





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__:    List[str]  	=	16
A__:    int  	=	32



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




class        _a    :

      """simple docstring"""


      def __enter__(		self:			str					):
          '''simple docstring'''


          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          UpperCamelCase__:	Optional[Any]					=     torch.cuda.memory_allocated()
          return self






      def __exit__(		self:			Dict	,    *__lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          gc.collect()
          torch.cuda.empty_cache()
          UpperCamelCase__:	int					=     torch.cuda.memory_allocated()
          UpperCamelCase__:	Optional[Any]					=     torch.cuda.max_memory_allocated()
          UpperCamelCase__:	int					=     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 				lowerCAmelCase_ (							A_    ,A_ = 16    ,A_ = "bert-base-cased"    ,A_ = 3_20    ,A_ = 1_60    ,):
    UpperCamelCase__:	Optional[int]					=     AutoTokenizer.from_pretrained(A_)
    UpperCamelCase__:	List[str]					=     load_dataset(
        "glue"    ,"mrpc"    ,split={"train": F"train[:{n_train}]", "validation": F"validation[:{n_val}]"})

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

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

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

    # Instantiate dataloaders.
    UpperCamelCase__:	Dict					=     DataLoader(
        tokenized_datasets["train"]    ,shuffle=A_    ,collate_fn=A_    ,batch_size=A_)
    UpperCamelCase__:	Union[str, Any]					=     DataLoader(
        tokenized_datasets["validation"]    ,shuffle=A_    ,collate_fn=A_    ,batch_size=A_)

    return train_dataloader, eval_dataloader



def 				lowerCAmelCase_ (							A_    ,A_):
    # Initialize accelerator
    UpperCamelCase__:	List[str]					=     Accelerator()

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

    set_seed(A_)
    UpperCamelCase__				, UpperCamelCase__:	Union[str, Any]					=     get_dataloaders(A_    ,A_    ,A_    ,args.n_train    ,args.n_val)

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

    # Instantiate optimizer
    UpperCamelCase__:	Union[str, Any]					=     (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    UpperCamelCase__:	Optional[int]					=     optimizer_cls(params=model.parameters()    ,lr=A_)

    if accelerator.state.deepspeed_plugin is not None:
        UpperCamelCase__:	Any					=     accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        UpperCamelCase__:	Dict					=     1
    UpperCamelCase__:	List[Any]					=     (len(A_) * 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=A_    ,num_warmup_steps=0    ,num_training_steps=A_    ,)
    else:
        UpperCamelCase__:	List[str]					=     DummyScheduler(A_    ,total_num_steps=A_    ,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__:	Tuple					=     accelerator.prepare(
        A_    ,A_    ,A_    ,A_    ,A_)

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

    # Now we train the model
    UpperCamelCase__:	str					=     {}
    for epoch in range(A_    ,A_):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(A_):
                UpperCamelCase__:	int					=     model(**A_)
                UpperCamelCase__:	Optional[int]					=     outputs.loss
                UpperCamelCase__:	Union[str, Any]					=     loss / gradient_accumulation_steps
                accelerator.backward(A_)
                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__:	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(A_    ,A_)



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


if __name__ == "__main__":
   main()








import copy

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


A__:    str  	=	logging.get_logger(__name__)

A__:    Union[str, Any]  	=	{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """deformable_detr"""
      UpperCamelCase__  		=       {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }






      def __init__(		self:			Dict	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			List[str]=None	,    __lowerCamelCase:			Tuple=3	,    __lowerCamelCase:			Union[str, Any]=300	,    __lowerCamelCase:			Optional[Any]=1024	,    __lowerCamelCase:			Optional[int]=6	,    __lowerCamelCase:			Optional[int]=1024	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			Any=6	,    __lowerCamelCase:			Tuple=1024	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			Optional[int]=0.0	,    __lowerCamelCase:			Union[str, Any]=True	,    __lowerCamelCase:			List[str]="relu"	,    __lowerCamelCase:			Tuple=256	,    __lowerCamelCase:			Dict=0.1	,    __lowerCamelCase:			Optional[Any]=0.0	,    __lowerCamelCase:			Optional[int]=0.0	,    __lowerCamelCase:			Any=0.02	,    __lowerCamelCase:			int=1.0	,    __lowerCamelCase:			Optional[Any]=True	,    __lowerCamelCase:			str=False	,    __lowerCamelCase:			Any="sine"	,    __lowerCamelCase:			Optional[Any]="resnet50"	,    __lowerCamelCase:			Optional[int]=True	,    __lowerCamelCase:			int=False	,    __lowerCamelCase:			int=4	,    __lowerCamelCase:			int=4	,    __lowerCamelCase:			Any=4	,    __lowerCamelCase:			Any=False	,    __lowerCamelCase:			List[str]=300	,    __lowerCamelCase:			Dict=False	,    __lowerCamelCase:			str=1	,    __lowerCamelCase:			int=5	,    __lowerCamelCase:			str=2	,    __lowerCamelCase:			Dict=1	,    __lowerCamelCase:			Tuple=1	,    __lowerCamelCase:			Union[str, Any]=5	,    __lowerCamelCase:			Optional[Any]=2	,    __lowerCamelCase:			List[str]=0.1	,    __lowerCamelCase:			Dict=0.25	,    __lowerCamelCase:			Dict=False	,    **__lowerCamelCase:			str	,    ):
          '''simple docstring'''


