


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
#
########################################################################


__magic_name__       					=      1_6
__magic_name__       					=      3_2







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return int(x / 2**20)





class    lowerCAmelCase__				:






						"""simple docstring"""




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

							lowerCamelCase_			:							Dict      =       self.cpu_mem_used()
							lowerCamelCase_			:							Optional[Any]      =       True
							lowerCamelCase_			:							Tuple      =       threading.Thread(target=self.peak_monitor_func     )
							lowerCamelCase_			:							List[Any]      =       True
							peak_monitor_thread.start()
							return self




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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Tuple      =       -1

							while True:
								lowerCamelCase_			:							Tuple      =       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_     ):
							lowerCamelCase_			:							List[Any]      =       False

							gc.collect()
							torch.cuda.empty_cache()
							lowerCamelCase_			:							Dict      =       torch.cuda.memory_allocated()
							lowerCamelCase_			:							str      =       torch.cuda.max_memory_allocated()
							lowerCamelCase_			:							str      =       bamb(self.end - self.begin     )
							lowerCamelCase_			:							Dict      =       bamb(self.peak - self.begin     )

							lowerCamelCase_			:							Optional[int]      =       self.cpu_mem_used()
							lowerCamelCase_			:							Any      =       bamb(self.cpu_end - self.cpu_begin     )
							lowerCamelCase_			:							Union[str, Any]      =       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

							__magic_name__       					=      mocked_dataloaders  # noqa: F811







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,					lowerCAmelCase_) == "1":
		lowerCamelCase_			:							List[str]      =       2

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

	# Initialize accelerator
	if args.with_tracking:
		lowerCamelCase_			:							List[Any]      =       Accelerator(
		    cpu=args.cpu      ,					mixed_precision=args.mixed_precision      ,					log_with="wandb"      ,					project_dir=args.logging_dir      ,					fsdp_plugin=lowerCAmelCase_      ,					)
	else:
		lowerCamelCase_			:							Union[str, Any]      =       Accelerator(fsdp_plugin=lowerCAmelCase_)
	accelerator.print(accelerator.distributed_type)

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

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

	lowerCamelCase_			:							List[str]      =       AutoTokenizer.from_pretrained(args.model_name_or_path)
	lowerCamelCase_			:							Optional[Any]      =       load_dataset("glue"      ,					"mrpc")
	lowerCamelCase_			:							Dict      =       evaluate.load("glue"      ,					"mrpc")

	def tokenize_function(lowerCAmelCase_):
		# max_length=None => use the model max length (it's actually the default)
		lowerCamelCase_			:							Any      =       tokenizer(examples["sentence1"]      ,					examples["sentence2"]      ,					truncation=lowerCAmelCase_      ,					max_length=lowerCAmelCase_)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		lowerCamelCase_			:							str      =       datasets.map(
		    lowerCAmelCase_      ,					batched=lowerCAmelCase_      ,					remove_columns=["idx", "sentence1", "sentence2"]      ,					)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	lowerCamelCase_			:							Optional[Any]      =       tokenized_datasets.rename_column("label"      ,					"labels")

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

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

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

	# Instantiate dataloaders.
	lowerCamelCase_			:							List[str]      =       DataLoader(
	    tokenized_datasets["train"]      ,					shuffle=lowerCAmelCase_      ,					collate_fn=lowerCAmelCase_      ,					batch_size=lowerCAmelCase_)
	lowerCamelCase_			:							Optional[Any]      =       DataLoader(
	    tokenized_datasets["validation"]      ,					shuffle=lowerCAmelCase_      ,					collate_fn=lowerCAmelCase_      ,					batch_size=lowerCAmelCase_)

	set_seed(lowerCAmelCase_)

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

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

	# Instantiate optimizer
	# New Code #
	# For FSDP feature, at present it doesn't support multiple parameter groups,
	# so we need to create a single parameter group for the whole model
	lowerCamelCase_			:							str      =       torch.optim.AdamW(params=model.parameters()      ,					lr=lowerCAmelCase_      ,					weight_decay=2E-4)

	# Instantiate scheduler
	lowerCamelCase_			:							Any      =       get_linear_schedule_with_warmup(
	    optimizer=lowerCAmelCase_      ,					num_warmup_steps=10      ,					num_training_steps=(len(lowerCAmelCase_) * num_epochs) // gradient_accumulation_steps      ,					)

	# New Code #
	# For FSDP feature, prepare everything except the model as we have already prepared the model
	# before creating the optimizer
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       accelerator.prepare(
	    lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_)

	lowerCamelCase_			:							List[str]      =       0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
			accelerator.load_state(args.resume_from_checkpoint)
			lowerCamelCase_			:							List[str]      =       os.path.basename(args.resume_from_checkpoint)
		else:
			# Get the most recent checkpoint
			lowerCamelCase_			:							int      =       [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
			dirs.sort(key=os.path.getctime)
			lowerCamelCase_			:							Any      =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		lowerCamelCase_			:							List[str]      =       os.path.splitext(lowerCAmelCase_)[0]

