



from jiwer import compute_measures

import datasets


lowerCamelCase__      =			'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

lowerCamelCase__      =			'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

lowerCamelCase__      =			'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,   _KWARGS_DESCRIPTION       )
class  SCREAMING_SNAKE_CASE      (				datasets.Metric       ):





							def     UpperCamelCase_       ( self		:		Optional[int]						):

											'''simple docstring'''
											return datasets.MetricInfo(
											    description=_DESCRIPTION   ,					citation=_CITATION   ,					inputs_description=_KWARGS_DESCRIPTION   ,					features=datasets.Features(
											        {
											            """predictions""": datasets.Value("""string"""   ,					id="""sequence"""						),
											            """references""": datasets.Value("""string"""   ,					id="""sequence"""						),
											        }						)   ,					codebase_urls=["""https://github.com/jitsi/jiwer/"""]   ,					reference_urls=[
											        """https://en.wikipedia.org/wiki/Word_error_rate""",
											    ]   ,					)







							def     UpperCamelCase_       ( self		:		List[str]   ,					__lowercase		:		Tuple=None   ,					__lowercase		:		str=None   ,					__lowercase		:		Optional[Any]=False						):

											'''simple docstring'''



											if concatenate_texts:
															return compute_measures(__a   ,					__a						)["wer"]
											else:
															__a	  =		0
															__a	  =		0
															for prediction, reference in zip(__a   ,					__a						):
																			__a	  =		compute_measures(__a   ,					__a						)
																			incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																			total += measures["substitutions"] + measures["deletions"] + measures["hits"]
															return incorrect / total





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		fname.split(os.path.sep			)[-1]
      return re.search(r"^(.*)_\d+\.jpg$" , UpperCAmelCase__			).groups()[0]





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a=None					,      __a=None     ):






           '''simple docstring'''



           lowerCamelCase             =		file_names
           lowerCamelCase             =		image_transform
           lowerCamelCase             =		label_to_id







     def __len__(self     ):






           '''simple docstring'''



           return len(self.file_names     )







     def __getitem__(self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.file_names[idx]
           lowerCamelCase             =		PIL.Image.open(__a     )
           lowerCamelCase             =		raw_image.convert("RGB"     )
           if self.image_transform is not None:
                 lowerCamelCase             =		self.image_transform(__a     )
           lowerCamelCase             =		extract_label(__a     )
           if self.label_to_id is not None:
                 lowerCamelCase             =		self.label_to_id[label]
           return {"image": image, "label": label}



def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if args.with_tracking:
            lowerCamelCase             =		Accelerator(
                cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir			)
      else:
            lowerCamelCase             =		Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision			)

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase             =		config["lr"]
      lowerCamelCase             =		int(config["num_epochs"]			)
      lowerCamelCase             =		int(config["seed"]			)
      lowerCamelCase             =		int(config["batch_size"]			)
      lowerCamelCase             =		config["image_size"]
      if not isinstance(UpperCAmelCase__ , (list, tuple)			):
            lowerCamelCase             =		(image_size, image_size)

      # 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":
                  lowerCamelCase             =		args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  lowerCamelCase             =		int(args.checkpointing_steps			)
            else:
                  raise ValueError(
                      F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
      else:
            lowerCamelCase             =		None

      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
            lowerCamelCase             =		os.path.split(UpperCAmelCase__			)[-1].split("."			)[0]
            accelerator.init_trackers(UpperCAmelCase__ , UpperCAmelCase__			)

      # Grab all the image filenames
      lowerCamelCase             =		[os.path.join(args.data_dir , UpperCAmelCase__			) for fname in os.listdir(args.data_dir			) if fname.endswith(".jpg"			)]

      # Build the label correspondences
      lowerCamelCase             =		[extract_label(UpperCAmelCase__			) for fname in file_names]
      lowerCamelCase             =		list(set(UpperCAmelCase__			)			)
      id_to_label.sort()
      lowerCamelCase             =		{lbl: i for i, lbl in enumerate(UpperCAmelCase__			)}

      # Set the seed before splitting the data.
      np.random.seed(UpperCAmelCase__			)
      torch.manual_seed(UpperCAmelCase__			)
      torch.cuda.manual_seed_all(UpperCAmelCase__			)

      # Split our filenames between train and validation
      lowerCamelCase             =		np.random.permutation(len(UpperCAmelCase__			)			)
      lowerCamelCase             =		int(0.8 * len(UpperCAmelCase__			)			)
      lowerCamelCase             =		random_perm[:cut]
      lowerCamelCase             =		random_perm[cut:]

      # For training we use a simple RandomResizedCrop
      lowerCamelCase             =		Compose([RandomResizedCrop(UpperCAmelCase__ , scale=(0.5, 1.0)			), ToTensor()]			)
      lowerCamelCase             =		PetsDataset(
          [file_names[i] for i in train_split] , image_transform=UpperCAmelCase__ , label_to_id=UpperCAmelCase__			)

      # For evaluation, we use a deterministic Resize
      lowerCamelCase             =		Compose([Resize(UpperCAmelCase__			), ToTensor()]			)
      lowerCamelCase             =		PetsDataset([file_names[i] for i in eval_split] , image_transform=UpperCAmelCase__ , label_to_id=UpperCAmelCase__			)

      # Instantiate dataloaders.
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase             =		create_model("resnet50d" , pretrained=UpperCAmelCase__ , num_classes=len(UpperCAmelCase__			)			)

      # 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).
      lowerCamelCase             =		model.to(accelerator.device			)

