
'''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   snake_case_       (							_a     :							Dict			):
      snake_case_						       =							fname.split(os.path.sep			)[-1]
      return re.search(R"^(.*)_\d+\.jpg$" ,					_a			).groups()[0]



class  lowerCAmelCase__   (       UpperCAmelCase__       ):



  '''simple docstring'''






  def __init__(self  : Any	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : List[Any]=None	, lowerCAmelCase_  : Any=None    )   ->       List[str]:
        snake_case_						       =							file_names
        snake_case_						       =							image_transform
        snake_case_						       =							label_to_id

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


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

def   snake_case_       (							_a     :							List[str] ,					_a     :							List[str]			):
      # Initialize accelerator
      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
      snake_case_						       =							config["lr"]
      snake_case_						       =							int(config["num_epochs"]			)
      snake_case_						       =							int(config["seed"]			)
      snake_case_						       =							int(config["batch_size"]			)
      snake_case_						       =							config["image_size"]
      if not isinstance(_a ,					(list, tuple)			):
            snake_case_						       =							(image_size, image_size)

      # Grab all the image filenames
      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
      snake_case_						       =							[extract_label(_a			) for fname in file_names]
      snake_case_						       =							list(set(_a			)			)
      id_to_label.sort()
      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
      snake_case_						       =							np.random.permutation(len(_a			)			)
      snake_case_						       =							int(0.8 * len(_a			)			)
      snake_case_						       =							random_perm[:cut]
      snake_case_						       =							random_perm[cut:]

      # For training we use a simple RandomResizedCrop
      snake_case_						       =							Compose([RandomResizedCrop(_a ,					scale=(0.5, 1.0)			), ToTensor()]			)
      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
      snake_case_						       =							Compose([Resize(_a			), ToTensor()]			)
      snake_case_						       =							PetsDataset([file_names[i] for i in eval_split] ,					image_transform=_a ,					label_to_id=_a			)

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

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

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

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

      # Instantiate learning rate scheduler
      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.
      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`.
                  snake_case_						       =							{k: v.to(accelerator.device			) for k, v in batch.items()}
                  snake_case_						       =							(batch["image"] - mean) / std
                  snake_case_						       =							model(_a			)
                  snake_case_						       =							torch.nn.functional.cross_entropy(_a ,					batch["label"]			)
                  accelerator.backward(_a			)
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()

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

            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   snake_case_       (							):
      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."			)
      snake_case_						       =							parser.parse_args()
      snake_case_						       =							{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
      training_function(_a ,					_a			)


if __name__ == "__main__":
     main()



'''simple docstring'''





import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


A           =   {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"}
A           =   set("".join(["‘", "’", "´", "`", ".", ",", "-", "\""]))
def   snake_case_       (							_a     :							Any ,					_a     :							Union[str, Any]=0 ,					_a     :							Optional[int]=None			):
      return [" ".join(x.split(" "			)[begin:end]			) for x in answers if len(x.split(" "			)			) > 1]
def   snake_case_       (							_a     :							Optional[int] ,					_a     :							str=False			):
      if make_sub_answers:
            # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
            # *e.g.* if the correct answer contains a prefix such as "the", or "a"
            snake_case_						       =							(
                given_answers + get_sub_answers(_a ,					begin=1			) + get_sub_answers(_a ,					end=-1			)
            )
      snake_case_						       =							[]
      for answer in given_answers:
            snake_case_						       =							answer.replace("_" ,					" "			).lower()
            snake_case_						       =							"".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias			)
            answers.append(" ".join(alias.split()			).strip()			)
      return set(_a			)
def   snake_case_       (							_a     :							Optional[Any] ,					_a     :							int ,					_a     :							Optional[Any]=1 ,					_a     :							List[Any]=100			):
      snake_case_       ,	snake_case_						       =							jax.lax.top_k(_a ,					_a			)
      snake_case_       ,	snake_case_						       =							jax.lax.top_k(_a ,					_a			)

      snake_case_						       =							best_end_idx[:, None] - best_start_idx[None, :]
      snake_case_						       =							jnp.logical_or(widths < 0 ,					widths > max_size			)
      snake_case_						       =							(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
      snake_case_						       =							jnp.argmax(_a			).item()

      return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
def   snake_case_       (							_a     :							Any			):
      snake_case_						       =							sample["question"]["text"]
      snake_case_						       =							sample["document"]["tokens"]["token"]
      snake_case_						       =							sample["document"]["tokens"]["is_html"]
      snake_case_						       =							sample["annotations"]["long_answer"]
      snake_case_						       =							sample["annotations"]["short_answers"]

      snake_case_						       =							" ".join([context[i] for i in range(len(_a			)			) if not is_html[i]]			)

