

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







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""



  def __init__(	self    :				List[Any]     ,  A_    :				List[str]     ,  A_    :				Optional[int]=None     ,  A_    :				Union[str, Any]=None	)					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						file_names
      SCREAMING_SNAKE_CASE__					     =						image_transform
      SCREAMING_SNAKE_CASE__					     =						label_to_id



  def __len__(	self    :				Dict	)					-> Tuple:
      return len(self.file_names	)







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





def 							__a       (					__A ,       __A	):
    # Initialize accelerator
    SCREAMING_SNAKE_CASE__					     =						Accelerator(cpu=args.cpu ,       mixed_precision=args.mixed_precision	)

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE__					     =						config["lr"]
    SCREAMING_SNAKE_CASE__					     =						int(config["num_epochs"]	)
    SCREAMING_SNAKE_CASE__					     =						int(config["seed"]	)
    SCREAMING_SNAKE_CASE__					     =						int(config["batch_size"]	)
    SCREAMING_SNAKE_CASE__					     =						config["image_size"]
    if not isinstance(__A ,       (list, tuple)	):
        SCREAMING_SNAKE_CASE__					     =						(image_size, image_size)

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

    # Build the label correspondences
    SCREAMING_SNAKE_CASE__					     =						[extract_label(__A	) for fname in file_names]
    SCREAMING_SNAKE_CASE__					     =						list(set(__A	)	)
    id_to_label.sort()
    SCREAMING_SNAKE_CASE__					     =						{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
    SCREAMING_SNAKE_CASE__					     =						np.random.permutation(len(__A	)	)
    SCREAMING_SNAKE_CASE__					     =						int(0.8 * len(__A	)	)
    SCREAMING_SNAKE_CASE__					     =						random_perm[:cut]
    SCREAMING_SNAKE_CASE__					     =						random_perm[cut:]

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

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

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE__					     =						DataLoader(__A ,       shuffle=__A ,       batch_size=__A ,       num_workers=4	)
    SCREAMING_SNAKE_CASE__					     =						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)
    SCREAMING_SNAKE_CASE__					     =						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).
    SCREAMING_SNAKE_CASE__					     =						model.to(accelerator.device	)

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

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

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

    # Instantiate learning rate scheduler
    SCREAMING_SNAKE_CASE__					     =						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.
    SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						accelerator.prepare(
        __A ,       __A ,       __A ,       __A ,       __A	)

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

        model.eval()
        SCREAMING_SNAKE_CASE__					     =						0
        SCREAMING_SNAKE_CASE__					     =						0
        for _, batch in enumerate(__A	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            SCREAMING_SNAKE_CASE__					     =						{k: v.to(accelerator.device	) for k, v in batch.items()}
            SCREAMING_SNAKE_CASE__					     =						(batch["image"] - mean) / std
            with torch.no_grad():
                SCREAMING_SNAKE_CASE__					     =						model(__A	)
            SCREAMING_SNAKE_CASE__					     =						outputs.argmax(dim=-1	)
            SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						accelerator.gather_for_metrics((predictions, batch["label"])	)
            SCREAMING_SNAKE_CASE__					     =						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        SCREAMING_SNAKE_CASE__					     =						accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"	)





def 							__a       (					):
    SCREAMING_SNAKE_CASE__					     =						argparse.ArgumentParser(description="Simple example of training script."	)
    parser.add_argument("--data_dir" ,       required=__A ,       help="The data folder on disk."	)
    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(
        "--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("--cpu" ,       action="store_true" ,       help="If passed, will train on the CPU."	)
    SCREAMING_SNAKE_CASE__					     =						parser.parse_args()
    SCREAMING_SNAKE_CASE__					     =						{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
    training_function(__A ,       __A	)


if __name__ == "__main__":
  main()





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


A__        =			logging.get_logger(__name__)

A__        =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A__        =			{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

