

'''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_     =      1_6
lowerCAmelCase_     =      3_2

def      __magic_name__							(			A				)      ->		Union[str, Any]:
	return int(x / 2**2_0				)







class lowerCamelCase   :

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

					snake_case							=					self.cpu_mem_used()
					snake_case							=					True
					snake_case							=					threading.Thread(target=self.peak_monitor_func )
					snake_case							=					True
					peak_monitor_thread.start()
					return self

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

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					-1

					while True:
						snake_case							=					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,						*lowercase_ )    ->							str:
					snake_case							=					False

					gc.collect()
					torch.cuda.empty_cache()
					snake_case							=					torch.cuda.memory_allocated()
					snake_case							=					torch.cuda.max_memory_allocated()
					snake_case							=					bamb(self.end - self.begin )
					snake_case							=					bamb(self.peak - self.begin )

					snake_case							=					self.cpu_mem_used()
					snake_case							=					bamb(self.cpu_end - self.cpu_begin )
					snake_case							=					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      __magic_name__							(			A       , A				)      ->		Any:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'       , A				) == "1":
		snake_case							=					2

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

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

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		snake_case							=					vars(A				)
		accelerator.init_trackers('fsdp_glue_no_trainer'       , A				)

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

	def tokenize_function(A				):
		# max_length=None => use the model max length (it's actually the default)
		snake_case							=					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():
		snake_case							=					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
	snake_case							=					tokenized_datasets.rename_column('label'       , 'labels'				)

	# If the batch size is too big we use gradient accumulation
	snake_case							=					1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		snake_case							=					batch_size // MAX_GPU_BATCH_SIZE
		snake_case							=					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.
		snake_case							=					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			snake_case							=					1_6
		elif accelerator.mixed_precision != "no":
			snake_case							=					8
		else:
			snake_case							=					None

		return tokenizer.pad(
		    A       , padding='longest'       , max_length=A       , pad_to_multiple_of=A       , return_tensors='pt'       , )

	# Instantiate dataloaders.
	snake_case							=					DataLoader(
	    tokenized_datasets['train']       , shuffle=A       , collate_fn=A       , batch_size=A				)
	snake_case							=					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)
	snake_case							=					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
	snake_case							=					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
	snake_case							=					torch.optim.AdamW(params=model.parameters()       , lr=A       , weight_decay=2E-4				)

	# Instantiate scheduler
	snake_case							=					get_linear_schedule_with_warmup(
	    optimizer=A       , num_warmup_steps=1_0       , 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.
	snake_case			,			snake_case			,			snake_case			,			snake_case							=					accelerator.prepare(
	    A       , A       , A       , A				)

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

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

			snake_case							=					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":
				snake_case							=					F'''epoch_{epoch}'''
				if args.output_dir is not None:
					snake_case							=					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      __magic_name__							(			)      ->		Optional[int]:
	snake_case							=					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       , )
	snake_case							=					parser.parse_args()
	snake_case							=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(A       , A				)


if __name__ == "__main__":
						main()









import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


_A		:					Optional[int]          =							logging.get_logger(__name__)



class 							a       (      nn.Module ):





     def __init__(     self       :					Dict				):
        super().__init__()
        if version.parse(torch.__version__				) < version.parse("""1.12.0"""				):
           raise ImportError(
               F'You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '
               """PytorchGELUTanh. Please upgrade torch."""				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return nn.functional.gelu(SCREAMING_SNAKE_CASE_     ,						approximate="""tanh"""				)



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.04_4715 * torch.pow(SCREAMING_SNAKE_CASE_     ,						3.0				))				))



class 							a       (      nn.Module ):





     def __init__(     self       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					bool = False				):
        super().__init__()
        if use_gelu_python:
           __lowerCamelCase:					Union[str, Any]	 =						self._gelu_python
        else:
           __lowerCamelCase:					Tuple	 =						nn.functional.gelu





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0				)				))







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return self.act(SCREAMING_SNAKE_CASE_				)



