


"""simple docstring"""







import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__lowercase    =				16
__lowercase    =				32


def 					lowercase      (    A_  ,       A_ = 16	)->				str:
  '''simple docstring'''







  a						:		int											=      AutoTokenizer.from_pretrained("bert-base-cased"	)
  a						:		Dict											=      load_dataset("glue"  ,       "mrpc"	)

  def tokenize_function(A_	):
    # max_length=None => use the model max length (it's actually the default)
    a						:		List[Any]											=      tokenizer(examples["sentence1"]  ,       examples["sentence2"]  ,       truncation=A_  ,       max_length=A_	)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    a						:		Any											=      datasets.map(
        A_  ,       batched=A_  ,       remove_columns=["idx", "sentence1", "sentence2"]  ,       )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  a						:		str											=      tokenized_datasets.rename_column("label"  ,       "labels"	)

  def collate_fn(A_	):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a						:		List[Any]											=      128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      a						:		Optional[int]											=      16
    elif accelerator.mixed_precision != "no":
      a						:		Tuple											=      8
    else:
      a						:		str											=      None

    return tokenizer.pad(
        A_  ,       padding="longest"  ,       max_length=A_  ,       pad_to_multiple_of=A_  ,       return_tensors="pt"  ,       )

  # Instantiate dataloaders.
  a						:		int											=      DataLoader(
      tokenized_datasets["train"]  ,       shuffle=A_  ,       collate_fn=A_  ,       batch_size=A_	)
  a						:		Union[str, Any]											=      DataLoader(
      tokenized_datasets["validation"]  ,       shuffle=A_  ,       collate_fn=A_  ,       batch_size=A_	)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 __lowercase    =				mocked_dataloaders  # noqa: F811


def 					lowercase      (    A_  ,       A_	)->				Any:
  '''simple docstring'''







  if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,       A_	) == "1":
    a						:		Tuple											=      2
  # Initialize accelerator
  a						:		str											=      Accelerator(cpu=args.cpu  ,       mixed_precision=args.mixed_precision	)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a						:		int											=      config["lr"]
  a						:		List[Any]											=      int(config["num_epochs"]	)
  a						:		Optional[int]											=      int(config["seed"]	)
  a						:		int											=      int(config["batch_size"]	)

  a						:		Dict											=      evaluate.load("glue"  ,       "mrpc"	)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=A_	)
  def inner_training_loop(A_	):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(A_	)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    a						:		Optional[int]											=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"  ,       return_dict=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).
    a						:		Dict											=      model.to(accelerator.device	)

    # Instantiate optimizer
    a						:		Optional[int]											=      AdamW(params=model.parameters()  ,       lr=A_	)
    a							,		a						:		str											=      get_dataloaders(A_  ,       A_	)

    # Instantiate scheduler
    a						:		Optional[Any]											=      get_linear_schedule_with_warmup(
        optimizer=A_  ,       num_warmup_steps=100  ,       num_training_steps=(len(A_	) * num_epochs)  ,       )

    # 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.
    a							,		a							,		a							,		a							,		a						:		List[str]											=      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`.
        batch.to(accelerator.device	)
        a						:		Optional[Any]											=      model(**A_	)
        a						:		Dict											=      outputs.loss
        accelerator.backward(A_	)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(A_	):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device	)
        with torch.no_grad():
          a						:		List[Any]											=      model(**A_	)
        a						:		Optional[Any]											=      outputs.logits.argmax(dim=-1	)
        a							,		a						:		str											=      accelerator.gather_for_metrics((predictions, batch["labels"])	)
        metric.add_batch(
            predictions=A_  ,       references=A_  ,       )

      a						:		Any											=      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''  ,       A_	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()


def 					lowercase      (    )->				List[str]:
  '''simple docstring'''







  a						:		str											=      argparse.ArgumentParser(description="Simple example of training script."	)
  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("--cpu"  ,       action="store_true"  ,       help="If passed, will train on the CPU."	)
  a						:		int											=      parser.parse_args()
  a						:		List[Any]											=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(A_  ,       A_	)


if __name__ == "__main__":
 main()










"""simple docstring"""







from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowercase    =				logging.get_logger(__name__)






@add_end_docstrings(_a  )
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			**__UpperCAmelCase    :   List[Any]):
      super().__init__(**__UpperCAmelCase)

      if self.framework == "tf":
        raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')

      requires_backends(self    ,			"vision")
      self.check_model_type(__UpperCAmelCase)






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Union[str, "Image.Image", List[Dict[str, Any]]]    ,			__UpperCAmelCase    :   Union[str, List[str]] = None    ,			**__UpperCAmelCase    :   List[Any]    ,			):
      if "text_queries" in kwargs:
        a						:		List[Any]											=      kwargs.pop("text_queries")

      if isinstance(__UpperCAmelCase    ,			(str, Image.Image)):
        a						:		Any											=      {"image": image, "candidate_labels": candidate_labels}
      else:
        a						:		Optional[int]											=      image
      a						:		Optional[int]											=      super().__call__(__UpperCAmelCase    ,			**__UpperCAmelCase)
      return results






    def      __snake_case							( self    :   Optional[int]    ,			**__UpperCAmelCase    :   List[Any]):
      a						:		str											=      {}
      if "threshold" in kwargs:
        a						:		Dict											=      kwargs["threshold"]
      if "top_k" in kwargs:
        a						:		str											=      kwargs["top_k"]
      return {}, {}, postprocess_params






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      a						:		Union[str, Any]											=      load_image(inputs["image"])
      a						:		Any											=      inputs["candidate_labels"]
      if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase):
        a						:		Optional[Any]											=      candidate_labels.split(",")

      a						:		Union[str, Any]											=      torch.tensor([[image.height, image.width]]    ,			dtype=torch.intaa)
      for i, candidate_label in enumerate(__UpperCAmelCase):
        a						:		int											=      self.tokenizer(__UpperCAmelCase    ,			return_tensors=self.framework)
        a						:		int											=      self.image_processor(__UpperCAmelCase    ,			return_tensors=self.framework)
        yield {
            "is_last": i == len(__UpperCAmelCase) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		List[Any]											=      model_inputs.pop("target_size")
      a						:		Optional[int]											=      model_inputs.pop("candidate_label")
      a						:		List[Any]											=      model_inputs.pop("is_last")

      a						:		List[Any]											=      self.model(**__UpperCAmelCase)

      a						:		Union[str, Any]											=      {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
      return model_outputs






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[str]=None):
      a						:		Dict											=      []
      for model_output in model_outputs:
        a						:		int											=      model_output["candidate_label"]
        a						:		Any											=      BaseModelOutput(__UpperCAmelCase)
        a						:		Optional[Any]											=      self.image_processor.post_process_object_detection(
            outputs=__UpperCAmelCase    ,			threshold=__UpperCAmelCase    ,			target_sizes=model_output["target_size"])[0]

        for index in outputs["scores"].nonzero():
          a						:		Any											=      outputs["scores"][index].item()
          a						:		str											=      self._get_bounding_box(outputs["boxes"][index][0])

          a						:		Optional[Any]											=      {"score": score, "label": label, "box": box}
          results.append(__UpperCAmelCase)

      a						:		str											=      sorted(__UpperCAmelCase    ,			key=lambda __UpperCAmelCase: x["score"]    ,			reverse=__UpperCAmelCase)
      if top_k:
        a						:		Union[str, Any]											=      results[:top_k]

      return results






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   "torch.Tensor"):
      if self.framework != "pt":
        raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
      a							,		a							,		a							,		a						:		List[Any]											=      box.int().tolist()
      a						:		str											=      {
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox






