



import bsa
import requests







def 							A     (						__UpperCAmelCase      )			->			dict[str, str]:



						'''simple docstring'''






						return {
						    "name": soup.ha.a.text,
						    "genre": soup.find('''span'''				,   class_='''genre'''      ).text.strip(),
						    "rating": soup.strong.text,
						    "page_link": f"https://www.imdb.com{soup.a.get('href'      )}",
						}







def 							A     (						__UpperCAmelCase = 5      )			->			tuple:



						'''simple docstring'''






						UpperCAmelCase_					=	int(float(__UpperCAmelCase      )      )
						if num_movies < 1:
												return ()
						UpperCAmelCase_					=	(
						    '''https://www.imdb.com/search/title?title_type='''
						    f"feature&sort=num_votes,desc&count={num_movies}"
						)
						UpperCAmelCase_					=	bsa.BeautifulSoup(requests.get(__UpperCAmelCase      ).content				,   '''html.parser'''      )
						return tuple(
						    get_movie_data_from_soup(__UpperCAmelCase      )
						    for movie in source.find_all('''div'''				,   class_='''lister-item mode-advanced'''      )      )


if __name__ == "__main__":
							import json

							UpperCamelCase_      			=  int(input("How many movies would you like to see? "))
							print(
							    ", ".join(
							        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
							    )
							)










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     (						__UpperCAmelCase      )			->			Optional[int]:



						'''simple docstring'''






						return int(x / 2**20      )







class 				a_    :

						def __enter__(       self      :Any)						->   Optional[Any]:
												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      :Tuple)						->   int:
												return self.process.memory_info().rss

						def       __a			(       self      :str)						->   Dict:
												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      :Optional[Any]  ,		*_lowercase      :Optional[Any])						->   List[str]:
												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     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			int:



						'''simple docstring'''






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

						# 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=__UpperCAmelCase				,   )
						else:
												UpperCAmelCase_					=	Accelerator(fsdp_plugin=__UpperCAmelCase      )
						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(__UpperCAmelCase      )
												accelerator.init_trackers('''fsdp_glue_no_trainer'''				,   __UpperCAmelCase      )

						UpperCAmelCase_					=	AutoTokenizer.from_pretrained(args.model_name_or_path      )
						UpperCAmelCase_					=	load_dataset('''glue'''				,   '''mrpc'''      )
						UpperCAmelCase_					=	evaluate.load('''glue'''				,   '''mrpc'''      )

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

						# Instantiate dataloaders.
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''train''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )
						UpperCAmelCase_					=	DataLoader(
						    tokenized_datasets['''validation''']				,   shuffle=__UpperCAmelCase				,   collate_fn=__UpperCAmelCase				,   batch_size=__UpperCAmelCase      )

						set_seed(__UpperCAmelCase      )

						# 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=__UpperCAmelCase				,   low_cpu_mem_usage=__UpperCAmelCase      )

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

						# 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=__UpperCAmelCase				,   weight_decay=2e-4      )

						# Instantiate scheduler
						UpperCAmelCase_					=	get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase				,   num_warmup_steps=10				,   num_training_steps=(len(__UpperCAmelCase      ) * 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(
						    __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )

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

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

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

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

																		if checkpointing_steps == "epoch":
																								UpperCAmelCase_					=	f"epoch_{epoch}"
																								if args.output_dir is not None:
																														UpperCAmelCase_					=	os.path.join(args.output_dir				,   __UpperCAmelCase      )
																								accelerator.save_state(__UpperCAmelCase      )
        # 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=__UpperCAmelCase				,   )

						if args.with_tracking:
												accelerator.end_training()







def 							A     (						)			->			Optional[Any]:



						'''simple docstring'''






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


if __name__ == "__main__":
							main()