      # Freezing the base model
      for param in model.parameters():
            lowerCamelCase             =		False
      for param in model.get_classifier().parameters():
            lowerCamelCase             =		True

      # We normalize the batches of images to be a bit faster.
      lowerCamelCase             =		torch.tensor(model.default_cfg["mean"]			)[None, :, None, None].to(accelerator.device			)
      lowerCamelCase             =		torch.tensor(model.default_cfg["std"]			)[None, :, None, None].to(accelerator.device			)

      # Instantiate optimizer
      lowerCamelCase             =		torch.optim.Adam(params=model.parameters() , lr=lr / 25			)

      # Instantiate learning rate scheduler
      lowerCamelCase             =		OneCycleLR(optimizer=UpperCAmelCase__ , max_lr=UpperCAmelCase__ , epochs=UpperCAmelCase__ , steps_per_epoch=len(UpperCAmelCase__			)			)

      # 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             =		accelerator.prepare(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # We need to keep track of how many total steps we have iterated over
      lowerCamelCase             =		0
      # We also need to keep track of the starting epoch so files are named properly
      lowerCamelCase             =		0

      # Potentially load in the weights and states from a previous save
      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			)
                  lowerCamelCase             =		os.path.basename(args.resume_from_checkpoint			)
            else:
                  # Get the most recent checkpoint
                  lowerCamelCase             =		[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
                  dirs.sort(key=os.path.getctime			)
                  lowerCamelCase             =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            lowerCamelCase             =		os.path.splitext(UpperCAmelCase__			)[0]

            if "epoch" in training_difference:
                  lowerCamelCase             =		int(training_difference.replace("epoch_" , ""			)			) + 1
                  lowerCamelCase             =		None
            else:
                  lowerCamelCase             =		int(training_difference.replace("step_" , ""			)			)
                  lowerCamelCase             =		resume_step // len(UpperCAmelCase__			)
                  resume_step -= starting_epoch * len(UpperCAmelCase__			)

    # Now we train the model
      for epoch in range(UpperCAmelCase__ , UpperCAmelCase__			):
            model.train()
            if args.with_tracking:
                  lowerCamelCase             =		0
            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
                  lowerCamelCase             =		accelerator.skip_first_batches(UpperCAmelCase__ , UpperCAmelCase__			)
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  lowerCamelCase             =		train_dataloader
            for batch in active_dataloader:
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  lowerCamelCase             =		{k: v.to(accelerator.device			) for k, v in batch.items()}
                  lowerCamelCase             =		(batch["image"] - mean) / std
                  lowerCamelCase             =		model(UpperCAmelCase__			)
                  lowerCamelCase             =		torch.nn.functional.cross_entropy(UpperCAmelCase__ , batch["label"]			)
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  accelerator.backward(UpperCAmelCase__			)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  overall_step += 1
                  if isinstance(UpperCAmelCase__ , UpperCAmelCase__			):
                        lowerCamelCase             =		F"""step_{overall_step}"""
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    lowerCamelCase             =		os.path.join(args.output_dir , UpperCAmelCase__			)
                              accelerator.save_state(UpperCAmelCase__			)
            model.eval()
            lowerCamelCase             =		0
            lowerCamelCase             =		0
            for step, batch in enumerate(UpperCAmelCase__			):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  lowerCamelCase             =		{k: v.to(accelerator.device			) for k, v in batch.items()}
                  lowerCamelCase             =		(batch["image"] - mean) / std
                  with torch.no_grad():
                        lowerCamelCase             =		model(UpperCAmelCase__			)
                  lowerCamelCase             =		outputs.argmax(dim=-1			)
                  lowerCamelCase      ,				lowerCamelCase             =		accelerator.gather_for_metrics((predictions, batch["label"])			)
                  lowerCamelCase             =		predictions == references
                  num_elems += accurate_preds.shape[0]
                  accurate += accurate_preds.long().sum()

            lowerCamelCase             =		accurate.item() / num_elems
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""			)
            if args.with_tracking:
                  accelerator.log(
                      {
                          "accuracy": 100 * eval_metric,
                          "train_loss": total_loss.item() / len(UpperCAmelCase__			),
                          "epoch": epoch,
                      } , step=UpperCAmelCase__ , )
            if checkpointing_steps == "epoch":
                  lowerCamelCase             =		F"""epoch_{epoch}"""
                  if args.output_dir is not None:
                        lowerCamelCase             =		os.path.join(args.output_dir , UpperCAmelCase__			)
                  accelerator.save_state(UpperCAmelCase__			)

      if args.with_tracking:
            accelerator.end_training()

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		argparse.ArgumentParser(description="Simple example of training script."			)
      parser.add_argument("--data_dir" , required=UpperCAmelCase__ , help="The data folder on disk."			)
      parser.add_argument("--fp16" , action="store_true" , help="If passed, will use FP16 training."			)
      parser.add_argument(
          "--mixed_precision" , type=UpperCAmelCase__ , default=UpperCAmelCase__ , 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=UpperCAmelCase__ , default=UpperCAmelCase__ , 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=UpperCAmelCase__ , 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=UpperCAmelCase__ , default=UpperCAmelCase__ , help="If the training should continue from a checkpoint folder." , )
      parser.add_argument(
          "--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
      parser.add_argument(
          "--project_dir" , type=UpperCAmelCase__ , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
      lowerCamelCase             =		parser.parse_args()
      lowerCamelCase             =		{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(UpperCAmelCase__ , UpperCAmelCase__			)


if __name__ == "__main__":
     main()