






"""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
#
########################################################################


_UpperCamelCase     			=  16
_UpperCamelCase     			=  32

def _a (		_snake_case							):







      """simple docstring"""






      return int(x / 2**20							)







class 					lowerCamelCase__					:






    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 				_UpperCamelCase							( self					):
          return self.process.memory_info().rss






    def 				_UpperCamelCase							( 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

  _UpperCamelCase     			=  mocked_dataloaders  # noqa: F811

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,       _snake_case							) == "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=_snake_case      ,       ranka_only=_snake_case							)      ,       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_snake_case      ,       ranka_only=_snake_case							)      ,       )

      # 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=_snake_case      ,       )
      else:
            UpperCAmelCase							=       Accelerator(fsdp_plugin=_snake_case							)
      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(_snake_case							)
            accelerator.init_trackers("""fsdp_glue_no_trainer"""      ,       _snake_case							)

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

      def tokenize_function(_snake_case							):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase							=       tokenizer(examples["""sentence1"""]      ,       examples["""sentence2"""]      ,       truncation=_snake_case      ,       max_length=_snake_case							)
            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(
                _snake_case      ,       batched=_snake_case      ,       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(_snake_case							):
            # 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(
                _snake_case      ,       padding="""longest"""      ,       max_length=_snake_case      ,       pad_to_multiple_of=_snake_case      ,       return_tensors="""pt"""      ,       )

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

      set_seed(_snake_case							)

      # 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=_snake_case      ,       low_cpu_mem_usage=_snake_case							)

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

      # 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=_snake_case      ,       weight_decay=2E-4							)

      # Instantiate scheduler
      UpperCAmelCase							=       get_linear_schedule_with_warmup(
          optimizer=_snake_case      ,       num_warmup_steps=10      ,       num_training_steps=(len(_snake_case							) * 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(
          _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							)

      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(_snake_case							)[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(_snake_case							)
                  # If resuming by step, we also need to know exactly how far into the DataLoader we went
                  UpperCAmelCase							=       (num_epochs * len(_snake_case							)) - resume_step

    # Now we train the model
      for epoch in range(_snake_case							):
            # 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(_snake_case							):
                        # 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(**_snake_case							)
                        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(_snake_case							)
                        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(_snake_case      ,       _snake_case							):
                              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      ,       _snake_case							)
                                    accelerator.save_state(_snake_case							)
        # 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=_snake_case      ,       )

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

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

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

      if args.with_tracking:
            accelerator.end_training()

def _a (		):







      """simple docstring"""






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


if __name__ == "__main__":
  main()











"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

_UpperCamelCase     			=  {
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

_UpperCamelCase     			=  datasets.load_dataset("""xnli""", split="""test+validation""")

_UpperCamelCase     			=  0
_UpperCamelCase     			=  0
_UpperCamelCase     			=  0
_UpperCamelCase     			=  0

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      if spm_diff == list(reversed(_snake_case							)							):
            # AAA -> AA+A vs A+AA case.
            return True
      elif len(_snake_case							) == len(_snake_case							) and fast.decode(_snake_case							) == fast.decode(_snake_case							):
            # Second order OK
            # Barrich -> Barr + ich vs Bar + rich
            return True
      UpperCAmelCase							=       slow.encode(slow.decode(_snake_case							)							)
      UpperCAmelCase							=       fast.encode(fast.decode(_snake_case							)							)
      if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
            # Type 3 error.
            # Snehagatha ->
            #       Sne, h, aga, th, a
            #       Sne, ha, gat, ha
            # Encoding the wrong with sp does not even recover what spm gave us
            # It fits tokenizer however...
            return True
      return False

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       fast.encode_plus(_snake_case							)[0]
      UpperCAmelCase							=       enc.offsets
      UpperCAmelCase			,					UpperCAmelCase							=       offsets[idx], offsets[idx - 1]
      if curr is not None and line[curr[0] : curr[1]] == "\u200f":
            return True
      if prev is not None and line[prev[0] : prev[1]] == "\u200f":
            return True

