __enter__(					self   ):
          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        __a							(					self   ):
          return self.process.memory_info().rss


    def        __a							(					self   ):
          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  , *a   ):
          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__  : List[str]				=	mocked_dataloaders  # noqa: F811







def _UpperCamelCase	(					__A				,      __A   )							->		int:


      '''simple docstring'''



      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   ):
            # 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   ):
            # 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 _UpperCamelCase	(					)							->		Optional[int]:


      '''simple docstring'''



      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()









'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
  from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

  from .test_feature_extraction_speechta import floats_list


a__  : int				=	get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_torch
class  lowercase_					(   unittest.TestCase       ):


    def        __a							(					self   ):
          UpperCamelCase__  =    tempfile.mkdtemp()

          UpperCamelCase__  =    SpeechTaTokenizer(a   )
          tokenizer.save_pretrained(self.tmpdirname   )

          UpperCamelCase__  =    {
              "feature_size": 1,
              "padding_value": 0.0,
              "sampling_rate": 1_60_00,
              "do_normalize": False,
              "num_mel_bins": 80,
              "hop_length": 16,
              "win_length": 64,
              "win_function": "hann_window",
              "fmin": 80,
              "fmax": 76_00,
              "mel_floor": 1e-10,
              "reduction_factor": 2,
              "return_attention_mask": True,
          }

          UpperCamelCase__  =    os.path.join(self.tmpdirname  , a   )
          with open(self.feature_extraction_file  , "w"  , encoding="utf-8"   ) as fp:
                fp.write(json.dumps(a   ) + "\n"   )


    def        __a							(					self  , **a   ):
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname  , **a   )


    def        __a							(					self  , **a   ):
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname  , **a   )


    def        __a							(					self   ):
          shutil.rmtree(self.tmpdirname   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_tokenizer()
          UpperCamelCase__  =    self.get_feature_extractor()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          processor.save_pretrained(self.tmpdirname   )
          UpperCamelCase__  =    SpeechTaProcessor.from_pretrained(self.tmpdirname   )

          self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer.get_vocab()   )
          self.assertIsInstance(processor.tokenizer  , a   )

          self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor.to_json_string()   )
          self.assertIsInstance(processor.feature_extractor  , a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=self.get_tokenizer()  , feature_extractor=self.get_feature_extractor()   )
          processor.save_pretrained(self.tmpdirname   )

          UpperCamelCase__  =    self.get_tokenizer(bos_token="(BOS)"  , eos_token="(EOS)"   )
          UpperCamelCase__  =    self.get_feature_extractor(do_normalize=a  , padding_value=1.0   )

          UpperCamelCase__  =    SpeechTaProcessor.from_pretrained(
              self.tmpdirname  , bos_token="(BOS)"  , eos_token="(EOS)"  , do_normalize=a  , padding_value=1.0   )

          self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer_add_kwargs.get_vocab()   )
          self.assertIsInstance(processor.tokenizer  , a   )

          self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor_add_kwargs.to_json_string()   )
          self.assertIsInstance(processor.feature_extractor  , a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    floats_list((3, 10_00)   )

          UpperCamelCase__  =    feature_extractor(audio=a  , return_tensors="np"   )
          UpperCamelCase__  =    processor(audio=a  , return_tensors="np"   )

          for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()  , input_processor[key].sum()  , delta=1e-2   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    floats_list((3, 10_00)   )

          UpperCamelCase__  =    feature_extractor(audio_target=a  , return_tensors="np"   )
          UpperCamelCase__  =    processor(audio_target=a  , return_tensors="np"   )

          for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()  , input_processor[key].sum()  , delta=1e-2   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    "This is a test string"

          UpperCamelCase__  =    processor(text=a   )
          UpperCamelCase__  =    tokenizer(a   )

          for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]  , encoded_processor[key]   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    "This is a test string"

          UpperCamelCase__  =    processor(text_target=a   )
          UpperCamelCase__  =    tokenizer(a   )

          for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]  , encoded_processor[key]   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase__  =    processor.batch_decode(a   )
          UpperCamelCase__  =    tokenizer.batch_decode(a   )

          self.assertListEqual(a  , a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          self.assertListEqual(
              processor.model_input_names  , feature_extractor.model_input_names  , msg="`processor` and `feature_extractor` model input names do not match"  , )