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

    # Now we train the model
	for epoch in range(lowerCAmelCase_):
		# New Code #
		# context manager to track the peak memory usage during the training epoch
		with TorchTracemalloc() as tracemalloc:
			model.train()
			if args.with_tracking:
				lowerCamelCase_			:							Any      =       0
			for step, batch in enumerate(lowerCAmelCase_):
				# We need to skip steps until we reach the resumed step
				if args.resume_from_checkpoint and epoch == 0:
					if resume_step is not None and step < resume_step:
						pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device)
				lowerCamelCase_			:							str      =       model(**lowerCAmelCase_)
				lowerCamelCase_			:							List[Any]      =       outputs.loss
				lowerCamelCase_			:							Optional[Any]      =       loss / gradient_accumulation_steps
				# We keep track of the loss at each epoch
				if args.with_tracking:
					total_loss += loss.detach().float()
				accelerator.backward(lowerCAmelCase_)
				if step % gradient_accumulation_steps == 0:
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# accelerator.print(lr_scheduler.get_lr())

				overall_step += 1

				if isinstance(lowerCAmelCase_      ,					lowerCAmelCase_):
					lowerCamelCase_			:							str      =       F"""step_{overall_step}"""
					if overall_step % checkpointing_steps == 0:
						if args.output_dir is not None:
							lowerCamelCase_			:							Optional[int]      =       os.path.join(args.output_dir      ,					lowerCAmelCase_)
						accelerator.save_state(lowerCAmelCase_)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
		accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin)))
		accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
		accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
		accelerator.print(
		    "Total Peak Memory consumed during the train (max): {}".format(
		        tracemalloc.peaked + bamb(tracemalloc.begin)))
		# Logging the peak memory usage of the GPU to the tracker
		if args.with_tracking:
			accelerator.log(
			    {
			        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin),
			    }      ,					step=lowerCAmelCase_      ,					)

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

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

			if checkpointing_steps == "epoch":
				lowerCamelCase_			:							int      =       F"""epoch_{epoch}"""
				if args.output_dir is not None:
					lowerCamelCase_			:							Dict      =       os.path.join(args.output_dir      ,					lowerCAmelCase_)
				accelerator.save_state(lowerCAmelCase_)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
		accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin)))
		accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
		accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
		accelerator.print(
		    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin)))
		# Logging the peak memory usage of the GPU to the tracker
		if args.with_tracking:
			accelerator.log(
			    {
			        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin),
			    }      ,					step=lowerCAmelCase_      ,					)

	if args.with_tracking:
		accelerator.end_training()







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							Tuple      =       argparse.ArgumentParser(description="Simple example of training script.")
	parser.add_argument(
	    "--mixed_precision"      ,					type=lowerCAmelCase_      ,					default=lowerCAmelCase_      ,					choices=["no", "fp16", "bf16", "fp8"]      ,					help="Whether to use mixed precision. Choose"
	    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
	    "and an Nvidia Ampere GPU."      ,					)
	parser.add_argument("--cpu"      ,					action="store_true"      ,					help="If passed, will train on the CPU.")
	parser.add_argument(
	    "--checkpointing_steps"      ,					type=lowerCAmelCase_      ,					default=lowerCAmelCase_      ,					help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."      ,					)
	parser.add_argument(
	    "--resume_from_checkpoint"      ,					type=lowerCAmelCase_      ,					default=lowerCAmelCase_      ,					help="If the training should continue from a checkpoint folder."      ,					)
	parser.add_argument(
	    "--with_tracking"      ,					action="store_true"      ,					help="Whether to load in all available experiment trackers from the environment and use them for logging."      ,					)
	parser.add_argument(
	    "--output_dir"      ,					type=lowerCAmelCase_      ,					default="."      ,					help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      ,					)
	parser.add_argument(
	    "--logging_dir"      ,					type=lowerCAmelCase_      ,					default="logs"      ,					help="Location on where to store experiment tracking logs`"      ,					)
	parser.add_argument(
	    "--model_name_or_path"      ,					type=lowerCAmelCase_      ,					help="Path to pretrained model or model identifier from huggingface.co/models."      ,					required=lowerCAmelCase_      ,					)
	lowerCamelCase_			:							Optional[int]      =       parser.parse_args()
	lowerCamelCase_			:							Dict      =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(lowerCAmelCase_      ,					lowerCAmelCase_)


if __name__ == "__main__":
							main()





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCAmelCase_))







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-comp-10.jpg")      ,					1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")


if __name__ == "__main__":
							main()

