import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=sys.maxsize							)  ->					List[str]:
        _A        =  """bilinear"""
        _A        =  max_size
        _A        =  short_edge_length



 def __call__(					self		,				lowerCAmelCase_							)  ->					Dict:
        _A        =  []
        for img in imgs:
               _A   ,  _A        =  img.shape[:2]
               # later: provide list and randomly choose index for resize
               _A        =  np.random.randint(self.short_edge_length[0]		,				self.short_edge_length[1] + 1							)
               if size == 0:
                      return img
               _A        =  size * 1.0 / min(lowerCAmelCase_		,				lowerCAmelCase_							)
               if h < w:
                      _A   ,  _A        =  size, scale * w
               else:
                      _A   ,  _A        =  scale * h, size
               if max(lowerCAmelCase_		,				lowerCAmelCase_							) > self.max_size:
                      _A        =  self.max_size * 1.0 / max(lowerCAmelCase_		,				lowerCAmelCase_							)
                      _A        =  newh * scale
                      _A        =  neww * scale
               _A        =  int(neww + 0.5							)
               _A        =  int(newh + 0.5							)

               if img.dtype == np.uinta:
                      _A        =  Image.fromarray(lowerCAmelCase_							)
                      _A        =  pil_image.resize((neww, newh)		,				PILImageResampling.BILINEAR							)
                      _A        =  np.asarray(lowerCAmelCase_							)
               else:
                      _A        =  img.permute(2		,				0		,				1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
                      _A        =  nn.functional.interpolate(
                          lowerCAmelCase_		,				(newh, neww)		,				mode=self.interp_method		,				align_corners=lowerCAmelCase_							).squeeze(0							)
               img_augs.append(lowerCAmelCase_							)

        return img_augs



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_							)  ->					Optional[int]:
        _A        =  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,				cfg.INPUT.MAX_SIZE_TEST							)
        _A        =  cfg.INPUT.FORMAT
        _A        =  cfg.SIZE_DIVISIBILITY
        _A        =  cfg.PAD_VALUE
        _A        =  cfg.INPUT.MAX_SIZE_TEST
        _A        =  cfg.MODEL.DEVICE
        _A        =  torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)		,				1		,				1							)
        _A        =  torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)		,				1		,				1							)
        _A        =  lambda lowerCAmelCase_							: (x - self.pixel_mean) / self.pixel_std



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Optional[int]:
        _A        =  tuple(max(lowerCAmelCase_							) for s in zip(*[img.shape for img in images]							)							)
        _A        =  [im.shape[-2:] for im in images]
        _A        =  [
            nn.functional.pad(
                lowerCAmelCase_		,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,				value=self.pad_value		,				)
            for size, im in zip(lowerCAmelCase_		,				lowerCAmelCase_							)
        ]

        return torch.stack(lowerCAmelCase_							), torch.tensor(lowerCAmelCase_							)



 def __call__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=False							)  ->					Any:
        with torch.no_grad():
               if not isinstance(lowerCAmelCase_		,				lowerCAmelCase_							):
                      _A        =  [images]
               if single_image:
                      assert len(lowerCAmelCase_							) == 1
               for i in range(len(lowerCAmelCase_							)							):
                      if isinstance(images[i]		,				torch.Tensor							):
                             images.insert(lowerCAmelCase_		,				images.pop(lowerCAmelCase_							).to(self.device							).float()							)
                      elif not isinstance(images[i]		,				torch.Tensor							):
                             images.insert(
                                 lowerCAmelCase_		,				torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_							)		,				input_format=self.input_format							)							)
                                 .to(self.device							)
                                 .float()		,				)
            # resize smallest edge
               _A        =  torch.tensor([im.shape[:2] for im in images]							)
               _A        =  self.aug(lowerCAmelCase_							)
               # transpose images and convert to torch tensors
               # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
               # now normalize before pad to avoid useless arithmetic
               _A        =  [self.normalizer(lowerCAmelCase_							) for x in images]
               # now pad them to do the following operations
               _A   ,  _A        =  self.pad(lowerCAmelCase_							)
               # Normalize

               if self.size_divisibility > 0:
                      raise NotImplementedError()
               # pad
               _A        =  torch.true_divide(lowerCAmelCase_		,				lowerCAmelCase_							)
               if single_image:
                      return images[0], sizes[0], scales_yx[0]
               else:
                      return images, sizes, scales_yx






def  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:Dict)						->   Dict:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes







def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:Tuple[int, int])						->   int:
       assert torch.isfinite(snake_case__).all(), "Box tensor contains infinite or NaN!"
       _A   ,  _A        =  box_size
       tensor[:, 0].clamp_(min=0   ,	max=snake_case__)
       tensor[:, 1].clamp_(min=0   ,	max=snake_case__)
       tensor[:, 2].clamp_(min=0   ,	max=snake_case__)
       tensor[:, 3].clamp_(min=0   ,	max=snake_case__)





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										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






 def __enter__(					self							)  ->					List[str]:
        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_							)  ->					Optional[int]:
        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  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _A        =  AutoTokenizer.from_pretrained(snake_case__)
       _A        =  load_dataset(
           """glue"""   ,	"""mrpc"""   ,	split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''})

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

       def collate_fn(snake_case__			:List[str]):
              # 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        =  DataLoader(
           tokenized_datasets["""train"""]   ,	shuffle=snake_case__   ,	collate_fn=snake_case__   ,	batch_size=snake_case__)
       _A        =  DataLoader(
           tokenized_datasets["""validation"""]   ,	shuffle=snake_case__   ,	collate_fn=snake_case__   ,	batch_size=snake_case__)

       return train_dataloader, eval_dataloader






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _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(snake_case__)
       _A   ,  _A        =  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        =  AutoModelForSequenceClassification.from_pretrained(snake_case__   ,	return_dict=snake_case__)

       # 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=snake_case__)

       if accelerator.state.deepspeed_plugin is not None:
              _A        =  accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              _A        =  1
       _A        =  (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        =  get_linear_schedule_with_warmup(
                  optimizer=snake_case__   ,	num_warmup_steps=0   ,	num_training_steps=snake_case__   ,	)
       else:
              _A        =  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        =  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        =  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(snake_case__   ,	snake_case__):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(snake_case__):
                            _A        =  model(**snake_case__)
                            _A        =  outputs.loss
                            _A        =  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        =  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  snake_case  (       )						->   Optional[int]:
       _A        =  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        =  parser.parse_args()
       _A        =  {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(snake_case__   ,	snake_case__)


if __name__ == "__main__":
    main()




