






"""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
#
########################################################################


UpperCAmelCase_     :				Tuple         =				16
UpperCAmelCase_     :				Dict         =				32



def       _A    (__a					,       __a = 16							)				-> Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[Any]							=	AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    SCREAMING_SNAKE_CASE_   :						Tuple							=	load_dataset('''glue'''					,       '''mrpc'''							)

    def tokenize_function(__a							):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_   :						List[str]							=	tokenizer(examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=_lowerCamelCase					,       max_length=_lowerCamelCase							)
        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():
        SCREAMING_SNAKE_CASE_   :						Dict							=	datasets.map(
            _lowerCamelCase					,       batched=_lowerCamelCase					,       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
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	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.
        SCREAMING_SNAKE_CASE_   :						str							=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            SCREAMING_SNAKE_CASE_   :						Tuple							=	16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_   :						str							=	8
        else:
            SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	None

        return tokenizer.pad(
            _lowerCamelCase					,       padding='''longest'''					,       max_length=_lowerCamelCase					,       pad_to_multiple_of=_lowerCamelCase					,       return_tensors='''pt'''					,       )

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_   :						List[str]							=	DataLoader(
        tokenized_datasets['''train''']					,       shuffle=_lowerCamelCase					,       collate_fn=_lowerCamelCase					,       batch_size=_lowerCamelCase							)
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	DataLoader(
        tokenized_datasets['''validation''']					,       shuffle=_lowerCamelCase					,       collate_fn=_lowerCamelCase					,       batch_size=_lowerCamelCase							)

    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

      UpperCAmelCase_     :				List[Any]         =				mocked_dataloaders  # noqa: F811



def       _A    (__a					,       __a							)				-> Any:
    """simple docstring"""






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,       _lowerCamelCase							) == "1":
        SCREAMING_SNAKE_CASE_   :						Tuple							=	2
    # Initialize accelerator
    SCREAMING_SNAKE_CASE_   :						Any							=	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_   :						Dict							=	config["lr"]
    SCREAMING_SNAKE_CASE_   :						str							=	int(config['''num_epochs''']							)
    SCREAMING_SNAKE_CASE_   :						int							=	int(config['''seed''']							)
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	int(config['''batch_size''']							)

    SCREAMING_SNAKE_CASE_   :						Any							=	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=_lowerCamelCase							)
    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(_lowerCamelCase							)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,       return_dict=_lowerCamelCase							)

        # 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_   :						Optional[int]							=	model.to(accelerator.device							)

        # Instantiate optimizer
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	AdamW(params=model.parameters()					,       lr=_lowerCamelCase							)
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	get_dataloaders(_lowerCamelCase					,       _lowerCamelCase							)

        # Instantiate scheduler
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	get_linear_schedule_with_warmup(
            optimizer=_lowerCamelCase					,       num_warmup_steps=1_00					,       num_training_steps=(len(_lowerCamelCase							) * 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.
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	accelerator.prepare(
            _lowerCamelCase					,       _lowerCamelCase					,       _lowerCamelCase					,       _lowerCamelCase					,       _lowerCamelCase							)

        # Now we train the model
        for epoch in range(_lowerCamelCase							):
            model.train()
            for step, batch in enumerate(_lowerCamelCase							):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device							)
                SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model(**_lowerCamelCase							)
                SCREAMING_SNAKE_CASE_   :						Dict							=	outputs.loss
                accelerator.backward(_lowerCamelCase							)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

            SCREAMING_SNAKE_CASE_   :						List[str]							=	metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:'					,       _lowerCamelCase							)

    # 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       _A    ()				-> Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
    parser.add_argument(
        '''--mixed_precision'''					,       type=_lowerCamelCase					,       default=_lowerCamelCase					,       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.'''							)
    SCREAMING_SNAKE_CASE_   :						str							=	parser.parse_args()
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(_lowerCamelCase					,       _lowerCamelCase							)


if __name__ == "__main__":
      main()










"""simple docstring"""






from typing import List, Union

import numpy as np

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


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase_     :				Optional[Any]         =				logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__				)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			*lowercase_  :						Any		,			**lowercase_  :						Any):




          '''simple docstring'''


          super().__init__(*lowercase_		,			**lowercase_)
          requires_backends(self		,			'''vision''')
          self.check_model_type(lowercase_)
      def __call__(		self  :						int		,			lowercase_  :						Union[str, List[str], "Image.Image", List["Image.Image"]]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


          return super().__call__(lowercase_		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			**lowercase_  :						List[str]):




          '''simple docstring'''


          return {}, {}, {}
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	load_image(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image.size
          SCREAMING_SNAKE_CASE_   :						int							=	self.image_processor(images=lowercase_		,			return_tensors=self.framework)
          return model_inputs
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.model(**lowercase_)
          return model_outputs



      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_outputs.predicted_depth
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,			size=self.image_size[::-1]		,			mode='''bicubic'''		,			align_corners=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	prediction.squeeze().cpu().numpy()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	(output * 255 / np.max(lowercase_)).astype('''uint8''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	Image.fromarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{}
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	predicted_depth
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	depth
          return output_dict


