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


UpperCAmelCase_								=							16
UpperCAmelCase_								=							32

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       return int(x / 2**20   )





class      lowerCAmelCase						:



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





     def __exit__(					self				,      *lowerCAmelCase__       ):
            gc.collect()
            torch.cuda.empty_cache()
            _A=	torch.cuda.memory_allocated()
            _A=	torch.cuda.max_memory_allocated()
            _A=	bamb(self.end - self.begin       )
            _A=	bamb(self.peak - self.begin       )
            # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_ = 16       ,					lowerCAmelCase_ = "bert-base-cased"       ,					lowerCAmelCase_ = 3_20       ,					lowerCAmelCase_ = 1_60       ,					)		->						Optional[Any]:




       '''simple docstring'''






       _A=	AutoTokenizer.from_pretrained(lowerCAmelCase_   )
       _A=	load_dataset(
           'glue'       ,					'mrpc'       ,					split={'train': F"train[:{n_train}]", 'validation': F"validation[:{n_val}]"}   )

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

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

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

       def collate_fn(lowerCAmelCase_   ):
              # 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(lowerCAmelCase_       ,					padding='max_length'       ,					max_length=1_28       ,					return_tensors='pt'   )
              return tokenizer.pad(lowerCAmelCase_       ,					padding='longest'       ,					return_tensors='pt'   )

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

       return train_dataloader, eval_dataloader

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       _A=	Accelerator()

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

       set_seed(lowerCAmelCase_   )
       _A,			_A=	get_dataloaders(lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					args.n_train       ,					args.n_val   )

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

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

       if accelerator.state.deepspeed_plugin is not None:
              _A=	accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              _A=	1
       _A=	(len(lowerCAmelCase_   ) * num_epochs) // gradient_accumulation_steps

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

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

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

def 							UpperCamelCase				(    )		->						Any:




       '''simple docstring'''






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


if __name__ == "__main__":
      main()
import copy
import os
from typing import Union

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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_vision_model"""



     def __init__(					self				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3				,      lowerCAmelCase__=16				,      lowerCAmelCase__=288				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__=True				,      lowerCAmelCase__=False				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_channels
            _A=	patch_size
            _A=	image_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	stop_gradient
            _A=	share_layernorm
            _A=	remove_last_layer





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	config_dict['text_config']

            if "model_type" in config_dict and hasattr(cls				,      'model_type'       ) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

            return cls.from_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_text_model"""



     def __init__(					self				,      lowerCAmelCase__=50265				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=514				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__="absolute"				,      lowerCAmelCase__=True				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	initializer_factor
            _A=	intermediate_size
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	type_vocab_size
            _A=	layer_norm_eps
            _A=	position_embedding_type
            _A=	use_cache
            _A=	pad_token_id
            _A=	bos_token_id
            _A=	eos_token_id





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	config_dict['text_config']

            if "model_type" in config_dict and hasattr(cls				,      'model_type'       ) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

            return cls.from_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Dict		="""bridgetower"""



     def __init__(					self				,      lowerCAmelCase__=True				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=768				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__="add"				,      lowerCAmelCase__=12				,      lowerCAmelCase__=6				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            # TODO: remove this once the Hub files are updated.
            _A=	kwargs.pop('text_config_dict'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('vision_config_dict'				,      lowerCAmelCase__       )

            super().__init__(**lowerCAmelCase__       )
            _A=	share_cross_modal_transformer_layers
            _A=	hidden_act
            _A=	hidden_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	share_link_tower_layers
            _A=	link_tower_type
            _A=	num_attention_heads
            _A=	num_hidden_layers
            _A=	tie_word_embeddings
            _A=	init_layernorm_from_vision_encoder

            if text_config is None:
                   _A=	{}
                   logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'       )

            if vision_config is None:
                   _A=	{}
                   logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'       )

            _A=	BridgeTowerTextConfig(**lowerCAmelCase__       )
            _A=	BridgeTowerVisionConfig(**lowerCAmelCase__       )



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return cls(text_config=text_config.to_dict()				,      vision_config=vision_config.to_dict()				,      **lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	copy.deepcopy(self.__dict__       )
            _A=	self.text_config.to_dict()
            _A=	self.vision_config.to_dict()
            _A=	self.__class__.model_type
            return output