'''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_:					Union[str, Any]  =16
SCREAMING_SNAKE_CASE_:					List[Any]  =32





def      lowerCAmelCase_    ( snake_case_	:			List[Any]      )   ->				Dict:


					'''simple docstring'''




					return int(x / 2**20      )




class 	__A				:



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






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





def      lowerCAmelCase_    ( snake_case_	:			Accelerator ,   snake_case_	:			int = 16 ,   snake_case_	:			str = "bert-base-cased" ,   snake_case_	:			int = 3_20 ,   snake_case_	:			int = 1_60 ,   )   ->				Dict:


					'''simple docstring'''




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

					def tokenize_function(snake_case_	:			Dict      ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCAmelCase_	   =       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
					UpperCAmelCase_	   =       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
					UpperCAmelCase_	   =       tokenized_datasets.rename_column("label" ,   "labels"      )

					def collate_fn(snake_case_	:			int      ):
										# 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.
					UpperCAmelCase_	   =       DataLoader(
					    tokenized_datasets["train"] ,   shuffle=snake_case_ ,   collate_fn=snake_case_ ,   batch_size=snake_case_      )
					UpperCAmelCase_	   =       DataLoader(
					    tokenized_datasets["validation"] ,   shuffle=snake_case_ ,   collate_fn=snake_case_ ,   batch_size=snake_case_      )

					return train_dataloader, eval_dataloader





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


					'''simple docstring'''




					UpperCAmelCase_	   =       Accelerator()

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

					set_seed(snake_case_      )
					UpperCAmelCase_	   =       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)
					UpperCAmelCase_	   =       AutoModelForSequenceClassification.from_pretrained(snake_case_ ,   return_dict=snake_case_      )

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

					if accelerator.state.deepspeed_plugin is not None:
										UpperCAmelCase_	   =       accelerator.state.deepspeed_plugin.deepspeed_config[
										    """gradient_accumulation_steps"""
										]
					else:
										UpperCAmelCase_	   =       1
					UpperCAmelCase_	   =       (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
					):
										UpperCAmelCase_	   =       get_linear_schedule_with_warmup(
										    optimizer=snake_case_ ,   num_warmup_steps=0 ,   num_training_steps=snake_case_ ,   )
					else:
										UpperCAmelCase_	   =       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.
					UpperCAmelCase_	   =       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
					UpperCAmelCase_	   =       0
					# We also need to keep track of the stating epoch so files are named properly
					UpperCAmelCase_	   =       0

					# Now we train the model
					UpperCAmelCase_	   =       {}
					for epoch in range(snake_case_ ,   snake_case_      ):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(snake_case_      ):
																				UpperCAmelCase_	   =       model(**snake_case_      )
																				UpperCAmelCase_	   =       outputs.loss
																				UpperCAmelCase_	   =       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      )      )      )
										UpperCAmelCase_	   =       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_    ( )   ->				Any:


					'''simple docstring'''




					UpperCAmelCase_	   =       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." ,   )
					UpperCAmelCase_	   =       parser.parse_args()
					UpperCAmelCase_	   =       {"""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'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