A__        =			{
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: str										=      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				: Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				: Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				: List[str]										=      ["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__				: List[str]										=      LongformerTokenizer



  def __init__(	self    :				List[Any]     ,  A_    :				Any=None     ,  A_    :				Union[str, Any]=None     ,  A_    :				Optional[int]=None     ,  A_    :				int="replace"     ,  A_    :				Any="<s>"     ,  A_    :				int="</s>"     ,  A_    :				str="</s>"     ,  A_    :				Optional[Any]="<s>"     ,  A_    :				Optional[int]="<unk>"     ,  A_    :				Optional[int]="<pad>"     ,  A_    :				List[str]="<mask>"     ,  A_    :				Optional[int]=False     ,  A_    :				List[str]=True     ,  **A_    :				List[str]     ,  )					-> Any:
      super().__init__(
          A_     ,  A_     ,  tokenizer_file=A_     ,  errors=A_     ,  bos_token=A_     ,  eos_token=A_     ,  sep_token=A_     ,  cls_token=A_     ,  unk_token=A_     ,  pad_token=A_     ,  mask_token=A_     ,  add_prefix_space=A_     ,  trim_offsets=A_     ,  **A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get("add_prefix_space"     ,  A_	) != add_prefix_space:
          SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  pre_tok_state.pop("type"	)	)
          SCREAMING_SNAKE_CASE__					     =						add_prefix_space
          SCREAMING_SNAKE_CASE__					     =						pre_tok_class(**A_	)

      SCREAMING_SNAKE_CASE__					     =						add_prefix_space

      SCREAMING_SNAKE_CASE__					     =						"post_processor"
      SCREAMING_SNAKE_CASE__					     =						getattr(self.backend_tokenizer     ,  A_     ,  A_	)
      if tokenizer_component_instance:
          SCREAMING_SNAKE_CASE__					     =						json.loads(tokenizer_component_instance.__getstate__()	)

          # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
          if "sep" in state:
              SCREAMING_SNAKE_CASE__					     =						tuple(state["sep"]	)
          if "cls" in state:
              SCREAMING_SNAKE_CASE__					     =						tuple(state["cls"]	)

          SCREAMING_SNAKE_CASE__					     =						False

          if state.get("add_prefix_space"     ,  A_	) != add_prefix_space:
              SCREAMING_SNAKE_CASE__					     =						add_prefix_space
              SCREAMING_SNAKE_CASE__					     =						True

          if state.get("trim_offsets"     ,  A_	) != trim_offsets:
              SCREAMING_SNAKE_CASE__					     =						trim_offsets
              SCREAMING_SNAKE_CASE__					     =						True

          if changes_to_apply:
              SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  state.pop("type"	)	)
              SCREAMING_SNAKE_CASE__					     =						component_class(**A_	)
              setattr(self.backend_tokenizer     ,  A_     ,  A_	)



  @property
  def      _UpperCamelCase    (	self    :				Dict	)					-> str:
      if self._mask_token is None:
          if self.verbose:
              logger.error("Using mask_token, but it is not set yet."	)
          return None
      return str(self._mask_token	)



  @mask_token.setter
  def      _UpperCamelCase    (	self    :				int     ,  A_    :				Optional[Any]	)					-> int:
      SCREAMING_SNAKE_CASE__					     =						AddedToken(A_     ,  lstrip=A_     ,  rstrip=A_	) if isinstance(A_     ,  A_	) else value
      SCREAMING_SNAKE_CASE__					     =						value



  def      _UpperCamelCase    (	self    :				int     ,  *A_    :				int     ,  **A_    :				Dict	)					-> BatchEncoding:
      SCREAMING_SNAKE_CASE__					     =						kwargs.get("is_split_into_words"     ,  A_	)
      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*A_     ,  **A_	)



  def      _UpperCamelCase    (	self    :				Dict     ,  *A_    :				int     ,  **A_    :				Union[str, Any]	)					-> BatchEncoding:
      SCREAMING_SNAKE_CASE__					     =						kwargs.get("is_split_into_words"     ,  A_	)

      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*A_     ,  **A_	)



  def      _UpperCamelCase    (	self    :				int     ,  A_    :				str     ,  A_    :				Optional[str] = None	)					-> Tuple[str]:
      SCREAMING_SNAKE_CASE__					     =						self._tokenizer.model.save(A_     ,  name=A_	)
      return tuple(A_	)



  def      _UpperCamelCase    (	self    :				Tuple     ,  A_    :				List[str]     ,  A_    :				str=None	)					-> List[str]:
      SCREAMING_SNAKE_CASE__					     =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
          return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







  def      _UpperCamelCase    (	self    :				List[str]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]

      if token_ids_a is None:
          return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]


