




'''simple docstring'''






import requests

try:
				from django.contrib.auth import authenticate, login
				from django.shortcuts import redirect, render
except ImportError:
				_lowerCAmelCase				     = _lowerCAmelCase				     = _lowerCAmelCase				     = _lowerCAmelCase				     = print

def       __lowerCAmelCase (   snake_case__						):
			# Enter your recaptcha secret key here
			__UpperCamelCase				:      Tuple									=       "secretKey"
			__UpperCamelCase				:      Any									=       "https://www.google.com/recaptcha/api/siteverify"

			# when method is not POST, direct user to login page
			if request.method != "POST":
						return render(snake_case__ ,				"login.html"						)

			# from the frontend, get username, password, and client_key
			__UpperCamelCase				:      Union[str, Any]									=       request.POST.get("username"						)
			__UpperCamelCase				:      str									=       request.POST.get("password"						)
			__UpperCamelCase				:      List[str]									=       request.POST.get("g-recaptcha-response"						)

			# post recaptcha response to Google's recaptcha api
			__UpperCamelCase				:      int									=       requests.post(snake_case__ ,				data={"secret": secret_key, "response": client_key}						)
			# if the recaptcha api verified our keys
			if response.json().get("success" ,				snake_case__						):
						# authenticate the user
						__UpperCamelCase				:      List[str]									=       authenticate(snake_case__ ,				username=snake_case__ ,				password=snake_case__						)
						if user_in_database:
									login(snake_case__ ,				snake_case__						)
									return redirect("/your-webpage"						)
			return render(snake_case__ ,				"login.html"						)









'''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
#
########################################################################


_lowerCAmelCase				     = 16
_lowerCAmelCase				     = 32

def       __lowerCAmelCase (   snake_case__						):
			return int(x / 2**20						)



class  A		:




				'''simple docstring'''





				def __enter__(self      )     ->	List[Any]:
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							__UpperCamelCase				:      str									=       torch.cuda.memory_allocated()
							__UpperCamelCase				:      Dict									=       psutil.Process()

							__UpperCamelCase				:      List[str]									=       self.cpu_mem_used()
							__UpperCamelCase				:      str									=       True
							__UpperCamelCase				:      Tuple									=       threading.Thread(target=self.peak_monitor_func      )
							__UpperCamelCase				:      int									=       True
							peak_monitor_thread.start()
							return self
				def 	a_	(self      )     ->	Any:
							return self.process.memory_info().rss
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Optional[Any]									=       -1

							while True:
										__UpperCamelCase				:      List[str]									=       max(self.cpu_mem_used()			,  self.cpu_peak      )

										# can't sleep or will not catch the peak right (this comment is here on purpose)
										# time.sleep(0.001) # 1msec

										if not self.peak_monitoring:
													break





				def __exit__(self			,  *_UpperCAmelCase      )     ->	Dict:
							__UpperCamelCase				:      List[str]									=       False

							gc.collect()
							torch.cuda.empty_cache()
							__UpperCamelCase				:      Tuple									=       torch.cuda.memory_allocated()
							__UpperCamelCase				:      Any									=       torch.cuda.max_memory_allocated()
							__UpperCamelCase				:      List[Any]									=       bamb(self.end - self.begin      )
							__UpperCamelCase				:      Optional[int]									=       bamb(self.peak - self.begin      )

							__UpperCamelCase				:      Tuple									=       self.cpu_mem_used()
							__UpperCamelCase				:      str									=       bamb(self.cpu_end - self.cpu_begin      )
							__UpperCamelCase				:      Dict									=       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

				_lowerCAmelCase				     = mocked_dataloaders  # noqa: F811

def       __lowerCAmelCase (   snake_case__ ,				snake_case__						):
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS" ,				snake_case__						) == "1":
						__UpperCamelCase				:      Optional[int]									=       2

			# New Code #
			# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
			__UpperCamelCase				:      Dict									=       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				:      str									=       Accelerator(
						    cpu=args.cpu ,				mixed_precision=args.mixed_precision ,				log_with="wandb" ,				project_dir=args.logging_dir ,				fsdp_plugin=snake_case__ ,				)
			else:
						__UpperCamelCase				:      Dict									=       Accelerator(fsdp_plugin=snake_case__						)
			accelerator.print(accelerator.distributed_type						)

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

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

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

			def tokenize_function(snake_case__						):
						# max_length=None => use the model max length (it's actually the default)
						__UpperCamelCase				:      List[str]									=       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				:      Union[str, Any]									=       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				:      List[Any]									=       tokenized_datasets.rename_column("label" ,				"labels"						)

			# If the batch size is too big we use gradient accumulation
			__UpperCamelCase				:      Tuple									=       1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__UpperCamelCase				:      Dict									=       batch_size // MAX_GPU_BATCH_SIZE
						__UpperCamelCase				:      Dict									=       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				:      List[str]									=       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				:      Optional[Any]									=       16
						elif accelerator.mixed_precision != "no":
									__UpperCamelCase				:      str									=       8
						else:
									__UpperCamelCase				:      Any									=       None

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

			# Instantiate dataloaders.
			__UpperCamelCase				:      Dict									=       DataLoader(
			    tokenized_datasets["train"] ,				shuffle=snake_case__ ,				collate_fn=snake_case__ ,				batch_size=snake_case__						)
			__UpperCamelCase				:      Optional[Any]									=       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				:      Union[str, Any]									=       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				:      List[Any]									=       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				:      Optional[Any]									=       torch.optim.AdamW(params=model.parameters() ,				lr=snake_case__ ,				weight_decay=2E-4						)

			# Instantiate scheduler
			__UpperCamelCase				:      int									=       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				:      Union[str, Any]									=       accelerator.prepare(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						)

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

						if "epoch" in training_difference:
									num_epochs -= int(training_difference.replace("epoch_" ,				""						)						)
									__UpperCamelCase				:      List[str]									=       None
						else:
									__UpperCamelCase				:      Any									=       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				:      Union[str, Any]									=       (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				:      int									=       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				:      int									=       model(**snake_case__						)
												__UpperCamelCase				:      Tuple									=       outputs.loss
												__UpperCamelCase				:      List[str]									=       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				:      Union[str, Any]									=       F"step_{overall_step}"
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					__UpperCamelCase				:      List[str]									=       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				:      Any									=       model(**snake_case__						)
												__UpperCamelCase				:      Tuple									=       outputs.logits.argmax(dim=-1						)
												__UpperCamelCase		, __UpperCamelCase				:      Union[str, Any]									=       accelerator.gather_for_metrics((predictions, batch["labels"])						)
												metric.add_batch(
												    predictions=snake_case__ ,				references=snake_case__ ,				)

									__UpperCamelCase				:      List[Any]									=       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				:      str									=       F"epoch_{epoch}"
												if args.output_dir is not None:
															__UpperCamelCase				:      Tuple									=       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       __lowerCAmelCase (   ):
			__UpperCamelCase				:      List[Any]									=       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				:      Optional[Any]									=       parser.parse_args()
			__UpperCamelCase				:      Optional[int]									=       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(snake_case__ ,				snake_case__						)


if __name__ == "__main__":
				main()



