"""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  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Union[str, Any]  )				-> Any:
						return int(x / 2**20  )
class   UpperCAmelCase__				:


	"""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
							a_							:			Any     		=					torch.cuda.memory_allocated()
							a_							:			Dict     		=					psutil.Process()

							a_							:			List[str]     		=					self.cpu_mem_used()
							a_							:			List[str]     		=					True
							a_							:			Optional[int]     		=					threading.Thread(target=self.peak_monitor_func )
							a_							:			Any     		=					True
							peak_monitor_thread.start()
							return self







	def       A    (			self )    ->	List[str]:
							return self.process.memory_info().rss







	def       A    (			self )    ->	str:
							a_							:			int     		=					-1

							while True:
													a_							:			Dict     		=					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       ,     *_SCREAMING_SNAKE_CASE )    ->	Tuple:
							a_							:			Optional[Any]     		=					False

							gc.collect()
							torch.cuda.empty_cache()
							a_							:			Tuple     		=					torch.cuda.memory_allocated()
							a_							:			List[Any]     		=					torch.cuda.max_memory_allocated()
							a_							:			Union[str, Any]     		=					bamb(self.end - self.begin )
							a_							:			Tuple     		=					bamb(self.peak - self.begin )

							a_							:			str     		=					self.cpu_mem_used()
							a_							:			int     		=					bamb(self.cpu_end - self.cpu_begin )
							a_							:			List[str]     		=					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  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int		,			_SCREAMING_SNAKE_CASE	:Tuple  )				-> int:
						# For testing only
						if os.environ.get("TESTING_MOCKED_DATALOADERS"		,			_SCREAMING_SNAKE_CASE  ) == "1":
												a_							:			Union[str, Any]     		=					2

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

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

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

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

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

						def tokenize_function(_SCREAMING_SNAKE_CASE	:Dict  ):
												# max_length=None => use the model max length (it's actually the default)
												a_							:			Tuple     		=					tokenizer(examples["sentence1"]		,			examples["sentence2"]		,			truncation=_SCREAMING_SNAKE_CASE		,			max_length=_SCREAMING_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():
												a_							:			Optional[Any]     		=					datasets.map(
												    _SCREAMING_SNAKE_CASE		,			batched=_SCREAMING_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
						a_							:			Tuple     		=					tokenized_datasets.rename_column("label"		,			"labels"  )

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

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

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

						# Instantiate dataloaders.
						a_							:			List[str]     		=					DataLoader(
						    tokenized_datasets["train"]		,			shuffle=_SCREAMING_SNAKE_CASE		,			collate_fn=_SCREAMING_SNAKE_CASE		,			batch_size=_SCREAMING_SNAKE_CASE  )
						a_							:			Union[str, Any]     		=					DataLoader(
						    tokenized_datasets["validation"]		,			shuffle=_SCREAMING_SNAKE_CASE		,			collate_fn=_SCREAMING_SNAKE_CASE		,			batch_size=_SCREAMING_SNAKE_CASE  )

						set_seed(_SCREAMING_SNAKE_CASE  )

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

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						a_							:			Dict     		=					accelerator.prepare(_SCREAMING_SNAKE_CASE  )
						accelerator.print(_SCREAMING_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
						a_							:			Dict     		=					torch.optim.AdamW(params=model.parameters()		,			lr=_SCREAMING_SNAKE_CASE		,			weight_decay=2E-4  )

						# Instantiate scheduler
						a_							:			str     		=					get_linear_schedule_with_warmup(
						    optimizer=_SCREAMING_SNAKE_CASE		,			num_warmup_steps=10		,			num_training_steps=(len(_SCREAMING_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.
						a_							:			Tuple     		=					accelerator.prepare(
						    _SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

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

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

    # Now we train the model
						for epoch in range(_SCREAMING_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:
																								a_							:			Tuple     		=					0
																		for step, batch in enumerate(_SCREAMING_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  )
																								a_							:			Optional[Any]     		=					model(**_SCREAMING_SNAKE_CASE  )
																								a_							:			Optional[Any]     		=					outputs.loss
																								a_							:			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(_SCREAMING_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(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  ):
																														a_							:			Any     		=					F'''step_{overall_step}'''
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										a_							:			Union[str, Any]     		=					os.path.join(args.output_dir		,			_SCREAMING_SNAKE_CASE  )
																																				accelerator.save_state(_SCREAMING_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=_SCREAMING_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(_SCREAMING_SNAKE_CASE  ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device  )
																								with torch.no_grad():
																														a_							:			Union[str, Any]     		=					model(**_SCREAMING_SNAKE_CASE  )
																								a_							:			List[Any]     		=					outputs.logits.argmax(dim=-1  )
																								a_							:			Tuple     		=					accelerator.gather_for_metrics((predictions, batch["labels"])  )
																								metric.add_batch(
																								    predictions=_SCREAMING_SNAKE_CASE		,			references=_SCREAMING_SNAKE_CASE		,			)

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

																		if checkpointing_steps == "epoch":
																								a_							:			List[str]     		=					F'''epoch_{epoch}'''
																								if args.output_dir is not None:
																														a_							:			str     		=					os.path.join(args.output_dir		,			_SCREAMING_SNAKE_CASE  )
																								accelerator.save_state(_SCREAMING_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=_SCREAMING_SNAKE_CASE		,			)

						if args.with_tracking:
												accelerator.end_training()

def  lowerCAmelCase_      ()				-> str:
						a_							:			Any     		=					argparse.ArgumentParser(description="Simple example of training script."  )
						parser.add_argument(
						    "--mixed_precision"		,			type=_SCREAMING_SNAKE_CASE		,			default=_SCREAMING_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=_SCREAMING_SNAKE_CASE		,			default=_SCREAMING_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=_SCREAMING_SNAKE_CASE		,			default=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE		,			default="logs"		,			help="Location on where to store experiment tracking logs`"		,			)
						parser.add_argument(
						    "--model_name_or_path"		,			type=_SCREAMING_SNAKE_CASE		,			help="Path to pretrained model or model identifier from huggingface.co/models."		,			required=_SCREAMING_SNAKE_CASE		,			)
						a_							:			Dict     		=					parser.parse_args()
						a_							:			str     		=					{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
					main()





"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




