





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							   =							16
__A							   =							32





def 	lowerCAmelCase_					(			__a						)	->						Tuple:







  """simple docstring"""





  return int(x / 2**20						)



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __enter__(self							:  List[str])			->Optional[int]:



   '''simple docstring'''



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







 def __exit__(self							:  List[Any]			,					*UpperCAmelCase_							:  Optional[Any])			->str:



   '''simple docstring'''



   gc.collect()
   torch.cuda.empty_cache()
   lowerCamelCase__: Tuple         =torch.cuda.memory_allocated()
   lowerCamelCase__: Dict         =torch.cuda.max_memory_allocated()
   lowerCamelCase__: Optional[int]         =bamb(self.end - self.begin)
   lowerCamelCase__: List[str]         =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 = 320      ,       __a = 160      ,       )	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: Optional[Any]         =AutoTokenizer.from_pretrained(__a						)
  lowerCamelCase__: Any         =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)
    lowerCamelCase__: Optional[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
  lowerCamelCase__: Any         =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
  lowerCamelCase__: Optional[Any]         =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=128      ,       return_tensors="pt"						)
    return tokenizer.pad(__a      ,       padding="longest"      ,       return_tensors="pt"						)

  # Instantiate dataloaders.
  lowerCamelCase__: List[Any]         =DataLoader(
      tokenized_datasets["train"]      ,       shuffle=__a      ,       collate_fn=__a      ,       batch_size=__a						)
  lowerCamelCase__: Dict         =DataLoader(
      tokenized_datasets["validation"]      ,       shuffle=__a      ,       collate_fn=__a      ,       batch_size=__a						)

  return train_dataloader, eval_dataloader





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						int:







  """simple docstring"""





  lowerCamelCase__: Union[str, Any]         =Accelerator()

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

  set_seed(__a						)
  lowerCamelCase__    ,   lowerCamelCase__: List[str]         =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)
  lowerCamelCase__: Union[str, Any]         =AutoModelForSequenceClassification.from_pretrained(__a      ,       return_dict=__a						)

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

  if accelerator.state.deepspeed_plugin is not None:
    lowerCamelCase__: int         =accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    lowerCamelCase__: Dict         =1
  lowerCamelCase__: Optional[int]         =(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
  ):
    lowerCamelCase__: int         =get_linear_schedule_with_warmup(
        optimizer=__a      ,       num_warmup_steps=0      ,       num_training_steps=__a      ,       )
  else:
    lowerCamelCase__: Optional[int]         =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.
  lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__: Tuple         =accelerator.prepare(
      __a      ,       __a      ,       __a      ,       __a      ,       __a						)

  # We need to keep track of how many total steps we have iterated over
  lowerCamelCase__: Union[str, Any]         =0
  # We also need to keep track of the stating epoch so files are named properly
  lowerCamelCase__: Union[str, Any]         =0

  # Now we train the model
  lowerCamelCase__: Tuple         ={}
  for epoch in range(__a      ,       __a						):
    with TorchTracemalloc() as tracemalloc:
      model.train()
      for step, batch in enumerate(__a						):
        lowerCamelCase__: List[str]         =model(**__a						)
        lowerCamelCase__: Optional[Any]         =outputs.loss
        lowerCamelCase__: Dict         =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						)						)						)
    lowerCamelCase__: str         =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_					(			)	->						Optional[int]:







  """simple docstring"""





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


if __name__ == "__main__":
     main()







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class    lowercase_		:
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
								assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
								assert len(self.x.shape  ) == len(self.y.shape  ) == len(self.z.shape  ) == len(self.origin.shape  ) == 2



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return torch.from_numpy(np.array([self.width, self.height]					,							dtype=np.floataa  )  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return torch.from_numpy(np.array([self.x_fov, self.y_fov]					,							dtype=np.floataa  )  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.arange(self.height * self.width  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case__					,							self.width					,							rounding_mode='trunc'  ),
								    ]					,							axis=1					,							)
								return coords



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

								SCREAMING_SNAKE_CASE_					      =							self.get_image_coords()
								SCREAMING_SNAKE_CASE_					      =							torch.broadcast_to(coords.unsqueeze(0  )					,							[batch_size * inner_batch_size, *coords.shape]  )
								SCREAMING_SNAKE_CASE_					      =							self.get_camera_rays(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   torch.Tensor  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								SCREAMING_SNAKE_CASE_					      =							coords.view(snake_case__					,							-1					,							2  )

								SCREAMING_SNAKE_CASE_					      =							self.resolution()
								SCREAMING_SNAKE_CASE_					      =							self.fov()

								SCREAMING_SNAKE_CASE_					      =							(flat.float() / (res - 1)) * 2 - 1
								SCREAMING_SNAKE_CASE_					      =							fracs * torch.tan(fov / 2  )

								SCREAMING_SNAKE_CASE_					      =							fracs.view(snake_case__					,							-1					,							2  )
								SCREAMING_SNAKE_CASE_					      =							(
								    self.z.view(snake_case__					,							1					,							3  )
								    + self.x.view(snake_case__					,							1					,							3  ) * fracs[:, :, :1]
								    + self.y.view(snake_case__					,							1					,							3  ) * fracs[:, :, 1:]
								)
								SCREAMING_SNAKE_CASE_					      =							directions / directions.norm(dim=-1					,							keepdim=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        torch.broadcast_to(self.origin.view(snake_case__					,							1					,							3  )					,							[batch_size, directions.shape[1], 3]  ),
								        directions,
								    ]					,							dim=2					,							)
								return rays.view(snake_case__					,							*snake_case__					,							2					,							3  )






						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   int  ):

								"""simple docstring"""

								assert width * self.height == height * self.width, "The aspect ratio should not change."
								return DifferentiableProjectiveCamera(
								    origin=self.origin					,							x=self.x					,							y=self.y					,							z=self.z					,							width=snake_case__					,							height=snake_case__					,							x_fov=self.x_fov					,							y_fov=self.y_fov					,							)


def 					_a							(					lowerCAmelCase )->	DifferentiableProjectiveCamera:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		for theta in np.linspace(0    ,						2 * np.pi    ,						num=20 ):
				SCREAMING_SNAKE_CASE_					      =							np.array([np.sin(lowerCAmelCase ), np.cos(lowerCAmelCase ), -0.5] )
				z /= np.sqrt(np.sum(z**2 ) )
				SCREAMING_SNAKE_CASE_					      =							-z * 4
				SCREAMING_SNAKE_CASE_					      =							np.array([np.cos(lowerCAmelCase ), -np.sin(lowerCAmelCase ), 0.0] )
				SCREAMING_SNAKE_CASE_					      =							np.cross(lowerCAmelCase    ,						lowerCAmelCase )
				origins.append(lowerCAmelCase )
				xs.append(lowerCAmelCase )
				ys.append(lowerCAmelCase )
				zs.append(lowerCAmelCase )
		return DifferentiableProjectiveCamera(
		    origin=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						x=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						y=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						z=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						width=lowerCAmelCase    ,						height=lowerCAmelCase    ,						x_fov=0.7    ,						y_fov=0.7    ,						shape=(1, len(lowerCAmelCase ))    ,						)