


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  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







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


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


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






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							file_names
         lowerCAmelCase					:					Optional[Any]								=							image_transform
         lowerCAmelCase					:					List[Any]								=							label_to_id

       def __len__(						self						):






         """simple docstring"""





         return len(self.file_names						)







       def __getitem__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							self.file_names[idx]
         lowerCAmelCase					:					int								=							PIL.Image.open(__A						)
         lowerCAmelCase					:					int								=							raw_image.convert("RGB"						)
         if self.image_transform is not None:
           lowerCAmelCase					:					List[str]								=							self.image_transform(__A						)
         lowerCAmelCase					:					Optional[Any]								=							extract_label(__A						)
         if self.label_to_id is not None:
           lowerCAmelCase					:					Optional[Any]								=							self.label_to_id[label]
         return {"image": image, "label": label}

def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  if args.with_tracking:
    lowerCAmelCase					:					Optional[Any]								=							Accelerator(
        cpu=args.cpu    ,				mixed_precision=args.mixed_precision    ,				log_with="all"    ,				project_dir=args.project_dir    )
  else:
    lowerCAmelCase					:					int								=							Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision    )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase					:					List[Any]								=							config['''lr''']
  lowerCAmelCase					:					Optional[int]								=							int(config["num_epochs"]    )
  lowerCAmelCase					:					List[Any]								=							int(config["seed"]    )
  lowerCAmelCase					:					Union[str, Any]								=							int(config["batch_size"]    )
  lowerCAmelCase					:					Optional[Any]								=							config['''image_size''']
  if not isinstance(SCREAMING_SNAKE_CASE    ,				(list, tuple)    ):
    lowerCAmelCase					:					str								=							(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					:					Dict								=							args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      lowerCAmelCase					:					Dict								=							int(args.checkpointing_steps    )
    else:
      raise ValueError(
          f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""    )
  else:
    lowerCAmelCase					:					Tuple								=							None

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

  # Grab all the image filenames
  lowerCAmelCase					:					List[str]								=							[os.path.join(args.data_dir    ,				SCREAMING_SNAKE_CASE    ) for fname in os.listdir(args.data_dir    ) if fname.endswith(".jpg"    )]

  # Build the label correspondences
  lowerCAmelCase					:					Union[str, Any]								=							[extract_label(SCREAMING_SNAKE_CASE    ) for fname in file_names]
  lowerCAmelCase					:					Tuple								=							list(set(SCREAMING_SNAKE_CASE    )    )
  id_to_label.sort()
  lowerCAmelCase					:					Optional[int]								=							{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE    )}

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

  # Split our filenames between train and validation
  lowerCAmelCase					:					Optional[Any]								=							np.random.permutation(len(SCREAMING_SNAKE_CASE    )    )
  lowerCAmelCase					:					int								=							int(0.8 * len(SCREAMING_SNAKE_CASE    )    )
  lowerCAmelCase					:					int								=							random_perm[:cut]
  lowerCAmelCase					:					str								=							random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  lowerCAmelCase					:					Union[str, Any]								=							Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE    ,				scale=(0.5, 1.0)    ), ToTensor()]    )
  lowerCAmelCase					:					Optional[Any]								=							PetsDataset(
      [file_names[i] for i in train_split]    ,				image_transform=SCREAMING_SNAKE_CASE    ,				label_to_id=SCREAMING_SNAKE_CASE    )

  # For evaluation, we use a deterministic Resize
  lowerCAmelCase					:					Optional[int]								=							Compose([Resize(SCREAMING_SNAKE_CASE    ), ToTensor()]    )
  lowerCAmelCase					:					Any								=							PetsDataset([file_names[i] for i in eval_split]    ,				image_transform=SCREAMING_SNAKE_CASE    ,				label_to_id=SCREAMING_SNAKE_CASE    )

  # Instantiate dataloaders.
  lowerCAmelCase					:					List[str]								=							DataLoader(SCREAMING_SNAKE_CASE    ,				shuffle=SCREAMING_SNAKE_CASE    ,				batch_size=SCREAMING_SNAKE_CASE    ,				num_workers=4    )
  lowerCAmelCase					:					List[str]								=							DataLoader(SCREAMING_SNAKE_CASE    ,				shuffle=SCREAMING_SNAKE_CASE    ,				batch_size=SCREAMING_SNAKE_CASE    ,				num_workers=4    )

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

  # 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					:					Optional[Any]								=							model.to(accelerator.device    )

  # Freezing the base model
  for param in model.parameters():
    lowerCAmelCase					:					Optional[int]								=							False
  for param in model.get_classifier().parameters():
    lowerCAmelCase					:					List[str]								=							True

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

  # Instantiate optimizer
  lowerCAmelCase					:					List[Any]								=							torch.optim.Adam(params=model.parameters()    ,				lr=lr / 2_5    )

