'''simple docstring'''
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a=  1_6
a=  3_2







def  _UpperCamelCase (     _a	:	Accelerator				,     _a	:	int = 1_6				,     _a	:	str = "bert-base-cased"			):




    """simple docstring"""



    __UpperCamelCase	:	Dict		    =  AutoTokenizer.from_pretrained(_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  load_dataset('glue'				,     'mrpc'			)

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

    def collate_fn(_a	:	Dict			):
        # 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_2_8				,     return_tensors='pt'			)
        return tokenizer.pad(_a				,     padding='longest'				,     return_tensors='pt'			)

    # Instantiate dataloaders.
    __UpperCamelCase	:	List[Any]		    =  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  _UpperCamelCase (     _a	:	Any				,     _a	:	str				,     _a	:	Optional[int]				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    model.eval()
    __UpperCamelCase	:	List[str]		    =  0
    for step, batch in enumerate(_a			):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device			)
        with torch.no_grad():
            __UpperCamelCase	:	str		    =  model(**_a			)
        __UpperCamelCase	:	Any		    =  outputs.logits.argmax(dim=-1			)
        # It is slightly faster to call this once, than multiple times
        __UpperCamelCase	:	str		    =  accelerator.gather(
            (predictions, batch['labels'])			)  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_a			) - 1:
                __UpperCamelCase	:	Dict		    =  predictions[: len(eval_dataloader.dataset			) - samples_seen]
                __UpperCamelCase	:	int		    =  references[: len(eval_dataloader.dataset			) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_a				,     references=_a				,     )

    __UpperCamelCase	:	str		    =  metric.compute()
    return eval_metric["accuracy"]







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	int			):




    """simple docstring"""



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

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

    set_seed(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  get_dataloaders(_a				,     _a				,     _a			)

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

    # Instantiate optimizer
    __UpperCamelCase	:	Optional[Any]		    =  (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    __UpperCamelCase	:	List[Any]		    =  optimizer_cls(params=model.parameters()				,     lr=_a			)

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

    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase	:	Dict		    =  0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase	:	int		    =  0
    __UpperCamelCase	:	Tuple		    =  evaluate.load('glue'				,     'mrpc'			)
    __UpperCamelCase	:	Union[str, Any]		    =  num_epochs

    if args.partial_train_epoch is not None:
        __UpperCamelCase	:	Optional[Any]		    =  args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint			)
        __UpperCamelCase	:	str		    =  args.resume_from_checkpoint.split('epoch_'			)[1]
        __UpperCamelCase	:	Any		    =  ''
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        __UpperCamelCase	:	Any		    =  int(_a			) + 1
        __UpperCamelCase	:	Optional[Any]		    =  evaluation_loop(_a				,     _a				,     _a				,     _a			)
        accelerator.print('resumed checkpoint performance:'				,     _a			)
        accelerator.print('resumed checkpoint\'s scheduler\'s lr:'				,     lr_scheduler.get_lr()[0]			)
        accelerator.print('resumed optimizers\'s lr:'				,     optimizer.param_groups[0]['lr']			)
        with open(os.path.join(args.output_dir				,     f"""state_{starting_epoch-1}.json"""			)				,     'r'			) as f:
            __UpperCamelCase	:	str		    =  json.load(_a			)
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    __UpperCamelCase	:	int		    =  {}
    for epoch in range(_a				,     _a			):
        model.train()
        for step, batch in enumerate(_a			):
            __UpperCamelCase	:	Tuple		    =  model(**_a			)
            __UpperCamelCase	:	Any		    =  outputs.loss
            __UpperCamelCase	:	int		    =  loss / gradient_accumulation_steps
            accelerator.backward(_a			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        __UpperCamelCase	:	Optional[Any]		    =  f"""epoch_{epoch}"""
        __UpperCamelCase	:	Optional[Any]		    =  os.path.join(args.output_dir				,     _a			)
        accelerator.save_state(_a			)
        __UpperCamelCase	:	Optional[Any]		    =  evaluation_loop(_a				,     _a				,     _a				,     _a			)
        __UpperCamelCase	:	Union[str, Any]		    =  accuracy
        __UpperCamelCase	:	Tuple		    =  lr_scheduler.get_lr()[0]
        __UpperCamelCase	:	Any		    =  optimizer.param_groups[0]['lr']
        __UpperCamelCase	:	Tuple		    =  epoch
        __UpperCamelCase	:	Tuple		    =  overall_step
        accelerator.print(f"""epoch {epoch}:"""				,     _a			)

