




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




class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					int       ,					_lowerCAmelCase:					int=None       ,					_lowerCAmelCase:					int=None				):
         lowercase    :List[Any]      	=						file_names
         lowercase    :Dict      	=						image_transform
         lowercase    :str      	=						label_to_id






       def __len__(   self:					Tuple				):
         return len(self.file_names				)






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







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  # Initialize accelerator
  if args.with_tracking:
    lowercase    :Tuple      	=						Accelerator(
        cpu=args.cpu,			mixed_precision=args.mixed_precision,			log_with="all",			project_dir=args.project_dir   )
  else:
    lowercase    :str      	=						Accelerator(cpu=args.cpu,			mixed_precision=args.mixed_precision   )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase    :Union[str, Any]      	=						config["lr"]
  lowercase    :str      	=						int(config["num_epochs"]   )
  lowercase    :str      	=						int(config["seed"]   )
  lowercase    :Optional[Any]      	=						int(config["batch_size"]   )
  lowercase    :Union[str, Any]      	=						config["image_size"]
  if not isinstance(lowerCamelCase,			(list, tuple)   ):
    lowercase    :List[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":
      lowercase    :Union[str, Any]      	=						args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      lowercase    :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:
    lowercase    :Any      	=						None

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

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

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

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

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

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

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

  # Instantiate dataloaders.
  lowercase    :Optional[int]      	=						DataLoader(lowerCamelCase,			shuffle=lowerCamelCase,			batch_size=lowerCamelCase,			num_workers=4   )
  lowercase    :int      	=						DataLoader(lowerCamelCase,			shuffle=lowerCamelCase,			batch_size=lowerCamelCase,			num_workers=4   )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase    :Optional[Any]      	=						create_model("resnet50d",			pretrained=lowerCamelCase,			num_classes=len(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).
  lowercase    :int      	=						model.to(accelerator.device   )

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

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

  # Instantiate optimizer
  lowercase    :Optional[Any]      	=						torch.optim.Adam(params=model.parameters(),			lr=lr / 25   )

  # Instantiate learning rate scheduler
  lowercase    :Optional[Any]      	=						OneCycleLR(optimizer=lowerCamelCase,			max_lr=lowerCamelCase,			epochs=lowerCamelCase,			steps_per_epoch=len(lowerCamelCase   )   )

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

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

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

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

  if args.with_tracking:
    accelerator.end_training()



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


if __name__ == "__main__":
       main()









import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset

_UpperCAmelCase	:						List[str]			=     random.Random()



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=1.0,			lowerCamelCase=None,			lowerCamelCase=None   ):
  if rng is None:
    lowercase    :Union[str, Any]      	=						global_rng

  lowercase    :Any      	=						[]
  for batch_idx in range(shape[0]   ):
    values.append([]   )
    for _ in range(shape[1]   ):
      values[-1].append(rng.random() * scale   )

  return values




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Union[str, Any]=7       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					List[str]=20_00       ,					_lowerCAmelCase:					Dict=20_48       ,					_lowerCAmelCase:					Any=1_28       ,					_lowerCAmelCase:					Any=1       ,					_lowerCAmelCase:					List[Any]=5_12       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					List[Any]=4_41_00       ,					):
         lowercase    :Any      	=						parent
         lowercase    :Any      	=						batch_size
         lowercase    :Optional[int]      	=						min_seq_length
         lowercase    :Optional[Any]      	=						max_seq_length
         lowercase    :Dict      	=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase    :List[str]      	=						spectrogram_length
         lowercase    :int      	=						feature_size
         lowercase    :Union[str, Any]      	=						num_audio_channels
         lowercase    :Optional[int]      	=						hop_length
         lowercase    :str      	=						chunk_length
         lowercase    :List[str]      	=						sampling_rate






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					int=False       ,					_lowerCAmelCase:					int=False				):
         def _flatten(_lowerCAmelCase:					Optional[Any]				):
           return list(itertools.chain(*_lowerCAmelCase				)				)

         if equal_length:
           lowercase    :Any      	=						[floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
         else:
           # make sure that inputs increase in size
           lowercase    :Tuple      	=						[
               floats_list((x, self.feature_size)				)
               for x in range(self.min_seq_length       ,					self.max_seq_length       ,					self.seq_length_diff				)
           ]
         if numpify:
           lowercase    :Optional[int]      	=						[np.asarray(_lowerCAmelCase				) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						TvltFeatureExtractor






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Dict      	=						TvltFeatureExtractionTester(self				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"spectrogram_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"feature_size"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"num_audio_channels"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"hop_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"chunk_length"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"sampling_rate"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Any      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Tuple      	=						feat_extract_first.save_pretrained(_lowerCAmelCase				)[0]
           check_json_file_has_correct_format(_lowerCAmelCase				)
           lowercase    :Tuple      	=						self.feature_extraction_class.from_pretrained(_lowerCAmelCase				)

         lowercase    :Optional[Any]      	=						feat_extract_first.to_dict()
         lowercase    :List[str]      	=						feat_extract_second.to_dict()
         lowercase    :Any      	=						dict_first.pop("mel_filters"				)
         lowercase    :Any      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Union[str, Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Any      	=						os.path.join(_lowerCAmelCase       ,					"feat_extract.json"				)
           feat_extract_first.to_json_file(_lowerCAmelCase				)
           lowercase    :List[Any]      	=						self.feature_extraction_class.from_json_file(_lowerCAmelCase				)

         lowercase    :Tuple      	=						feat_extract_first.to_dict()
         lowercase    :int      	=						feat_extract_second.to_dict()
         lowercase    :Tuple      	=						dict_first.pop("mel_filters"				)
         lowercase    :Tuple      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize feature_extractor
         lowercase    :int      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         # create three inputs of length 800, 1000, and 1200
         lowercase    :List[Any]      	=						[floats_list((1, x)				)[0] for x in range(8_00       ,					14_00       ,					2_00				)]
         lowercase    :Union[str, Any]      	=						[np.asarray(_lowerCAmelCase				) for speech_input in speech_inputs]

         # Test not batched input
         lowercase    :Any      	=						feature_extractor(np_speech_inputs[0]       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test batched
         lowercase    :Optional[int]      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test audio masking
         lowercase    :Dict      	=						feature_extractor(
             _lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00       ,					mask_audio=_lowerCAmelCase				).audio_values

         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

         # Test 2-D numpy arrays are batched.
         lowercase    :Optional[int]      	=						[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
         lowercase    :Union[str, Any]      	=						np.asarray(_lowerCAmelCase				)
         lowercase    :Dict      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="np"       ,					sampling_rate=4_41_00				).audio_values
         self.assertTrue(encoded_audios.ndim == 4				)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :str      	=						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,					"clean"       ,					split="validation"				)
         # automatic decoding with librispeech
         lowercase    :Any      	=						ds.sort("id"				).select(range(_lowerCAmelCase				)				)[:num_samples]["audio"]

         return [x["array"] for x in speech_samples]






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Optional[int]      	=						self._load_datasamples(1				)
         lowercase    :Union[str, Any]      	=						TvltFeatureExtractor()
         lowercase    :str      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="pt"				).audio_values

         self.assertEquals(audio_values.shape       ,					(1, 1, 1_92, 1_28)				)

         lowercase    :Optional[int]      	=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,					_lowerCAmelCase       ,					atol=1e-4				)				)



