



'''simple docstring'''







import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_=False       ,	lowerCAmelCase_=False       ,	lowerCAmelCase_="data2vec."						):
    UpperCamelCase__	     =      "backbone." if is_semantic else ""

    UpperCamelCase__	     =      []
    for i in range(config.num_hidden_layers						):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (F'{prefix}blocks.{i}.norm1.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_before.weight')						)
        rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_before.bias')						)
        rename_keys.append(
            (F'{prefix}blocks.{i}.attn.proj.weight', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.weight')						)
        rename_keys.append(
            (F'{prefix}blocks.{i}.attn.proj.bias', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.bias')						)
        rename_keys.append(
            (F'{prefix}blocks.{i}.norm2.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_after.weight')						)
        rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_after.bias')						)
        rename_keys.append(
            (F'{prefix}blocks.{i}.mlp.fc1.weight', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.weight')						)
        rename_keys.append(
            (F'{prefix}blocks.{i}.mlp.fc1.bias', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.bias')						)
        rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'{hf_prefix}encoder.layer.{i}.output.dense.weight')						)
        rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'{hf_prefix}encoder.layer.{i}.output.dense.bias')						)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (F'{prefix}cls_token', F'{hf_prefix}embeddings.cls_token'),
            (F'{prefix}patch_embed.proj.weight', F'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
            (F'{prefix}patch_embed.proj.bias', F'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
        ]						)

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("mask_token", F'{hf_prefix}embeddings.mask_token'),
                (
                    "rel_pos_bias.relative_position_bias_table",
                    F'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
                ),
                (
                    "rel_pos_bias.relative_position_index",
                    F'{hf_prefix}encoder.relative_position_bias.relative_position_index',
                ),
                ("norm.weight", "layernorm.weight"),
                ("norm.bias", "layernorm.bias"),
            ]						)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
            ]						)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("fc_norm.weight", F'{hf_prefix}pooler.layernorm.weight'),
                ("fc_norm.bias", F'{hf_prefix}pooler.layernorm.bias'),
                ("head.weight", "classifier.weight"),
                ("head.bias", "classifier.bias"),
            ]						)

    return rename_keys





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=False       ,	lowerCAmelCase_=False       ,	lowerCAmelCase_="data2vec_vision."						):
    for i in range(config.num_hidden_layers						):
        UpperCamelCase__	     =      "backbone." if is_semantic else ""
        # queries, keys and values
        UpperCamelCase__	     =      state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'						)
        UpperCamelCase__	     =      state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'						)
        UpperCamelCase__	     =      state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'						)

        UpperCamelCase__	     =      in_proj_weight[
            : config.hidden_size, :
        ]
        UpperCamelCase__	     =      q_bias
        UpperCamelCase__	     =      in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase__	     =      in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCamelCase__	     =      v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        UpperCamelCase__	     =      state_dict.pop(F'{prefix}blocks.{i}.gamma_1'						)
        UpperCamelCase__	     =      state_dict.pop(F'{prefix}blocks.{i}.gamma_2'						)

        UpperCamelCase__	     =      gamma_a
        UpperCamelCase__	     =      gamma_a

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            UpperCamelCase__	     =      state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_bias_table'						)
            UpperCamelCase__	     =      state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_index'						)

            UpperCamelCase__	     =      table
            UpperCamelCase__	     =      index





def     snake_case__			():
    UpperCamelCase__	     =      argparse.ArgumentParser(
        "Convert Data2VecVision to HF for image classification and pretraining"       ,	add_help=lowerCAmelCase_						)
    parser.add_argument("--hf_checkpoint_name"       ,	type=lowerCAmelCase_						)
    parser.add_argument("--input_size"       ,	default=2_24       ,	type=lowerCAmelCase_       ,	help="images input size"						)
    parser.add_argument("--beit_checkpoint"       ,	default=""       ,	help="beit checkpoint"						)

    return parser.parse_args()





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    def load_state_dict(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=""       ,	lowerCAmelCase_="relative_position_index"						):
        UpperCamelCase__	     =      []
        UpperCamelCase__	     =      []
        UpperCamelCase__	     =      []
        # copy state_dict so _load_from_state_dict can modify it
        UpperCamelCase__	     =      getattr(lowerCAmelCase_       ,	"_metadata"       ,	lowerCAmelCase_						)
        UpperCamelCase__	     =      state_dict.copy()
        if metadata is not None:
            UpperCamelCase__	     =      metadata

        def load(lowerCAmelCase_       ,	lowerCAmelCase_=""						):
            UpperCamelCase__	     =      {} if metadata is None else metadata.get(prefix[:-1]       ,	{}						)
            module._load_from_state_dict(
                lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)
            for name, child in module._modules.items():
                if child is not None:
                    load(lowerCAmelCase_       ,	prefix + name + "."						)

        load(lowerCAmelCase_       ,	prefix=lowerCAmelCase_						)