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir				,     f"""state_{epoch}.json"""			)				,     'w'			) as f:
                json.dump(_a				,     _a			)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  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(
        '--resume_from_checkpoint'				,     type=_a				,     default=_a				,     help='If the training should continue from a checkpoint folder.'				,     )
    parser.add_argument(
        '--partial_train_epoch'				,     type=_a				,     default=_a				,     help='If passed, the training will stop after this number of epochs.'				,     )
    parser.add_argument(
        '--num_epochs'				,     type=_a				,     default=2				,     help='Number of train epochs.'				,     )
    __UpperCamelCase	:	Any		    =  parser.parse_args()
    __UpperCamelCase	:	Optional[int]		    =  {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

    training_function(_a				,     _a			)


if __name__ == "__main__":
  main()




'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
a=  ''''''
a=  ''''''
a=  ''''''
a=  1  # (0 is vertical, 1 is horizontal)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  get_dataset(_a				,     _a			)
    print('Processing...'			)
    __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  update_image_and_anno(_a				,     _a				,     _a			)

    for index, image in enumerate(_a			):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __UpperCamelCase	:	Tuple		    =  random_chars(3_2			)
        __UpperCamelCase	:	Tuple		    =  paths[index].split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        __UpperCamelCase	:	List[str]		    =  f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
        cva.imwrite(f"""/{file_root}.jpg"""				,     _a				,     [cva.IMWRITE_JPEG_QUALITY, 8_5]			)
        print(f"""Success {index+1}/{len(_a			)} with {file_name}"""			)
        __UpperCamelCase	:	Tuple		    =  []
        for anno in new_annos[index]:
            __UpperCamelCase	:	Any		    =  f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
            annos_list.append(_a			)
        with open(f"""/{file_root}.txt"""				,     'w'			) as outfile:
            outfile.write('\n'.join(line for line in annos_list			)			)







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	Union[str, Any]		    =  []
    for label_file in glob.glob(os.path.join(_a				,     '*.txt'			)			):
        __UpperCamelCase	:	Any		    =  label_file.split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        with open(_a			) as in_file:
            __UpperCamelCase	:	Tuple		    =  in_file.readlines()
        __UpperCamelCase	:	Dict		    =  os.path.join(_a				,     f"""{label_name}.jpg"""			)

        __UpperCamelCase	:	Optional[Any]		    =  []
        for obj_list in obj_lists:
            __UpperCamelCase	:	Optional[Any]		    =  obj_list.rstrip('\n'			).split(' '			)
            boxes.append(
                [
                    int(obj[0]			),
                    float(obj[1]			),
                    float(obj[2]			),
                    float(obj[3]			),
                    float(obj[4]			),
                ]			)
        if not boxes:
            continue
        img_paths.append(_a			)
        labels.append(_a			)
    return img_paths, labels







def  _UpperCamelCase (     _a	:	list				,     _a	:	list				,     _a	:	int = 1			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	str		    =  []
    __UpperCamelCase	:	str		    =  []
    for idx in range(len(_a			)			):
        __UpperCamelCase	:	Union[str, Any]		    =  []
        __UpperCamelCase	:	List[str]		    =  img_list[idx]
        path_list.append(_a			)
        __UpperCamelCase	:	Dict		    =  anno_list[idx]
        __UpperCamelCase	:	List[str]		    =  cva.imread(_a			)
        if flip_type == 1:
            __UpperCamelCase	:	List[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
        elif flip_type == 0:
            __UpperCamelCase	:	Optional[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[2]
                new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
        new_annos_lists.append(_a			)
        new_imgs_list.append(_a			)
    return new_imgs_list, new_annos_lists, path_list







def  _UpperCamelCase (     _a	:	int = 3_2			):




    """simple docstring"""



    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase	:	Optional[Any]		    =  ascii_lowercase + digits
    return "".join(random.choice(_a			) for _ in range(_a			)			)


if __name__ == "__main__":
  main()
  print('''DONE ✅''')



