


'''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'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__  : Any				=	{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : int				=	[
      'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TvltModel',
      'TvltForPreTraining',
      'TvltForAudioVisualClassification',
      'TvltPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : List[Any]				=	['TvltImageProcessor']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

  a__  : Any				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