        UpperCamelCase__	     =      []
        UpperCamelCase__	     =      []
        for key in missing_keys:
            UpperCamelCase__	     =      True
            for ignore_key in ignore_missing.split("|"						):
                if ignore_key in key:
                    UpperCamelCase__	     =      False
                    break
            if keep_flag:
                warn_missing_keys.append(lowerCAmelCase_						)
            else:
                ignore_missing_keys.append(lowerCAmelCase_						)

        UpperCamelCase__	     =      warn_missing_keys

        if len(lowerCAmelCase_						) > 0:
            print(
                "Weights of {} not initialized from pretrained model: {}".format(
                    model.__class__.__name__       ,	lowerCAmelCase_						)						)
        if len(lowerCAmelCase_						) > 0:
            print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__       ,	lowerCAmelCase_						)						)
        if len(lowerCAmelCase_						) > 0:
            print(
                "Ignored weights of {} not initialized from pretrained model: {}".format(
                    model.__class__.__name__       ,	lowerCAmelCase_						)						)
        if len(lowerCAmelCase_						) > 0:
            print("\n".join(lowerCAmelCase_						)						)

    UpperCamelCase__	     =      {
        "pretrained": False,
        "use_shared_rel_pos_bias": True,
        "use_abs_pos_emb": False,
        "init_values": 0.1,
    }

    if is_finetuned:
        model_kwargs.update(
            {
                "num_classes": 10_00,
                "use_mean_pooling": True,
                "init_scale": 0.0_01,
                "use_rel_pos_bias": True,
            }						)

    UpperCamelCase__	     =      create_model(
        "beit_large_patch16_224" if is_large else "beit_base_patch16_224"       ,	**lowerCAmelCase_       ,	)
    UpperCamelCase__	     =      model.patch_embed.patch_size
    UpperCamelCase__	     =      (args.input_size // patch_size[0], args.input_size // patch_size[1])
    UpperCamelCase__	     =      torch.load(args.beit_checkpoint       ,	map_location="cpu"						)

    print(F'Load ckpt from {args.beit_checkpoint}'						)
    UpperCamelCase__	     =      None
    for model_key in ("model", "module"):
        if model_key in checkpoint:
            UpperCamelCase__	     =      checkpoint[model_key]
            print(F'Load state_dict by model_key = {model_key}'						)
            break

    UpperCamelCase__	     =      list(checkpoint_model.keys()						)
    for key in all_keys:
        if "relative_position_index" in key:
            checkpoint_model.pop(lowerCAmelCase_						)

        if "relative_position_bias_table" in key:
            UpperCamelCase__	     =      checkpoint_model[key]
            UpperCamelCase__     ,	UpperCamelCase__	     =      rel_pos_bias.size()
            UpperCamelCase__     ,	UpperCamelCase__	     =      model.state_dict()[key].size()
            UpperCamelCase__	     =      model.patch_embed.patch_shape
            if dst_patch_shape[0] != dst_patch_shape[1]:
                raise NotImplementedError()

    load_state_dict(lowerCAmelCase_       ,	lowerCAmelCase_       ,	prefix=""						)

    return model





def     snake_case__			():
    UpperCamelCase__	     =      get_args()

    UpperCamelCase__	     =      "ft1k" in args.hf_checkpoint_name
    UpperCamelCase__	     =      "large" in args.hf_checkpoint_name

    if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
    else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401

    # 1. Create model config
    UpperCamelCase__	     =      DataaVecVisionConfig()
    if is_finetuned:
        UpperCamelCase__	     =      True
        UpperCamelCase__	     =      False
        UpperCamelCase__	     =      True
        UpperCamelCase__	     =      10_00

        UpperCamelCase__	     =      "huggingface/label-files"
        UpperCamelCase__	     =      "imagenet-1k-id2label.json"
        UpperCamelCase__	     =      json.load(open(hf_hub_download(lowerCAmelCase_       ,	lowerCAmelCase_       ,	repo_type="dataset"						)       ,	"r"						)						)
        UpperCamelCase__	     =      {int(lowerCAmelCase_						): v for k, v in idalabel.items()}
        UpperCamelCase__	     =      idalabel
        UpperCamelCase__	     =      {v: k for k, v in idalabel.items()}
    else:
        UpperCamelCase__	     =      False
        UpperCamelCase__	     =      True
        UpperCamelCase__	     =      False

    if is_large:
        UpperCamelCase__	     =      10_24
        UpperCamelCase__	     =      40_96
        UpperCamelCase__	     =      24
        UpperCamelCase__	     =      16

