

"""simple docstring"""

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






class 			__a					(						__magic_name__       ):





       """simple docstring"""


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





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				file_names
        lowerCAmelCase__   :				Union[str, Any] 			=				image_transform
        lowerCAmelCase__   :				Tuple 			=				label_to_id






       def __len__(   self   ):





        """simple docstring"""

        return len(self.file_names   )






       def __getitem__(   self			,					snake_case   ):





        """simple docstring"""

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




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Union[str, Any]:
 # Initialize accelerator
 if args.with_tracking:
  lowerCAmelCase__   :				str 			=				Accelerator(
      cpu=args.cpu					,					mixed_precision=args.mixed_precision					,					log_with="all"					,					project_dir=args.project_dir   )
 else:
  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__   :				List[Any] 			=				config["lr"]
 lowerCAmelCase__   :				Optional[int] 			=				int(config["num_epochs"]   )
 lowerCAmelCase__   :				str 			=				int(config["seed"]   )
 lowerCAmelCase__   :				Optional[int] 			=				int(config["batch_size"]   )
 lowerCAmelCase__   :				List[Any] 			=				config["image_size"]
 if not isinstance(lowercase__					,					(list, tuple)   ):
  lowerCAmelCase__   :				int 			=				(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__   :				Tuple 			=				args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   lowerCAmelCase__   :				List[str] 			=				int(args.checkpointing_steps   )
  else:
   raise ValueError(
       F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
 else:
  lowerCAmelCase__   :				Dict 			=				None

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

 # 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__   :				Union[str, Any] 			=				[extract_label(lowercase__   ) for fname in file_names]
 lowerCAmelCase__   :				Union[str, 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__   :				List[Any] 			=				np.random.permutation(len(lowercase__   )   )
 lowerCAmelCase__   :				Any 			=				int(0.8 * len(lowercase__   )   )
 lowerCAmelCase__   :				str 			=				random_perm[:cut]
 lowerCAmelCase__   :				List[str] 			=				random_perm[cut:]

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

 # For evaluation, we use a deterministic Resize
 lowerCAmelCase__   :				Dict 			=				Compose([Resize(lowercase__   ), ToTensor()]   )
 lowerCAmelCase__   :				Tuple 			=				PetsDataset([file_names[i] for i in eval_split]					,					image_transform=lowercase__					,					label_to_id=lowercase__   )

 # Instantiate dataloaders.
 lowerCAmelCase__   :				Any 			=				DataLoader(lowercase__					,					shuffle=lowercase__					,					batch_size=lowercase__					,					num_workers=4   )
 lowerCAmelCase__   :				Optional[int] 			=				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[Any] 			=				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__   :				str 			=				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__   :				Any 			=				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__   :				Tuple 			=				torch.tensor(model.default_cfg["std"]   )[None, :, None, None].to(accelerator.device   )

 # Instantiate optimizer
 lowerCAmelCase__   :				Tuple 			=				torch.optim.Adam(params=model.parameters()					,					lr=lr / 2_5   )

 # Instantiate learning rate scheduler
 lowerCAmelCase__   :				List[str] 			=				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__   :				Optional[Any] 			=				accelerator.prepare(
     lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )
 # 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__   :				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   )
   lowerCAmelCase__   :				Optional[Any] 			=				os.path.basename(args.resume_from_checkpoint   )
  else:
   # Get the most recent checkpoint
   lowerCAmelCase__   :				Optional[int] 			=				[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__   :				List[str] 			=				os.path.splitext(lowercase__   )[0]

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

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

  lowerCAmelCase__   :				Union[str, Any] 			=				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(lowercase__   ),
           "epoch": epoch,
       }					,					step=lowercase__					,					)
  if checkpointing_steps == "epoch":
   lowerCAmelCase__   :				Optional[Any] 			=				F"""epoch_{epoch}"""
   if args.output_dir is not None:
    lowerCAmelCase__   :				int 			=				os.path.join(args.output_dir					,					lowercase__   )
   accelerator.save_state(lowercase__   )

 if args.with_tracking:
  accelerator.end_training()



def       SCREAMING_SNAKE_CASE							( )			->						Any:
 lowerCAmelCase__   :				Optional[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("--fp16"					,					action="store_true"					,					help="If passed, will use FP16 training."   )
 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("--cpu"					,					action="store_true"					,					help="If passed, will train on the CPU."   )
 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(
     "--output_dir"					,					type=lowercase__					,					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=lowercase__					,					default=lowercase__					,					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=lowercase__					,					default="logs"					,					help="Location on where to store experiment tracking logs` and relevent project information"					,					)
 lowerCAmelCase__   :				Optional[Any] 			=				parser.parse_args()
 lowerCAmelCase__   :				Any 			=				{"lr": 3E-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
 training_function(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    main()









"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

_UpperCamelCase		     =    TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_UpperCamelCase		     =    TaTokenizerFast

_UpperCamelCase		     =    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    [
        """MT5EncoderModel""",
        """MT5ForConditionalGeneration""",
        """MT5ForQuestionAnswering""",
        """MT5Model""",
        """MT5PreTrainedModel""",
        """MT5Stack""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    _UpperCamelCase		     =    _LazyModule(
        __name__,
        globals()["""__file__"""],
        _import_structure,
        extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
        module_spec=__spec__,
    )






