
"""simple docstring"""






import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__SCREAMING_SNAKE_CASE							=     16
__SCREAMING_SNAKE_CASE							=     32




def       A_  (				__lowercase	):
 return int(x / 2**20	)





class 				a__					:



     def __enter__(							self :List[Any]   ):







      '''simple docstring'''



      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      UpperCamelCase_     :					Tuple   	=torch.cuda.memory_allocated()
      return self




     def __exit__(							self :Any      ,  *_lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      gc.collect()
      torch.cuda.empty_cache()
      UpperCamelCase_     :					Dict   	=torch.cuda.memory_allocated()
      UpperCamelCase_     :					Optional[Any]   	=torch.cuda.max_memory_allocated()
      UpperCamelCase_     :					Optional[Any]   	=bamb(self.end - self.begin   )
      UpperCamelCase_     :					Optional[Any]   	=bamb(self.peak - self.begin   )
      # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def       A_  (				__lowercase						,	__lowercase = 16						,	__lowercase = "bert-base-cased"						,	__lowercase = 3_20						,	__lowercase = 1_60						,	):
 UpperCamelCase_     :					Optional[Any]   	=AutoTokenizer.from_pretrained(__lowercase	)
 UpperCamelCase_     :					Optional[int]   	=load_dataset(
     'glue'						,	'mrpc'						,	split={'train': F'''train[:{n_train}]''', 'validation': F'''validation[:{n_val}]'''}	)

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 UpperCamelCase_     :					List[str]   	=datasets.map(
     __lowercase						,	batched=__lowercase						,	remove_columns=['idx', 'sentence1', 'sentence2']						,	load_from_cache_file=__lowercase	)

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

 def collate_fn(__lowercase	):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(__lowercase						,	padding='max_length'						,	max_length=1_28						,	return_tensors='pt'	)
  return tokenizer.pad(__lowercase						,	padding='longest'						,	return_tensors='pt'	)

 # Instantiate dataloaders.
 UpperCamelCase_     :					Optional[Any]   	=DataLoader(
     tokenized_datasets['train']						,	shuffle=__lowercase						,	collate_fn=__lowercase						,	batch_size=__lowercase	)
 UpperCamelCase_     :					Tuple   	=DataLoader(
     tokenized_datasets['validation']						,	shuffle=__lowercase						,	collate_fn=__lowercase						,	batch_size=__lowercase	)

 return train_dataloader, eval_dataloader




def       A_  (				__lowercase						,	__lowercase	):
 # Initialize accelerator
 UpperCamelCase_     :					Optional[Any]   	=Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCamelCase_     :					List[Any]   	=config['lr']
 UpperCamelCase_     :					List[Any]   	=int(config['num_epochs']	)
 UpperCamelCase_     :					Tuple   	=int(config['seed']	)
 UpperCamelCase_     :					Any   	=int(config['batch_size']	)
 UpperCamelCase_     :					Optional[Any]   	=args.model_name_or_path

 set_seed(__lowercase	)
 UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=get_dataloaders(__lowercase						,	__lowercase						,	__lowercase						,	args.n_train						,	args.n_val	)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase_     :					Any   	=AutoModelForSequenceClassification.from_pretrained(__lowercase						,	return_dict=__lowercase	)

 # Instantiate optimizer
 UpperCamelCase_     :					List[str]   	=(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 UpperCamelCase_     :					Any   	=optimizer_cls(params=model.parameters()						,	lr=__lowercase	)

 if accelerator.state.deepspeed_plugin is not None:
  UpperCamelCase_     :					List[Any]   	=accelerator.state.deepspeed_plugin.deepspeed_config[
      'gradient_accumulation_steps'
  ]
 else:
  UpperCamelCase_     :					Union[str, Any]   	=1
 UpperCamelCase_     :					Dict   	=(len(__lowercase	) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  UpperCamelCase_     :					List[Any]   	=get_linear_schedule_with_warmup(
      optimizer=__lowercase						,	num_warmup_steps=0						,	num_training_steps=__lowercase						,	)
 else:
  UpperCamelCase_     :					str   	=DummyScheduler(__lowercase						,	total_num_steps=__lowercase						,	warmup_num_steps=0	)

 # Prepare everything
 # 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_	,     UpperCamelCase_     :					Dict   	=accelerator.prepare(
     __lowercase						,	__lowercase						,	__lowercase						,	__lowercase						,	__lowercase	)

 # We need to keep track of how many total steps we have iterated over
 UpperCamelCase_     :					Tuple   	=0
 # We also need to keep track of the stating epoch so files are named properly
 UpperCamelCase_     :					List[Any]   	=0