  # Instantiate learning rate scheduler
  lowerCAmelCase					:					Dict								=							OneCycleLR(optimizer=SCREAMING_SNAKE_CASE    ,				max_lr=SCREAMING_SNAKE_CASE    ,				epochs=SCREAMING_SNAKE_CASE    ,				steps_per_epoch=len(SCREAMING_SNAKE_CASE    )    )

  # 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					:					Union[str, Any]								=							accelerator.prepare(
      SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  # We need to keep track of how many total steps we have iterated over
  lowerCAmelCase					:					List[Any]								=							0
  # We also need to keep track of the starting epoch so files are named properly
  lowerCAmelCase					:					Union[str, Any]								=							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					:					Tuple								=							os.path.basename(args.resume_from_checkpoint    )
    else:
      # Get the most recent checkpoint
      lowerCAmelCase					:					List[Any]								=							[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
      dirs.sort(key=os.path.getctime    )
      lowerCAmelCase					:					Any								=							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    lowerCAmelCase					:					Any								=							os.path.splitext(SCREAMING_SNAKE_CASE    )[0]

    if "epoch" in training_difference:
      lowerCAmelCase					:					Dict								=							int(training_difference.replace("epoch_"    ,				""    )    ) + 1
      lowerCAmelCase					:					Union[str, Any]								=							None
    else:
      lowerCAmelCase					:					Optional[int]								=							int(training_difference.replace("step_"    ,				""    )    )
      lowerCAmelCase					:					List[Any]								=							resume_step // len(SCREAMING_SNAKE_CASE    )
      resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE    )

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

    lowerCAmelCase					:					str								=							accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}: {1_0_0 * eval_metric:.2f}"""    )
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": 1_0_0 * eval_metric,
              "train_loss": total_loss.item() / len(SCREAMING_SNAKE_CASE    ),
              "epoch": epoch,
          }    ,				step=SCREAMING_SNAKE_CASE    ,				)
    if checkpointing_steps == "epoch":
      lowerCAmelCase					:					Union[str, Any]								=							f"""epoch_{epoch}"""
      if args.output_dir is not None:
        lowerCAmelCase					:					str								=							os.path.join(args.output_dir    ,				SCREAMING_SNAKE_CASE    )
      accelerator.save_state(SCREAMING_SNAKE_CASE    )

  if args.with_tracking:
    accelerator.end_training()
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							argparse.ArgumentParser(description="Simple example of training script."    )
  parser.add_argument("--data_dir"    ,				required=SCREAMING_SNAKE_CASE    ,				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=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				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=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				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=SCREAMING_SNAKE_CASE    ,				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=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				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=SCREAMING_SNAKE_CASE    ,				default="logs"    ,				help="Location on where to store experiment tracking logs` and relevent project information"    ,				)
  lowerCAmelCase					:					Dict								=							parser.parse_args()
  lowerCAmelCase					:					Union[str, Any]								=							{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
  training_function(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
     main()









"""simple docstring"""







from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
     import importlib_metadata
else:
     import importlib.metadata as importlib_metadata


lowerCAmelCase__				    =       ''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):


     class 				SCREAMING_SNAKE_CASE__    (		tr.AbstractTransform    ):
            """simple docstring"""


            def __init__(						self		,					snake_case__ = " "						):






              """simple docstring"""





              lowerCAmelCase					:					List[Any]								=							sentence_delimiter

            def 	lowercase__							(						self		,					snake_case__						):






              """simple docstring"""





              return list(snake_case__						)







            def 	lowercase__							(						self		,					snake_case__						):






              """simple docstring"""





              lowerCAmelCase					:					Any								=							[]
              for sent_idx, sentence in enumerate(snake_case__						):
                chars.extend(self.process_string(snake_case__						)						)
                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case__						) - 1:
                  chars.append(self.sentence_delimiter						)
              return chars

     lowerCAmelCase__				    =       tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
else:
     lowerCAmelCase__				    =       tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )


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

lowerCAmelCase__				    =       '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

lowerCAmelCase__				    =       '''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,   _KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__    (		datasets.Metric    ):
       """simple docstring"""


       def 	lowercase__							(						self						):






         """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",
                 "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
             ]		,					)







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__=False						):






         """simple docstring"""





         if concatenate_texts:
           return jiwer.compute_measures(
               snake_case__		,					snake_case__		,					truth_transform=snake_case__		,					hypothesis_transform=snake_case__		,					)["wer"]

         lowerCAmelCase					:					List[Any]								=							0
         lowerCAmelCase					:					Optional[Any]								=							0
         for prediction, reference in zip(snake_case__		,					snake_case__						):
           lowerCAmelCase					:					Optional[int]								=							jiwer.compute_measures(
               snake_case__		,					snake_case__		,					truth_transform=snake_case__		,					hypothesis_transform=snake_case__		,					)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

         return incorrect / total