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)				))



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input * torch.sigmoid(1.702 * input				)



class 							a       (      nn.Module ):





     def __init__(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					float     ,						SCREAMING_SNAKE_CASE_       :					float				):
        if min > max:
           raise ValueError(F'min should be < max (got min: {min}, max: {max})'				)

        super().__init__()
        __lowerCamelCase:					Optional[int]	 =						min
        __lowerCamelCase:					str	 =						max







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return torch.clip(gelu(SCREAMING_SNAKE_CASE_				)     ,						self.min     ,						self.max				)



class 							a       (      nn.Module ):





     def __init__(     self       :					int				):
        super().__init__()
        __lowerCamelCase:					List[str]	 =						math.sqrt(2 / math.pi				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(SCREAMING_SNAKE_CASE_     ,						3				))				))



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return nn.functional.silu(SCREAMING_SNAKE_CASE_				)



class 							a       (      nn.Module ):





     def __init__(     self       :					Any				):
        super().__init__()
        if version.parse(torch.__version__				) < version.parse("""1.9.0"""				):
           __lowerCamelCase:					str	 =						self._mish_python
        else:
           __lowerCamelCase:					Any	 =						nn.functional.mish





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input * torch.tanh(nn.functional.softplus(SCREAMING_SNAKE_CASE_				)				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return self.act(SCREAMING_SNAKE_CASE_				)



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tensor				):
        return input



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=0.70_7107     ,						SCREAMING_SNAKE_CASE_       :					Tuple=0.28_2095				):
        __lowerCamelCase:					Optional[Any]	 =						(input - mu).div(sigma * math.sqrt(2.0				)				)
        return 0.5 * (1.0 + torch.erf(SCREAMING_SNAKE_CASE_				))



class 							a       (      nn.Module ):





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					str				):
        __lowerCamelCase:					Any	 =						nn.functional.relu(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					List[str]	 =						torch.square(SCREAMING_SNAKE_CASE_				)
        return squared



class 							a       (      _UpperCAmelCase ):





     def __getitem__(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					Tuple				):
        __lowerCamelCase:					Optional[Any]	 =						super().__getitem__(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						content if isinstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				) else (content, {})
        return cls(**SCREAMING_SNAKE_CASE_				)



_A		:					Optional[Any]          =							{
    '''gelu''': GELUActivation,
    '''gelu_10''': (ClippedGELUActivation, {'''min''': -10, '''max''': 10}),
    '''gelu_fast''': FastGELUActivation,
    '''gelu_new''': NewGELUActivation,
    '''gelu_python''': (GELUActivation, {'''use_gelu_python''': True}),
    '''gelu_pytorch_tanh''': PytorchGELUTanh,
    '''gelu_accurate''': AccurateGELUActivation,
    '''laplace''': LaplaceActivation,
    '''linear''': LinearActivation,
    '''mish''': MishActivation,
    '''quick_gelu''': QuickGELUActivation,
    '''relu''': nn.ReLU,
    '''relu2''': ReLUSquaredActivation,
    '''relu6''': nn.ReLUa,
    '''sigmoid''': nn.Sigmoid,
    '''silu''': SiLUActivation,
    '''swish''': SiLUActivation,
    '''tanh''': nn.Tanh,
}
_A		:					Optional[int]          =							ClassInstantier(ACTaCLS)



def      __lowerCAmelCase    (				snake_case						:     Any			)							->				int:
   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()			)}'			)


# For backwards compatibility with: from activations import gelu_python
_A		:					Optional[int]          =							get_activation('''gelu_python''')
_A		:					Dict          =							get_activation('''gelu_new''')
_A		:					List[str]          =							get_activation('''gelu''')
_A		:					Tuple          =							get_activation('''gelu_fast''')
_A		:					int          =							get_activation('''quick_gelu''')
_A		:					Tuple          =							get_activation('''silu''')
_A		:					Optional[Any]          =							get_activation('''mish''')







_A		:					Dict          =							get_activation('''linear''')