 # Now we train the model
 UpperCamelCase_     :					str   	={}
 for epoch in range(__lowercase						,	__lowercase	):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(__lowercase	):
    UpperCamelCase_     :					Any   	=model(**__lowercase	)
    UpperCamelCase_     :					str   	=outputs.loss
    UpperCamelCase_     :					List[str]   	=loss / gradient_accumulation_steps
    accelerator.backward(__lowercase	)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

    overall_step += 1

        # 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	)	)	)
  UpperCamelCase_     :					Optional[int]   	=tracemalloc.peaked + bamb(tracemalloc.begin	)
  if args.peak_memory_upper_bound is not None:
   assert (
       train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
   ), "Peak memory usage exceeded the upper bound"

 accelerator.wait_for_everyone()
 if accelerator.is_main_process:
  with open(os.path.join(args.output_dir						,	'peak_memory_utilization.json'	)						,	'w'	) as f:
   json.dump(__lowercase						,	__lowercase	)




def       A_  (				):
 UpperCamelCase_     :					Any   	=argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
 parser.add_argument(
     '--model_name_or_path'						,	type=__lowercase						,	default='bert-base-cased'						,	help='Path to pretrained model or model identifier from huggingface.co/models.'						,	required=__lowercase						,	)
 parser.add_argument(
     '--output_dir'						,	type=__lowercase						,	default='.'						,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'						,	)
 parser.add_argument(
     '--peak_memory_upper_bound'						,	type=__lowercase						,	default=__lowercase						,	help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'						,	)
 parser.add_argument(
     '--n_train'						,	type=__lowercase						,	default=3_20						,	help='Number of training examples to use.'						,	)
 parser.add_argument(
     '--n_val'						,	type=__lowercase						,	default=1_60						,	help='Number of validation examples to use.'						,	)
 parser.add_argument(
     '--num_epochs'						,	type=__lowercase						,	default=1						,	help='Number of train epochs.'						,	)
 UpperCamelCase_     :					List[str]   	=parser.parse_args()
 UpperCamelCase_     :					int   	={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
 training_function(__lowercase						,	__lowercase	)


if __name__ == "__main__":
 main()


"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}





class 				a__					(						A__   ,							A__       ):
     UpperCAmelCase__         =      '''focalnet'''



     def __init__(							self :Any      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :str=4      ,  _lowerCamelCase :List[str]=3      ,  _lowerCamelCase :Optional[Any]=96      ,  _lowerCamelCase :Dict=False      ,  _lowerCamelCase :Optional[Any]=[192, 384, 768, 768]      ,  _lowerCamelCase :Tuple=[2, 2, 6, 2]      ,  _lowerCamelCase :List[Any]=[2, 2, 2, 2]      ,  _lowerCamelCase :Optional[int]=[3, 3, 3, 3]      ,  _lowerCamelCase :Optional[Any]="gelu"      ,  _lowerCamelCase :Tuple=4.0      ,  _lowerCamelCase :Optional[Any]=0.0      ,  _lowerCamelCase :int=0.1      ,  _lowerCamelCase :List[str]=False      ,  _lowerCamelCase :str=1E-4      ,  _lowerCamelCase :Optional[int]=False      ,  _lowerCamelCase :List[str]=False      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :Optional[int]=0.02      ,  _lowerCamelCase :int=1E-5      ,  _lowerCamelCase :Tuple=32      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :Dict=None      ,  **_lowerCamelCase :List[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      UpperCamelCase_     :					Any   	=image_size
      UpperCamelCase_     :					int   	=patch_size
      UpperCamelCase_     :					int   	=num_channels
      UpperCamelCase_     :					Union[str, Any]   	=embed_dim
      UpperCamelCase_     :					int   	=use_conv_embed
      UpperCamelCase_     :					Optional[int]   	=hidden_sizes
      UpperCamelCase_     :					str   	=depths
      UpperCamelCase_     :					Any   	=focal_levels
      UpperCamelCase_     :					List[str]   	=focal_windows
      UpperCamelCase_     :					str   	=hidden_act
      UpperCamelCase_     :					Dict   	=mlp_ratio
      UpperCamelCase_     :					List[str]   	=hidden_dropout_prob
      UpperCamelCase_     :					Optional[int]   	=drop_path_rate
      UpperCamelCase_     :					str   	=use_layerscale
      UpperCamelCase_     :					List[Any]   	=layerscale_value
      UpperCamelCase_     :					Optional[int]   	=use_post_layernorm
      UpperCamelCase_     :					Dict   	=use_post_layernorm_in_modulation
      UpperCamelCase_     :					Optional[Any]   	=normalize_modulator
      UpperCamelCase_     :					List[Any]   	=initializer_range
      UpperCamelCase_     :					List[str]   	=layer_norm_eps
      UpperCamelCase_     :					List[Any]   	=encoder_stride
      UpperCamelCase_     :					str   	=['stem'] + [f'''stage{idx}''' for idx in range(1      ,  len(self.depths   ) + 1   )]
      UpperCamelCase_	,     UpperCamelCase_     :					int   	=get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase      ,  out_indices=_lowerCamelCase      ,  stage_names=self.stage_names   )
