import argparse
import os

import evaluate
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__snake_case         =     16
__snake_case         =     32





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase = 16    )->	Optional[int]:






      '''simple docstring'''





      UpperCAmelCase     :			str     	=AutoTokenizer.from_pretrained('''bert-base-cased'''    )
      UpperCAmelCase     :			Dict     	=load_dataset('''glue'''		,       '''mrpc'''    )

      def tokenize_function(__lowerCAmelCase    ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase     :			int     	=tokenizer(examples['''sentence1''']		,       examples['''sentence2''']		,       truncation=lowerCAmelCase__		,       max_length=lowerCAmelCase__    )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            UpperCAmelCase     :			Dict     	=datasets.map(
                lowerCAmelCase__		,       batched=lowerCAmelCase__		,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,       )

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

      def collate_fn(__lowerCAmelCase    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase     :			Optional[Any]     	=1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  UpperCAmelCase     :			str     	=16
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase     :			List[str]     	=8
            else:
                  UpperCAmelCase     :			str     	=None

            return tokenizer.pad(
                lowerCAmelCase__		,       padding='''longest'''		,       max_length=lowerCAmelCase__		,       pad_to_multiple_of=lowerCAmelCase__		,       return_tensors='''pt'''		,       )

      # Instantiate dataloaders.
      UpperCAmelCase     :			int     	=DataLoader(
          tokenized_datasets['''train''']		,       shuffle=lowerCAmelCase__		,       collate_fn=lowerCAmelCase__		,       batch_size=lowerCAmelCase__    )
      UpperCAmelCase     :			Any     	=DataLoader(
          tokenized_datasets['''validation''']		,       shuffle=lowerCAmelCase__		,       collate_fn=lowerCAmelCase__		,       batch_size=lowerCAmelCase__    )

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     __snake_case         =     mocked_dataloaders  # noqa: F811





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






      '''simple docstring'''





      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,       lowerCAmelCase__    ) == "1":
            UpperCAmelCase     :			Any     	=2
      # Initialize accelerator
      UpperCAmelCase     :			Any     	=Accelerator(cpu=args.cpu		,       mixed_precision=args.mixed_precision    )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase     :			List[Any]     	=config['''lr''']
      UpperCAmelCase     :			List[str]     	=int(config['''num_epochs''']    )
      UpperCAmelCase     :			int     	=int(config['''seed''']    )
      UpperCAmelCase     :			str     	=int(config['''batch_size''']    )

      # New Code #
      # Parse out whether we are saving every epoch or after a certain number of batches
      if hasattr(args.checkpointing_steps		,       '''isdigit'''    ):
            if args.checkpointing_steps == "epoch":
                  UpperCAmelCase     :			int     	=args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  UpperCAmelCase     :			List[Any]     	=int(args.checkpointing_steps    )
            else:
                  raise ValueError(
                      f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
      else:
            UpperCAmelCase     :			List[Any]     	=None

      set_seed(lowerCAmelCase__    )

      UpperCAmelCase	,    UpperCAmelCase     :			int     	=get_dataloaders(lowerCAmelCase__		,       lowerCAmelCase__    )
      UpperCAmelCase     :			Optional[Any]     	=evaluate.load('''glue'''		,       '''mrpc'''    )

      # If the batch size is too big we use gradient accumulation
      UpperCAmelCase     :			str     	=1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            UpperCAmelCase     :			List[Any]     	=batch_size // MAX_GPU_BATCH_SIZE
            UpperCAmelCase     :			int     	=MAX_GPU_BATCH_SIZE

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase     :			List[str]     	=AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,       return_dict=lowerCAmelCase__    )

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      UpperCAmelCase     :			Dict     	=model.to(accelerator.device    )

      # Instantiate optimizer
      UpperCAmelCase     :			Any     	=AdamW(params=model.parameters()		,       lr=lowerCAmelCase__    )

      # Instantiate scheduler
      UpperCAmelCase     :			Any     	=get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase__		,       num_warmup_steps=1_00		,       num_training_steps=(len(lowerCAmelCase__    ) * num_epochs) // gradient_accumulation_steps		,       )

      # 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     :			List[Any]     	=accelerator.prepare(
          lowerCAmelCase__		,       lowerCAmelCase__		,       lowerCAmelCase__		,       lowerCAmelCase__		,       lowerCAmelCase__    )

      # New Code #
      # We need to keep track of how many total steps we have iterated over
      UpperCAmelCase     :			Optional[int]     	=0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCAmelCase     :			Optional[int]     	=0

      # We need to load the checkpoint back in before training here with `load_state`
      # The total number of epochs is adjusted based on where the state is being loaded from,
      # as we assume continuation of the same training script
      if args.resume_from_checkpoint:
            if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                  accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
                  accelerator.load_state(args.resume_from_checkpoint    )
                  UpperCAmelCase     :			Any     	=os.path.basename(args.resume_from_checkpoint    )
            else:
                  # Get the most recent checkpoint
                  UpperCAmelCase     :			Union[str, Any]     	=[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
                  dirs.sort(key=os.path.getctime    )
                  UpperCAmelCase     :			List[Any]     	=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            UpperCAmelCase     :			Dict     	=os.path.splitext(lowerCAmelCase__    )[0]

            if "epoch" in training_difference:
                  UpperCAmelCase     :			Any     	=int(training_difference.replace('''epoch_'''		,       ''''''    )    ) + 1
                  UpperCAmelCase     :			Dict     	=None
            else:
                  UpperCAmelCase     :			int     	=int(training_difference.replace('''step_'''		,       ''''''    )    )
                  UpperCAmelCase     :			Optional[Any]     	=resume_step // len(lowerCAmelCase__    )
                  resume_step -= starting_epoch * len(lowerCAmelCase__    )

