

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 (     A__      :  Optional[int]	)       -> Any:





      """simple docstring"""



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





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase=None   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=file_names
        _lowercase      			=image_transform
        _lowercase      			=label_to_id




  def __len__(							self   )    ->					List[str]:
        '''simple docstring'''






        return len(self.file_names   )




  def __getitem__(							self				,  lowerCAmelCase   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=self.file_names[idx]
        _lowercase      			=PIL.Image.open(lowerCAmelCase   )
        _lowercase      			=raw_image.convert('RGB'   )
        if self.image_transform is not None:
              _lowercase      			=self.image_transform(lowerCAmelCase   )
        _lowercase      			=extract_label(lowerCAmelCase   )
        if self.label_to_id is not None:
              _lowercase      			=self.label_to_id[label]
        return {"image": image, "label": label}
def 	a (     A__      :  int ,  A__      :  Tuple	)       -> Tuple:





      """simple docstring"""



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

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

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

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

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

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

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

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

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

      # Instantiate dataloaders.
      _lowercase      			=DataLoader(A__ ,  shuffle=A__ ,  batch_size=A__ ,  num_workers=4	)
      _lowercase      			=DataLoader(A__ ,  shuffle=A__ ,  batch_size=A__ ,  num_workers=4	)

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

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

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

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

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

      # Instantiate learning rate scheduler
      _lowercase      			=OneCycleLR(optimizer=A__ ,  max_lr=A__ ,  epochs=A__ ,  steps_per_epoch=len(A__	)	)

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

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

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

            _lowercase      			=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(A__	),
                          'epoch': epoch,
                      } ,  step=A__ ,  )
            if checkpointing_steps == "epoch":
                  _lowercase      			=F'''epoch_{epoch}'''
                  if args.output_dir is not None:
                        _lowercase      			=os.path.join(args.output_dir ,  A__	)
                  accelerator.save_state(A__	)

      if args.with_tracking:
            accelerator.end_training()
def 	a (     )       -> List[str]:





      """simple docstring"""



      _lowercase      			=argparse.ArgumentParser(description='Simple example of training script.'	)
      parser.add_argument('--data_dir' ,  required=A__ ,  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=A__ ,  default=A__ ,  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=A__ ,  default=A__ ,  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=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(
          '--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=A__ ,  default='logs' ,  help='Location on where to store experiment tracking logs` and relevent project information' ,  )
      _lowercase      			=parser.parse_args()
      _lowercase      			={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
      training_function(A__ ,  A__	)


if __name__ == "__main__":
  main()





from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowercase_   							=  logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE				)
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  **lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        if self.framework != "pt":
              raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''   )
        # No specific FOR_XXX available yet




  def __call__(							self				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        return super().__call__(lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			={}
        if "candidate_labels" in kwargs:
              _lowercase      			=kwargs['candidate_labels']
        if "hypothesis_template" in kwargs:
              _lowercase      			=kwargs['hypothesis_template']

        return preprocess_params, {}, {}




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase="This is a sound of {}."   )    ->					Tuple:
        '''simple docstring'''






        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              if audio.startswith('http://'   ) or audio.startswith('https://'   ):
                    # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                    # like http_huggingface_co.png
                    _lowercase      			=requests.get(lowerCAmelCase   ).content
              else:
                    with open(lowerCAmelCase				,  'rb'   ) as f:
                          _lowercase      			=f.read()

        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=ffmpeg_read(lowerCAmelCase				,  self.feature_extractor.sampling_rate   )

        if not isinstance(lowerCAmelCase				,  np.ndarray   ):
              raise ValueError('We expect a numpy ndarray as input'   )
        if len(audio.shape   ) != 1:
              raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'   )

        _lowercase      			=self.feature_extractor(
            [audio]				,  sampling_rate=self.feature_extractor.sampling_rate				,  return_tensors='pt'   )
        _lowercase      			=candidate_labels
        _lowercase      			=[hypothesis_template.format(lowerCAmelCase   ) for x in candidate_labels]
        _lowercase      			=self.tokenizer(lowerCAmelCase				,  return_tensors=self.framework				,  padding=lowerCAmelCase   )
        _lowercase      			=[text_inputs]
        return inputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=model_inputs.pop('candidate_labels'   )
        _lowercase      			=model_inputs.pop('text_inputs'   )
        if isinstance(text_inputs[0]				,  lowerCAmelCase   ):
              _lowercase      			=text_inputs[0]
        else:
              # Batching case.
              _lowercase      			=text_inputs[0][0]

        _lowercase      			=self.model(**lowerCAmelCase				,  **lowerCAmelCase   )

        _lowercase      			={
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_audio,
        }
        return model_outputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=model_outputs.pop('candidate_labels'   )
        _lowercase      			=model_outputs['logits'][0]

        if self.framework == "pt":
              _lowercase      			=logits.softmax(dim=0   )
              _lowercase      			=probs.tolist()
        else:
              raise ValueError('`tf` framework not supported.'   )

        _lowercase      			=[
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCAmelCase				,  lowerCAmelCase   )				,  key=lambda lowerCAmelCase   : -x[0]   )
        ]
        return result


