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

class 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=None					,	lowerCamelCase__=None   ):
       lowerCAmelCase_:       List[str]         =  file_names
       lowerCAmelCase_:       Dict         =  image_transform
       lowerCAmelCase_:       Optional[Any]         =  label_to_id




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




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





def 		snake_case__  (		lowercase       ,							lowercase				):
   # Initialize accelerator
   lowerCAmelCase_:       Dict         =  Accelerator(cpu=args.cpu       ,							mixed_precision=args.mixed_precision				)

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase_:       str         =  config["lr"]
   lowerCAmelCase_:       Union[str, Any]         =  int(config["num_epochs"]				)
   lowerCAmelCase_:       Any         =  int(config["seed"]				)
   lowerCAmelCase_:       Dict         =  int(config["batch_size"]				)
   lowerCAmelCase_:       Any         =  config["image_size"]
   if not isinstance(lowercase       ,							(list, tuple)				):
      lowerCAmelCase_:       Tuple         =  (image_size, image_size)

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

   # Build the label correspondences
   lowerCAmelCase_:       List[str]         =  [extract_label(lowercase				) for fname in file_names]
   lowerCAmelCase_:       List[Any]         =  list(set(lowercase				)				)
   id_to_label.sort()
   lowerCAmelCase_:       Tuple         =  {lbl: i for i, lbl in enumerate(lowercase				)}

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

   # Split our filenames between train and validation
   lowerCAmelCase_:       Union[str, Any]         =  np.random.permutation(len(lowercase				)				)
   lowerCAmelCase_:       int         =  int(0.8 * len(lowercase				)				)
   lowerCAmelCase_:       Tuple         =  random_perm[:cut]
   lowerCAmelCase_:       Any         =  random_perm[cut:]

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

   # For evaluation, we use a deterministic Resize
   lowerCAmelCase_:       Union[str, Any]         =  Compose([Resize(lowercase				), ToTensor()]				)
   lowerCAmelCase_:       Dict         =  PetsDataset([file_names[i] for i in eval_split]       ,							image_transform=lowercase       ,							label_to_id=lowercase				)

   # Instantiate dataloaders.
   lowerCAmelCase_:       List[Any]         =  DataLoader(lowercase       ,							shuffle=lowercase       ,							batch_size=lowercase       ,							num_workers=4				)
   lowerCAmelCase_:       Dict         =  DataLoader(lowercase       ,							shuffle=lowercase       ,							batch_size=lowercase       ,							num_workers=4				)

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

   # 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_:       Union[str, Any]         =  model.to(accelerator.device				)

   # Freezing the base model
   for param in model.parameters():
      lowerCAmelCase_:       Optional[Any]         =  False
   for param in model.get_classifier().parameters():
      lowerCAmelCase_:       Optional[Any]         =  True

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

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

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

   # 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_:       Dict         =  accelerator.prepare(
       lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							lowercase				)

   # Now we train the model
   for epoch in range(lowercase				):
      model.train()
      for step, batch in enumerate(lowercase				):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         lowerCAmelCase_:       str         =  {k: v.to(accelerator.device				) for k, v in batch.items()}
         lowerCAmelCase_:       int         =  (batch["image"] - mean) / std
         lowerCAmelCase_:       str         =  model(lowercase				)
         lowerCAmelCase_:       int         =  torch.nn.functional.cross_entropy(lowercase       ,							batch["label"]				)
         accelerator.backward(lowercase				)
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()

      model.eval()
      lowerCAmelCase_:       Tuple         =  0
      lowerCAmelCase_:       Optional[int]         =  0
      for _, batch in enumerate(lowercase				):
         # 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_:       Any         =  (batch["image"] - mean) / std
         with torch.no_grad():
            lowerCAmelCase_:       Optional[int]         =  model(lowercase				)
         lowerCAmelCase_:       List[str]         =  outputs.argmax(dim=-1				)
         lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  accelerator.gather_for_metrics((predictions, batch["label"])				)
         lowerCAmelCase_:       List[str]         =  predictions == references
         num_elems += accurate_preds.shape[0]
         accurate += accurate_preds.long().sum()

