

import argparse
import os

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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													=		16
UpperCamelCase													=		32

def     lowercase_       (    _lowerCamelCase							: List[Any]	,			_lowerCamelCase							: int):
    # Initialize accelerator
    if args.with_tracking:
        lowercase__				:     int					      =       Accelerator(
            cpu=args.cpu	,			mixed_precision=args.mixed_precision	,			log_with="all"	,			project_dir=args.project_dir)
    else:
        lowercase__				:     Union[str, Any]					      =       Accelerator(cpu=args.cpu	,			mixed_precision=args.mixed_precision)

    if hasattr(args.checkpointing_steps	,			"isdigit"):
        if args.checkpointing_steps == "epoch":
            lowercase__				:     Optional[Any]					      =       args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            lowercase__				:     Optional[int]					      =       int(args.checkpointing_steps)
        else:
            raise ValueError(
                f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''')
    else:
        lowercase__				:     Dict					      =       None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowercase__				:     Dict					      =       config["lr"]
    lowercase__				:     Optional[Any]					      =       int(config["num_epochs"])
    lowercase__				:     Tuple					      =       int(config["seed"])
    lowercase__				:     str					      =       int(config["batch_size"])

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

    lowercase__				:     List[str]					      =       AutoTokenizer.from_pretrained("bert-base-cased")
    lowercase__				:     str					      =       load_dataset("glue"	,			"mrpc")
    lowercase__				:     Any					      =       evaluate.load("glue"	,			"mrpc")

    def tokenize_function(_lowerCamelCase							: Tuple):
        # max_length=None => use the model max length (it's actually the default)
        lowercase__				:     List[Any]					      =       tokenizer(examples["sentence1"]	,			examples["sentence2"]	,			truncation=lowercase__	,			max_length=lowercase__)
        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():
        lowercase__				:     List[str]					      =       datasets.map(
            lowercase__	,			batched=lowercase__	,			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
    lowercase__				:     Union[str, Any]					      =       tokenized_datasets.rename_column("label"	,			"labels")

    # If the batch size is too big we use gradient accumulation
    lowercase__				:     Dict					      =       1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        lowercase__				:     Optional[Any]					      =       batch_size // MAX_GPU_BATCH_SIZE
        lowercase__				:     Optional[Any]					      =       MAX_GPU_BATCH_SIZE

    def collate_fn(_lowerCamelCase							: List[Any]):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowercase__				:     List[str]					      =       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":
            lowercase__				:     Optional[Any]					      =       16
        elif accelerator.mixed_precision != "no":
            lowercase__				:     Dict					      =       8
        else:
            lowercase__				:     Dict					      =       None

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

    # Instantiate dataloaders.
    lowercase__				:     List[Any]					      =       DataLoader(
        tokenized_datasets["train"]	,			shuffle=lowercase__	,			collate_fn=lowercase__	,			batch_size=lowercase__)
    lowercase__				:     Tuple					      =       DataLoader(
        tokenized_datasets["validation"]	,			shuffle=lowercase__	,			collate_fn=lowercase__	,			batch_size=lowercase__)

    set_seed(lowercase__)

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

    # Instantiate optimizer
    lowercase__				:     List[Any]					      =       AdamW(params=model.parameters()	,			lr=lowercase__)

    # Instantiate scheduler
    lowercase__				:     List[str]					      =       get_linear_schedule_with_warmup(
        optimizer=lowercase__	,			num_warmup_steps=100	,			num_training_steps=(len(lowercase__) * num_epochs) // gradient_accumulation_steps	,			)

    # 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.
    lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__      ,     lowercase__				:     List[str]					      =       accelerator.prepare(
        lowercase__	,			lowercase__	,			lowercase__	,			lowercase__	,			lowercase__)

    # We need to keep track of how many total steps we have iterated over
    lowercase__				:     List[str]					      =       0
    # We also need to keep track of the stating epoch so files are named properly
    lowercase__				:     Optional[int]					      =       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)
            lowercase__				:     Tuple					      =       os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            lowercase__				:     Union[str, Any]					      =       [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
            dirs.sort(key=os.path.getctime)
            lowercase__				:     int					      =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        lowercase__				:     Tuple					      =       os.path.splitext(lowercase__)[0]

        if "epoch" in training_difference:
            lowercase__				:     Dict					      =       int(training_difference.replace("epoch_"	,			"")) + 1
            lowercase__				:     Dict					      =       None
        else:
            lowercase__				:     Optional[Any]					      =       int(training_difference.replace("step_"	,			""))
            lowercase__				:     Optional[Any]					      =       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:
            lowercase__				:     Optional[Any]					      =       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
            lowercase__				:     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
            lowercase__				:     str					      =       train_dataloader
        for step, batch in enumerate(lowercase__):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device)
            lowercase__				:     str					      =       model(**lowercase__)
            lowercase__				:     int					      =       outputs.loss
            lowercase__				:     int					      =       loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(lowercase__)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(lowercase__	,			lowercase__):
                lowercase__				:     Union[str, Any]					      =       f'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        lowercase__				:     Optional[int]					      =       os.path.join(args.output_dir	,			lowercase__)
                    accelerator.save_state(lowercase__)

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

        lowercase__				:     Union[str, Any]					      =       metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'''epoch {epoch}:'''	,			lowercase__)
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(lowercase__),
                    "epoch": epoch,
                }	,			step=lowercase__	,			)

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

    if args.with_tracking:
        accelerator.end_training()

def     lowercase_       (    ):
    lowercase__				:     Optional[Any]					      =       argparse.ArgumentParser(description="Simple example of training script.")
    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(
        "--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(
        "--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(
        "--project_dir"	,			type=lowercase__	,			default="logs"	,			help="Location on where to store experiment tracking logs` and relevent project information"	,			)
    lowercase__				:     str					      =       parser.parse_args()
    lowercase__				:     Union[str, Any]					      =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase__	,			lowercase__)


if __name__ == "__main__":
      main()




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[Any]					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_			,		lowercase_						):
               lowercase__				:     List[Any]					      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__				:     Union[str, Any]					      =       features["words"]

       lowercase__				:     List[str]					      =       self.tokenizer(
           text=text if text is not None else features["words"]			,		text_pair=text_pair if text_pair is not None else None			,		boxes=boxes if boxes is not None else features["boxes"]			,		word_labels=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Optional[int]			,		lowercase_       :  Union[str, Any]						)      ->		Any:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__				:     str					      =       []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(lowercase_						) != len(lowercase_						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_						)} and {len(lowercase_						)}'''						)

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






   @property
   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Tuple:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