    # Now we train the model
      for epoch in range(lowerCAmelCase__		,       lowerCAmelCase__    ):
            model.train()
            # New Code #
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                  # We need to skip steps until we reach the resumed step
                  UpperCAmelCase     :			List[Any]     	=accelerator.skip_first_batches(lowerCAmelCase__		,       lowerCAmelCase__    )
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  UpperCAmelCase     :			Dict     	=train_dataloader
            for step, batch in enumerate(lowerCAmelCase__    ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device    )
                  UpperCAmelCase     :			List[Any]     	=model(**lowerCAmelCase__    )
                  UpperCAmelCase     :			str     	=outputs.loss
                  UpperCAmelCase     :			Tuple     	=loss / gradient_accumulation_steps
                  accelerator.backward(lowerCAmelCase__    )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()
                  # New Code #
                  overall_step += 1

                  # New Code #
                  # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                  # These are saved to folders named `step_{overall_step}`
                  # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                  # If mixed precision was used, will also save a "scalar.bin" file
                  if isinstance(lowerCAmelCase__		,       lowerCAmelCase__    ):
                        UpperCAmelCase     :			Optional[int]     	=f'''step_{overall_step}'''
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    UpperCAmelCase     :			Optional[Any]     	=os.path.join(args.output_dir		,       lowerCAmelCase__    )
                              accelerator.save_state(lowerCAmelCase__    )

            model.eval()
            for step, batch in enumerate(lowerCAmelCase__    ):
                  # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                  batch.to(accelerator.device    )
                  with torch.no_grad():
                        UpperCAmelCase     :			Union[str, Any]     	=model(**lowerCAmelCase__    )
                  UpperCAmelCase     :			int     	=outputs.logits.argmax(dim=-1    )
                  UpperCAmelCase	,    UpperCAmelCase     :			str     	=accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
                  metric.add_batch(
                      predictions=lowerCAmelCase__		,       references=lowerCAmelCase__		,       )

            UpperCAmelCase     :			str     	=metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'''epoch {epoch}:'''		,       lowerCAmelCase__    )

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `epoch_{epoch}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if checkpointing_steps == "epoch":
                  UpperCAmelCase     :			List[str]     	=f'''epoch_{epoch}'''
                  if args.output_dir is not None:
                        UpperCAmelCase     :			str     	=os.path.join(args.output_dir		,       lowerCAmelCase__    )
                  accelerator.save_state(lowerCAmelCase__    )





def 	lowerCAmelCase_			(					)->	Tuple:






      '''simple docstring'''





      UpperCAmelCase     :			str     	=argparse.ArgumentParser(description='''Simple example of training script.'''    )
      parser.add_argument(
          '''--mixed_precision'''		,       type=lowerCAmelCase__		,       default=lowerCAmelCase__		,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,       help='''Whether to use mixed precision. Choose'''
          '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
          '''and an Nvidia Ampere GPU.'''		,       )
      parser.add_argument('''--cpu'''		,       action='''store_true'''		,       help='''If passed, will train on the CPU.'''    )
      parser.add_argument(
          '''--checkpointing_steps'''		,       type=lowerCAmelCase__		,       default=lowerCAmelCase__		,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''		,       )
      parser.add_argument(
          '''--output_dir'''		,       type=lowerCAmelCase__		,       default='''.'''		,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,       )
      parser.add_argument(
          '''--resume_from_checkpoint'''		,       type=lowerCAmelCase__		,       default=lowerCAmelCase__		,       help='''If the training should continue from a checkpoint folder.'''		,       )
      UpperCAmelCase     :			Any     	=parser.parse_args()
      UpperCAmelCase     :			Optional[int]     	={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(lowerCAmelCase__		,       lowerCAmelCase__    )


if __name__ == "__main__":
     main()








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__      :






		def __init__(      self							, lowercase__							, lowercase__=sys.maxsize						)				->				str:
				__A    =							"bilinear"
				__A    =							max_size
				__A    =							short_edge_length






		def __call__(      self							, lowercase__						)				->				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(lowercase__							, lowercase__						)
						if h < w:
								__A ,   __A    =							size, scale * w
						else:
								__A ,   __A    =							scale * h, size
						if max(lowercase__							, lowercase__						) > self.max_size:
								__A    =							self.max_size * 1.0 / max(lowercase__							, lowercase__						)
								__A    =							newh * scale
								__A    =							neww * scale
						__A    =							int(neww + 0.5						)
						__A    =							int(newh + 0.5						)

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

				return img_augs





class        a__      :






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				__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 lowercase__						: (x - self.pixel_mean) / self.pixel_std






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

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






		def __call__(      self							, lowercase__							, lowercase__=False						)				->				int:
				with torch.no_grad():
						if not isinstance(lowercase__							, lowercase__						):
								__A    =							[images]
						if single_image:
								assert len(lowercase__						) == 1
						for i in range(len(lowercase__						)						):
								if isinstance(images[i]							, torch.Tensor						):
										images.insert(lowercase__							, images.pop(lowercase__						).to(self.device						).float()						)
								elif not isinstance(images[i]							, torch.Tensor						):
										images.insert(
										    lowercase__							, torch.as_tensor(img_tensorize(images.pop(lowercase__						)							, 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(lowercase__						)
						# 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(lowercase__						) for x in images]
						# now pad them to do the following operations
						__A ,   __A    =							self.pad(lowercase__						)
						# Normalize

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



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		assert torch.isfinite(lowerCAmelCase__	).all(), "Box tensor contains infinite or NaN!"
		__A ,   __A    =							box_size
		tensor[:, 0].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 1].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 2].clamp_(min=0					,     max=lowerCAmelCase__	)
		tensor[:, 3].clamp_(min=0					,     max=lowerCAmelCase__	)