      lowerCAmelCase_:       int         =  accurate.item() / num_elems
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''				)
def 		snake_case__  (		):
   lowerCAmelCase_:       Any         =  argparse.ArgumentParser(description="Simple example of training script."				)
   parser.add_argument("--data_dir"       ,							required=lowercase       ,							help="The data folder on disk."				)
   parser.add_argument(
       "--mixed_precision"       ,							type=lowercase       ,							default=lowercase       ,							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(
       "--checkpointing_steps"       ,							type=lowercase       ,							default=lowercase       ,							help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."       ,							)
   parser.add_argument("--cpu"       ,							action="store_true"       ,							help="If passed, will train on the CPU."				)
   lowerCAmelCase_:       Optional[int]         =  parser.parse_args()
   lowerCAmelCase_:       Union[str, Any]         =  {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
   training_function(lowercase       ,							lowercase				)


if __name__ == "__main__":
 main()
from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


a     :			Optional[Any]					    =						{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Union[str, Any]					    =						[
     """Constraint""",
     """ConstraintListState""",
     """DisjunctiveConstraint""",
     """PhrasalConstraint""",
 ]
 a     :			Optional[int]					    =						[
     """BeamHypotheses""",
     """BeamScorer""",
     """BeamSearchScorer""",
     """ConstrainedBeamSearchScorer""",
 ]
 a     :			Any					    =						[
     """EpsilonLogitsWarper""",
     """EtaLogitsWarper""",
     """ForcedBOSTokenLogitsProcessor""",
     """ForcedEOSTokenLogitsProcessor""",
     """HammingDiversityLogitsProcessor""",
     """InfNanRemoveLogitsProcessor""",
     """LogitsProcessor""",
     """LogitsProcessorList""",
     """LogitsWarper""",
     """MinLengthLogitsProcessor""",
     """MinNewTokensLengthLogitsProcessor""",
     """NoBadWordsLogitsProcessor""",
     """NoRepeatNGramLogitsProcessor""",
     """PrefixConstrainedLogitsProcessor""",
     """RepetitionPenaltyLogitsProcessor""",
     """SequenceBiasLogitsProcessor""",
     """EncoderRepetitionPenaltyLogitsProcessor""",
     """TemperatureLogitsWarper""",
     """TopKLogitsWarper""",
     """TopPLogitsWarper""",
     """TypicalLogitsWarper""",
     """EncoderNoRepeatNGramLogitsProcessor""",
     """ExponentialDecayLengthPenalty""",
     """LogitNormalization""",
 ]
 a     :			int					    =						[
     """MaxNewTokensCriteria""",
     """MaxLengthCriteria""",
     """MaxTimeCriteria""",
     """StoppingCriteria""",
     """StoppingCriteriaList""",
     """validate_stopping_criteria""",
 ]
 a     :			Dict					    =						[
     """GenerationMixin""",
     """top_k_top_p_filtering""",
     """GreedySearchEncoderDecoderOutput""",
     """GreedySearchDecoderOnlyOutput""",
     """SampleEncoderDecoderOutput""",
     """SampleDecoderOnlyOutput""",
     """BeamSearchEncoderDecoderOutput""",
     """BeamSearchDecoderOnlyOutput""",
     """BeamSampleEncoderDecoderOutput""",
     """BeamSampleDecoderOnlyOutput""",
     """ContrastiveSearchEncoderDecoderOutput""",
     """ContrastiveSearchDecoderOnlyOutput""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Optional[Any]					    =						[
     """TFForcedBOSTokenLogitsProcessor""",
     """TFForcedEOSTokenLogitsProcessor""",
     """TFLogitsProcessor""",
     """TFLogitsProcessorList""",
     """TFLogitsWarper""",
     """TFMinLengthLogitsProcessor""",
     """TFNoBadWordsLogitsProcessor""",
     """TFNoRepeatNGramLogitsProcessor""",
     """TFRepetitionPenaltyLogitsProcessor""",
     """TFTemperatureLogitsWarper""",
     """TFTopKLogitsWarper""",
     """TFTopPLogitsWarper""",
     """TFForceTokensLogitsProcessor""",
     """TFSuppressTokensAtBeginLogitsProcessor""",
     """TFSuppressTokensLogitsProcessor""",
 ]
 a     :			Any					    =						[
     """TFGenerationMixin""",
     """tf_top_k_top_p_filtering""",
     """TFGreedySearchDecoderOnlyOutput""",
     """TFGreedySearchEncoderDecoderOutput""",
     """TFSampleEncoderDecoderOutput""",
     """TFSampleDecoderOnlyOutput""",
     """TFBeamSearchEncoderDecoderOutput""",
     """TFBeamSearchDecoderOnlyOutput""",
     """TFBeamSampleEncoderDecoderOutput""",
     """TFBeamSampleDecoderOnlyOutput""",
     """TFContrastiveSearchEncoderDecoderOutput""",
     """TFContrastiveSearchDecoderOnlyOutput""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Union[str, Any]					    =						[
     """FlaxForcedBOSTokenLogitsProcessor""",
     """FlaxForcedEOSTokenLogitsProcessor""",
     """FlaxLogitsProcessor""",
     """FlaxLogitsProcessorList""",
     """FlaxLogitsWarper""",
     """FlaxMinLengthLogitsProcessor""",
     """FlaxTemperatureLogitsWarper""",
     """FlaxTopKLogitsWarper""",
     """FlaxTopPLogitsWarper""",
 ]
 a     :			Dict					    =						[
     """FlaxGenerationMixin""",
     """FlaxGreedySearchOutput""",
     """FlaxSampleOutput""",
     """FlaxBeamSearchOutput""",
 ]

