'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:      str					      =				logging.get_logger(__name__)

_A			:      List[Any]					      =				{
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""visual_bert"""
 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	int=3_05_22      ,       SCREAMING_SNAKE_CASE__   :	str=7_68      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=5_12      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=12      ,       SCREAMING_SNAKE_CASE__   :	Any=12      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=30_72      ,       SCREAMING_SNAKE_CASE__   :	int="gelu"      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Any=5_12      ,       SCREAMING_SNAKE_CASE__   :	Tuple=2      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	int=1e-1_2      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=False      ,       SCREAMING_SNAKE_CASE__   :	Any=True      ,       SCREAMING_SNAKE_CASE__   :	int=1      ,       SCREAMING_SNAKE_CASE__   :	Any=0      ,       SCREAMING_SNAKE_CASE__   :	str=2      ,       **SCREAMING_SNAKE_CASE__   :	List[Any]      ,       )  ->    str:
  super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__      ,       bos_token_id=SCREAMING_SNAKE_CASE__      ,       eos_token_id=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						max_position_embeddings
  __lowerCAmelCase											=						hidden_size
  __lowerCAmelCase											=						visual_embedding_dim
  __lowerCAmelCase											=						num_hidden_layers
  __lowerCAmelCase											=						num_attention_heads
  __lowerCAmelCase											=						intermediate_size
  __lowerCAmelCase											=						hidden_act
  __lowerCAmelCase											=						hidden_dropout_prob
  __lowerCAmelCase											=						attention_probs_dropout_prob
  __lowerCAmelCase											=						initializer_range
  __lowerCAmelCase											=						type_vocab_size
  __lowerCAmelCase											=						layer_norm_eps
  __lowerCAmelCase											=						bypass_transformer
  __lowerCAmelCase											=						special_visual_initialize







'''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


_A			:      Optional[Any]					      =				16
_A			:      Union[str, Any]					      =				32







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     str:






 '''simple docstring'''

 return int(x / 2**20      )







class 				_lowercase     :


 '''simple docstring'''






 def __enter__(      self   :	List[Any]							)  ->    int:
  gc.collect()
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  __lowerCAmelCase											=						torch.cuda.memory_allocated()
  return self





 def __exit__(      self   :	Tuple      ,       *SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    Optional[Any]:
  gc.collect()
  torch.cuda.empty_cache()
  __lowerCAmelCase											=						torch.cuda.memory_allocated()
  __lowerCAmelCase											=						torch.cuda.max_memory_allocated()
  __lowerCAmelCase											=						bamb(self.end - self.begin							)
  __lowerCAmelCase											=						bamb(self.peak - self.begin							)
  # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def      UpperCamelCase_						(					snake_case_       :       Accelerator						,		snake_case_       :       int = 16						,		snake_case_       :       str = "bert-base-cased"						,		snake_case_       :       int = 3_20						,		snake_case_       :       int = 1_60						,		)					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained(snake_case_      )
 __lowerCAmelCase											=						load_dataset(
     """glue"""						,		"""mrpc"""						,		split={"""train""": f"""train[:{n_train}]""", """validation""": f"""validation[:{n_val}]"""}      )

 def tokenize_function(snake_case_       :       List[Any]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						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
 __lowerCAmelCase											=						datasets.map(
     snake_case_						,		batched=snake_case_						,		remove_columns=["""idx""", """sentence1""", """sentence2"""]						,		load_from_cache_file=snake_case_      )

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 __lowerCAmelCase											=						tokenized_datasets.rename_column("""label"""						,		"""labels"""      )

 def collate_fn(snake_case_       :       List[Any]      ):
  # 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(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(
     tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=snake_case_      )
 __lowerCAmelCase											=						DataLoader(
     tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=snake_case_      )

 return train_dataloader, eval_dataloader







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowerCAmelCase											=						config["""lr"""]
 __lowerCAmelCase											=						int(config["""num_epochs"""]      )
 __lowerCAmelCase											=						int(config["""seed"""]      )
 __lowerCAmelCase											=						int(config["""batch_size"""]      )
 __lowerCAmelCase											=						args.model_name_or_path

 set_seed(snake_case_      )
 __lowerCAmelCase							,       __lowerCAmelCase											=						get_dataloaders(snake_case_						,		snake_case_						,		snake_case_						,		args.n_train						,		args.n_val      )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __lowerCAmelCase											=						AutoModelForSequenceClassification.from_pretrained(snake_case_						,		return_dict=snake_case_      )

 # Instantiate optimizer
 __lowerCAmelCase											=						(
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 __lowerCAmelCase											=						optimizer_cls(params=model.parameters()						,		lr=snake_case_      )

 if accelerator.state.deepspeed_plugin is not None:
  __lowerCAmelCase											=						accelerator.state.deepspeed_plugin.deepspeed_config[
      """gradient_accumulation_steps"""
  ]
 else:
  __lowerCAmelCase											=						1
 __lowerCAmelCase											=						(len(snake_case_      ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  __lowerCAmelCase											=						get_linear_schedule_with_warmup(
      optimizer=snake_case_						,		num_warmup_steps=0						,		num_training_steps=snake_case_						,		)
 else:
  __lowerCAmelCase											=						DummyScheduler(snake_case_						,		total_num_steps=snake_case_						,		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.
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						accelerator.prepare(
     snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_      )

 # We need to keep track of how many total steps we have iterated over
 __lowerCAmelCase											=						0
 # We also need to keep track of the stating epoch so files are named properly
 __lowerCAmelCase											=						0

 # Now we train the model
 __lowerCAmelCase											=						{}
 for epoch in range(snake_case_						,		snake_case_      ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(snake_case_      ):
    __lowerCAmelCase											=						model(**snake_case_      )
    __lowerCAmelCase											=						outputs.loss
    __lowerCAmelCase											=						loss / gradient_accumulation_steps
    accelerator.backward(snake_case_      )
    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      )      )      )
  __lowerCAmelCase											=						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(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					)					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""      )
 parser.add_argument(
     """--model_name_or_path"""						,		type=snake_case_						,		default="""bert-base-cased"""						,		help="""Path to pretrained model or model identifier from huggingface.co/models."""						,		required=snake_case_						,		)
 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(
     """--peak_memory_upper_bound"""						,		type=snake_case_						,		default=snake_case_						,		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=snake_case_						,		default=3_20						,		help="""Number of training examples to use."""						,		)
 parser.add_argument(
     """--n_val"""						,		type=snake_case_						,		default=1_60						,		help="""Number of validation examples to use."""						,		)
 parser.add_argument(
     """--num_epochs"""						,		type=snake_case_						,		default=1						,		help="""Number of train epochs."""						,		)
 __lowerCAmelCase											=						parser.parse_args()
 __lowerCAmelCase											=						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
 training_function(snake_case_						,		snake_case_      )


if __name__ == "__main__":
 main()