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      for i, (spm_id, tok_id) in enumerate(zip(_snake_case      ,       _snake_case							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(_snake_case							)      ,       reversed(_snake_case							)							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       len(_snake_case							) - i

      UpperCAmelCase							=       spm_ids[first:last]
      UpperCAmelCase							=       tok_ids[first:last]

      if check_diff(_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):
            return True

      if check_LTR_mark(_snake_case      ,       _snake_case      ,       _snake_case							):
            return True

      if last - first > 5:
            # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
            UpperCAmelCase							=       Counter(spm_ids[first:last]							)
            UpperCAmelCase							=       Counter(tok_ids[first:last]							)

            UpperCAmelCase							=       {spm_ for (spm_, si) in spms.items() if toks.get(spm_      ,       0							) == si}
            UpperCAmelCase							=       3
            for i in range(last - first - min_width							):
                  if all(spm_ids[first + i + j] in removable_tokens for j in range(_snake_case							)							):
                        UpperCAmelCase							=       [
                            k
                            for k in range(last - first - min_width							)
                            if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                        ]
                        for j in possible_matches:
                              if check_diff(spm_ids[first : first + i]      ,       tok_ids[first : first + j]      ,       _snake_case      ,       _snake_case							) and check_details(
                                  _snake_case      ,       spm_ids[first + i : last]      ,       tok_ids[first + j : last]      ,       _snake_case      ,       _snake_case      ,       ):
                                    return True

      print(F'''Spm: {[fast.decode([spm_ids[i]]							) for i in range(_snake_case      ,       _snake_case							)]}'''							)
      try:
            print(F'''Tok: {[fast.decode([tok_ids[i]]							) for i in range(_snake_case      ,       _snake_case							)]}'''							)
      except Exception:
            pass

      UpperCAmelCase							=       fast.decode(spm_ids[:first]							)
      UpperCAmelCase							=       fast.decode(spm_ids[last:]							)
      UpperCAmelCase							=       fast.decode(spm_ids[first:last]							)
      print()
      print(_snake_case							)
      return False

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      global perfect
      global imperfect
      global wrong
      global total

      UpperCAmelCase							=       slow.encode(_snake_case							)
      UpperCAmelCase							=       fast.encode(_snake_case							)

      UpperCAmelCase							=       False
      total += 1

      if slow_ids != fast_ids:
            if check_details(_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):
                  UpperCAmelCase							=       True
                  imperfect += 1
            else:
                  wrong += 1
      else:
            perfect += 1

      if total % 1_0000 == 0:
            print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''							)

      if skip_assert:
            return

      assert (
          slow_ids == fast_ids
      ), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(_snake_case							)}\n{fast.tokenize(_snake_case							)}'''

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      global batch_total
      for i in range(len(_snake_case							)							):
            # premise, all languages
            for text in dataset[i]["premise"].values():
                  test_string(_snake_case      ,       _snake_case      ,       _snake_case							)

            # hypothesis, all languages
            for text in dataset[i]["hypothesis"]["translation"]:
                  test_string(_snake_case      ,       _snake_case      ,       _snake_case							)


if __name__ == "__main__":
  for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
    _UpperCamelCase     			=  list(slow_class.max_model_input_sizes.keys())
    for checkpoint in checkpoint_names:
      _UpperCamelCase     			=  0
      _UpperCamelCase     			=  0
      _UpperCamelCase     			=  0
      _UpperCamelCase     			=  0

      print(F"""========================== Checking {name}: {checkpoint} ==========================""")
      _UpperCamelCase     			=  slow_class.from_pretrained(checkpoint, force_download=True)
      _UpperCamelCase     			=  fast_class.from_pretrained(checkpoint, force_download=True)
      test_tokenizer(slow, fast)
      print(F"""Accuracy {perfect * 100 / total:.2f}""")