if TYPE_CHECKING:
 from .configuration_utils import GenerationConfig
 from .streamers import TextIteratorStreamer, TextStreamer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
  from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  from .logits_process import (
      EncoderNoRepeatNGramLogitsProcessor,
      EncoderRepetitionPenaltyLogitsProcessor,
      EpsilonLogitsWarper,
      EtaLogitsWarper,
      ExponentialDecayLengthPenalty,
      ForcedBOSTokenLogitsProcessor,
      ForcedEOSTokenLogitsProcessor,
      HammingDiversityLogitsProcessor,
      InfNanRemoveLogitsProcessor,
      LogitNormalization,
      LogitsProcessor,
      LogitsProcessorList,
      LogitsWarper,
      MinLengthLogitsProcessor,
      MinNewTokensLengthLogitsProcessor,
      NoBadWordsLogitsProcessor,
      NoRepeatNGramLogitsProcessor,
      PrefixConstrainedLogitsProcessor,
      RepetitionPenaltyLogitsProcessor,
      SequenceBiasLogitsProcessor,
      TemperatureLogitsWarper,
      TopKLogitsWarper,
      TopPLogitsWarper,
      TypicalLogitsWarper,
  )
  from .stopping_criteria import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )
  from .utils import (
      BeamSampleDecoderOnlyOutput,
      BeamSampleEncoderDecoderOutput,
      BeamSearchDecoderOnlyOutput,
      BeamSearchEncoderDecoderOutput,
      ContrastiveSearchDecoderOnlyOutput,
      ContrastiveSearchEncoderDecoderOutput,
      GenerationMixin,
      GreedySearchDecoderOnlyOutput,
      GreedySearchEncoderDecoderOutput,
      SampleDecoderOnlyOutput,
      SampleEncoderDecoderOutput,
      top_k_top_p_filtering,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tf_logits_process import (
      TFForcedBOSTokenLogitsProcessor,
      TFForcedEOSTokenLogitsProcessor,
      TFForceTokensLogitsProcessor,
      TFLogitsProcessor,
      TFLogitsProcessorList,
      TFLogitsWarper,
      TFMinLengthLogitsProcessor,
      TFNoBadWordsLogitsProcessor,
      TFNoRepeatNGramLogitsProcessor,
      TFRepetitionPenaltyLogitsProcessor,
      TFSuppressTokensAtBeginLogitsProcessor,
      TFSuppressTokensLogitsProcessor,
      TFTemperatureLogitsWarper,
      TFTopKLogitsWarper,
      TFTopPLogitsWarper,
  )
  from .tf_utils import (
      TFBeamSampleDecoderOnlyOutput,
      TFBeamSampleEncoderDecoderOutput,
      TFBeamSearchDecoderOnlyOutput,
      TFBeamSearchEncoderDecoderOutput,
      TFContrastiveSearchDecoderOnlyOutput,
      TFContrastiveSearchEncoderDecoderOutput,
      TFGenerationMixin,
      TFGreedySearchDecoderOnlyOutput,
      TFGreedySearchEncoderDecoderOutput,
      TFSampleDecoderOnlyOutput,
      TFSampleEncoderDecoderOutput,
      tf_top_k_top_p_filtering,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .flax_logits_process import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxLogitsWarper,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )
  from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
 import sys

 a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)