
'''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


lowercase     :							List[str]       						=     16
lowercase     :							Optional[int]       						=     32







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    return int(x / 2**20		)





class 					A							:





     def __enter__(				self      )					->							Dict:






         """simple docstring"""






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





     def __exit__(				self		,     *SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__ = 16						,							snake_case__ = "bert-base-cased"						,							snake_case__ = 320						,							snake_case__ = 160						,							):




    '''simple docstring'''



    A			:  str   =		AutoTokenizer.from_pretrained(snake_case__		)
    A			:  str   =		load_dataset(
        '''glue'''						,							'''mrpc'''						,							split={'''train''': F'train[:{n_train}]', '''validation''': F'validation[:{n_val}]'}		)

    def tokenize_function(snake_case__		):
        # max_length=None => use the model max length (it's actually the default)
        A			:  Optional[int]   =		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
    A			:  Any   =		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
    A			:  List[Any]   =		tokenized_datasets.rename_column('''label'''						,							'''labels'''		)

    def collate_fn(snake_case__		):
        # 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=128						,							return_tensors='''pt'''		)
        return tokenizer.pad(snake_case__						,							padding='''longest'''						,							return_tensors='''pt'''		)

    # Instantiate dataloaders.
    A			:  Dict   =		DataLoader(
        tokenized_datasets['''train''']						,							shuffle=snake_case__						,							collate_fn=snake_case__						,							batch_size=snake_case__		)
    A			:  Optional[Any]   =		DataLoader(
        tokenized_datasets['''validation''']						,							shuffle=snake_case__						,							collate_fn=snake_case__						,							batch_size=snake_case__		)

    return train_dataloader, eval_dataloader







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  str   =		Accelerator()

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

    set_seed(snake_case__		)
    A,    A			:  str   =		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)
    A			:  Optional[int]   =		AutoModelForSequenceClassification.from_pretrained(snake_case__						,							return_dict=snake_case__		)

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

    if accelerator.state.deepspeed_plugin is not None:
        A			:  Any   =		accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        A			:  int   =		1
    A			:  List[str]   =		(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
    ):
        A			:  int   =		get_linear_schedule_with_warmup(
            optimizer=snake_case__						,							num_warmup_steps=0						,							num_training_steps=snake_case__						,							)
    else:
        A			:  Tuple   =		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.
    A,    A,    A,    A,    A			:  Optional[Any]   =		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
    A			:  List[Any]   =		0
    # We also need to keep track of the stating epoch so files are named properly
    A			:  Optional[Any]   =		0

    # Now we train the model
    A			:  str   =		{}
    for epoch in range(snake_case__						,							snake_case__		):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(snake_case__		):
                A			:  Optional[int]   =		model(**snake_case__		)
                A			:  List[str]   =		outputs.loss
                A			:  Union[str, Any]   =		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		)		)		)
        A			:  Any   =		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 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Union[str, Any]   =		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=320						,							help='''Number of training examples to use.'''						,							)
    parser.add_argument(
        '''--n_val'''						,							type=snake_case__						,							default=160						,							help='''Number of validation examples to use.'''						,							)
    parser.add_argument(
        '''--num_epochs'''						,							type=snake_case__						,							default=1						,							help='''Number of train epochs.'''						,							)
    A			:  Optional[Any]   =		parser.parse_args()
    A			:  Union[str, Any]   =		{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(snake_case__						,							snake_case__		)


if __name__ == "__main__":
      main()






'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase     :							Optional[Any]       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''deta'''
     __magic_name__          =						{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }





     def __init__(				self		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=900		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=1024		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=256		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE="sine"		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=300		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.25		,     **SCREAMING_SNAKE_CASE		,     )					->							Any:






         """simple docstring"""






         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
             A			:  Optional[Any]   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']      )
         else:
             if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 A			:  List[Any]   =		backbone_config.pop('''model_type'''      )
                 A			:  List[Any]   =		CONFIG_MAPPING[backbone_model_type]
                 A			:  Optional[int]   =		config_class.from_dict(SCREAMING_SNAKE_CASE      )

         A			:  str   =		backbone_config
         A			:  Optional[int]   =		num_queries
         A			:  Dict   =		max_position_embeddings
         A			:  Optional[Any]   =		d_model
         A			:  Optional[Any]   =		encoder_ffn_dim
         A			:  List[str]   =		encoder_layers
         A			:  Tuple   =		encoder_attention_heads
         A			:  Optional[Any]   =		decoder_ffn_dim
         A			:  Optional[int]   =		decoder_layers
         A			:  List[str]   =		decoder_attention_heads
         A			:  Union[str, Any]   =		dropout
         A			:  str   =		attention_dropout
         A			:  Any   =		activation_dropout
         A			:  Optional[int]   =		activation_function
         A			:  Tuple   =		init_std
         A			:  Any   =		init_xavier_std
         A			:  Optional[Any]   =		encoder_layerdrop
         A			:  int   =		auxiliary_loss
         A			:  Dict   =		position_embedding_type
         # deformable attributes
         A			:  str   =		num_feature_levels
         A			:  Optional[int]   =		encoder_n_points
         A			:  Any   =		decoder_n_points
         A			:  Tuple   =		two_stage
         A			:  Dict   =		two_stage_num_proposals
         A			:  List[str]   =		with_box_refine
         A			:  List[str]   =		assign_first_stage
         if two_stage is True and with_box_refine is False:
             raise ValueError('''If two_stage is True, with_box_refine must be True.'''      )
         # Hungarian matcher
         A			:  Dict   =		class_cost
         A			:  Optional[int]   =		bbox_cost
         A			:  Optional[Any]   =		giou_cost
         # Loss coefficients
         A			:  int   =		mask_loss_coefficient
         A			:  int   =		dice_loss_coefficient
         A			:  Tuple   =		bbox_loss_coefficient
         A			:  int   =		giou_loss_coefficient
         A			:  Dict   =		eos_coefficient
         A			:  Optional[Any]   =		focal_alpha
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.encoder_attention_heads





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.d_model





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Any   =		copy.deepcopy(self.__dict__      )
         A			:  Dict   =		self.backbone_config.to_dict()
         A			:  List[Any]   =		self.__class__.model_type
         return output