          if backbone_config is not None and use_timm_backbone:
              raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."					)

          if not use_timm_backbone:
              if backbone_config is None:
                  logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
                  UpperCamelCase__:	Union[str, Any]					=     CONFIG_MAPPING["resnet"](out_features=["stage4"]					)
              elif isinstance(__lowerCamelCase	,    __lowerCamelCase					):
                  UpperCamelCase__:	Tuple					=     backbone_config.get("model_type"					)
                  UpperCamelCase__:	Optional[int]					=     CONFIG_MAPPING[backbone_model_type]
                  UpperCamelCase__:	Tuple					=     config_class.from_dict(__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     use_timm_backbone
          UpperCamelCase__:	Any					=     backbone_config
          UpperCamelCase__:	Optional[int]					=     num_channels
          UpperCamelCase__:	int					=     num_queries
          UpperCamelCase__:	List[str]					=     max_position_embeddings
          UpperCamelCase__:	Dict					=     d_model
          UpperCamelCase__:	List[Any]					=     encoder_ffn_dim
          UpperCamelCase__:	Union[str, Any]					=     encoder_layers
          UpperCamelCase__:	Tuple					=     encoder_attention_heads
          UpperCamelCase__:	Tuple					=     decoder_ffn_dim
          UpperCamelCase__:	Optional[int]					=     decoder_layers
          UpperCamelCase__:	int					=     decoder_attention_heads
          UpperCamelCase__:	Optional[Any]					=     dropout
          UpperCamelCase__:	List[str]					=     attention_dropout
          UpperCamelCase__:	List[Any]					=     activation_dropout
          UpperCamelCase__:	List[Any]					=     activation_function
          UpperCamelCase__:	Union[str, Any]					=     init_std
          UpperCamelCase__:	List[str]					=     init_xavier_std
          UpperCamelCase__:	Optional[Any]					=     encoder_layerdrop
          UpperCamelCase__:	List[str]					=     auxiliary_loss
          UpperCamelCase__:	Optional[int]					=     position_embedding_type
          UpperCamelCase__:	Optional[Any]					=     backbone
          UpperCamelCase__:	Any					=     use_pretrained_backbone
          UpperCamelCase__:	Union[str, Any]					=     dilation
          # deformable attributes
          UpperCamelCase__:	Union[str, Any]					=     num_feature_levels
          UpperCamelCase__:	Optional[Any]					=     encoder_n_points
          UpperCamelCase__:	Tuple					=     decoder_n_points
          UpperCamelCase__:	Any					=     two_stage
          UpperCamelCase__:	Optional[int]					=     two_stage_num_proposals
          UpperCamelCase__:	str					=     with_box_refine
          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
          UpperCamelCase__:	Any					=     class_cost
          UpperCamelCase__:	str					=     bbox_cost
          UpperCamelCase__:	List[str]					=     giou_cost
          # Loss coefficients
          UpperCamelCase__:	Optional[Any]					=     mask_loss_coefficient
          UpperCamelCase__:	List[Any]					=     dice_loss_coefficient
          UpperCamelCase__:	Optional[Any]					=     bbox_loss_coefficient
          UpperCamelCase__:	List[Any]					=     giou_loss_coefficient
          UpperCamelCase__:	Dict					=     eos_coefficient
          UpperCamelCase__:	List[Any]					=     focal_alpha
          UpperCamelCase__:	Dict					=     disable_custom_kernels
          super().__init__(is_encoder_decoder=__lowerCamelCase	,    **__lowerCamelCase					)






      @property
      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          return self.encoder_attention_heads






      @property
      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          return self.d_model






      def 			UpperCAmelCase_       (		self:			int					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     copy.deepcopy(self.__dict__					)
          if self.backbone_config is not None:
              UpperCamelCase__:	Tuple					=     self.backbone_config.to_dict()
          UpperCamelCase__:	Optional[int]					=     self.__class__.model_type
          return output