      # 0 - No ; 1 - Yes
      for answer in sample["annotations"]["yes_no_answer"]:
            if answer == 0 or answer == 1:
                  return {
                      "question": question,
                      "context": context_string,
                      "short": [],
                      "long": [],
                      "category": "no" if answer == 0 else "yes",
                  }

      snake_case_						       =							[]
      for s in short_answers:
            short_targets.extend(s["text"]			)
      snake_case_						       =							list(set(_a			)			)

      snake_case_						       =							[]
      for s in long_answers:
            if s["start_token"] == -1:
                  continue
            snake_case_						       =							context[s["start_token"] : s["end_token"]]
            snake_case_						       =							is_html[s["start_token"] : s["end_token"]]
            snake_case_						       =							" ".join([answer[i] for i in range(len(_a			)			) if not html[i]]			)
            if new_answer not in long_targets:
                  long_targets.append(_a			)

      snake_case_						       =							"long_short" if len(short_targets + long_targets			) > 0 else "null"

      return {
          "question": question,
          "context": context_string,
          "short": short_targets,
          "long": long_targets,
          "category": category,
      }
def   snake_case_       (							):
      snake_case_						       =							load_from_disk("natural-questions-validation"			)
      snake_case_						       =							dataset.map(_a			).remove_columns(["annotations", "document", "id"]			)
      print(_a			)

      snake_case_						       =							dataset.filter(lambda _a			: (len(x["question"]			) + len(x["context"]			)) < 4 * 4_096			)
      snake_case_						       =							short_validation_dataset.filter(lambda _a			: x["category"] != "null"			)
      short_validation_dataset

      snake_case_						       =							"vasudevgupta/flax-bigbird-natural-questions"
      snake_case_						       =							FlaxBigBirdForNaturalQuestions.from_pretrained(_a			)
      snake_case_						       =							BigBirdTokenizerFast.from_pretrained(_a			)

      @jax.jit
      def forward(*_a     :							List[Any] ,					**_a     :							int			):
            snake_case_       ,	snake_case_       ,	snake_case_						       =							model(*_a ,					**_a			)
            return start_logits, end_logits, jnp.argmax(_a ,					axis=-1			)

      def evaluate(_a     :							List[Any]			):
            # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
            snake_case_						       =							tokenizer(
                example["question"] ,					example["context"] ,					return_tensors="np" ,					max_length=4_096 ,					padding="max_length" ,					truncation=_a ,					)

            snake_case_       ,	snake_case_       ,	snake_case_						       =							forward(**_a			)

            snake_case_						       =							CATEGORY_MAPPING[category.item()]

            snake_case_						       =							example["long"] + example["short"]
            if example["category"] in ["yes", "no", "null"]:
                  snake_case_						       =							[example["category"]]
            snake_case_						       =							example["category"] != "null"
            # Now target can be: "yes", "no", "null", "list of long & short answers"

            if predicted_category in ["yes", "no", "null"]:
                  snake_case_						       =							[predicted_category]
                  snake_case_						       =							example["output"] == example["targets"]
                  snake_case_						       =							predicted_category != "null"
                  return example

            snake_case_						       =							38 if predicted_category == "short" else 1_024
            snake_case_       ,	snake_case_						       =							get_best_valid_start_end_idx(
                start_scores[0] ,					end_scores[0] ,					top_k=8 ,					max_size=_a			)

            snake_case_						       =							inputs["input_ids"][0].tolist()
            snake_case_						       =							[tokenizer.decode(input_ids[start_score : end_score + 1]			)]

            snake_case_						       =							expand_to_aliases(example["targets"] ,					make_sub_answers=_a			)
            snake_case_						       =							expand_to_aliases(example["output"]			)

            # some preprocessing to both prediction and answer
            snake_case_						       =							{"".join(a.split()			) for a in answers}
            snake_case_						       =							{"".join(p.split()			) for p in predictions}
            snake_case_						       =							{s for s in predictions if s not in ["``", "''", "`", "'"]}

            # if there is a common element, it's a exact match
            snake_case_						       =							len(list(answers & predictions			)			) > 0
            snake_case_						       =							predicted_category != "null" and len(_a			) > 0

            return example

      snake_case_						       =							short_validation_dataset.map(_a			)

      snake_case_						       =							len(_a			)
      snake_case_						       =							len(short_validation_dataset.filter(lambda _a			: x["match"] == 1			)			)
      print("EM score:" ,					(matched / total) * 100 ,					"%"			)


if __name__ == "__main__":
     main()