    # 2. Load Beit model
    UpperCamelCase__	     =      load_beit_model(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)
    orig_model.eval()

    # 3. Forward Beit model
    UpperCamelCase__	     =      BeitImageProcessor(size=config.image_size       ,	do_center_crop=lowerCAmelCase_						)
    UpperCamelCase__	     =      Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"						)
    UpperCamelCase__	     =      image_processor(images=lowerCAmelCase_       ,	return_tensors="pt"						)
    UpperCamelCase__	     =      encoding["pixel_values"]

    UpperCamelCase__	     =      (pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
        UpperCamelCase__	     =      orig_model(*lowerCAmelCase_						)

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        UpperCamelCase__	     =      DataaVecVisionForImageClassification(lowerCAmelCase_						)
        hf_model.eval()
        UpperCamelCase__	     =      False
        UpperCamelCase__	     =      "data2vec_vision."
    else:
        UpperCamelCase__	     =      DataaVecVisionModel(lowerCAmelCase_						)
        hf_model.eval()
        UpperCamelCase__	     =      True
        UpperCamelCase__	     =      ""

    UpperCamelCase__	     =      create_rename_keys(lowerCAmelCase_       ,	hf_prefix=lowerCAmelCase_       ,	has_lm_head=lowerCAmelCase_						)
    UpperCamelCase__	     =      orig_model.state_dict()
    for src, dest in rename_keys:
        UpperCamelCase__	     =      state_dict.pop(lowerCAmelCase_						)
        UpperCamelCase__	     =      val

    read_in_q_k_v(lowerCAmelCase_       ,	lowerCAmelCase_       ,	hf_prefix=lowerCAmelCase_       ,	has_lm_head=lowerCAmelCase_						)
    UpperCamelCase__     ,	UpperCamelCase__	     =      hf_model.load_state_dict(lowerCAmelCase_       ,	strict=lowerCAmelCase_						)
    print("HF missing"       ,	lowerCAmelCase_						)
    print("HF unexpected_keys"       ,	lowerCAmelCase_						)

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        UpperCamelCase__	     =      hf_model(lowerCAmelCase_						)

    UpperCamelCase__	     =      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

    # 6. Compare
    UpperCamelCase__	     =      torch.max(torch.abs(hf_output - orig_model_output						)						).item()

    print(F'max_absolute_diff = {max_absolute_diff}'						)
    UpperCamelCase__	     =      torch.allclose(lowerCAmelCase_       ,	lowerCAmelCase_       ,	atol=1e-3						)
    print("Do both models output the same tensors?"       ,	"🔥" if success else "💩"						)
    if not success:
        raise Exception("Something went wRoNg"						)

    # 7. Save
    print(F'Saving to {args.hf_checkpoint_name}'						)
    hf_model.save_pretrained(args.hf_checkpoint_name						)
    image_processor.save_pretrained(args.hf_checkpoint_name						)


if __name__ == "__main__":
      main()
      # Run the following to convert checkpoints
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./pretrained_base.pt \
      #          --hf_checkpoint_name "./data2vec-vision-base"
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./finetuned_base.pt \
      #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./pretrained_large.pt \
      #          --hf_checkpoint_name "./data2vec-vision-large"
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./finetuned_large.pt \
      #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"









'''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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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__  :	Optional[Any]            =      32





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_ = 16						):
    UpperCamelCase__	     =      AutoTokenizer.from_pretrained("bert-base-cased"						)
    UpperCamelCase__	     =      load_dataset("glue"       ,	"mrpc"						)

    def tokenize_function(lowerCAmelCase_						):
        # max_length=None => use the model max length (it's actually the default)
        UpperCamelCase__	     =      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():
        UpperCamelCase__	     =      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
    UpperCamelCase__	     =      tokenized_datasets.rename_column("label"       ,	"labels"						)

    def collate_fn(lowerCAmelCase_						):
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            UpperCamelCase__	     =      16
        elif accelerator.mixed_precision != "no":
            UpperCamelCase__	     =      8
        else:
            UpperCamelCase__	     =      None

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

    # Instantiate dataloaders.
    UpperCamelCase__	     =      DataLoader(
        tokenized_datasets["train"]       ,	shuffle=lowerCAmelCase_       ,	collate_fn=lowerCAmelCase_       ,	batch_size=lowerCAmelCase_						)
    UpperCamelCase__	     =      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

      lowercase__  :	Dict            =      mocked_dataloaders  # noqa: F811





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,	lowerCAmelCase_						) == "1":
        UpperCamelCase__	     =      2
    # Initialize accelerator
    UpperCamelCase__	     =      Accelerator(cpu=args.cpu       ,	mixed_precision=args.mixed_precision						)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    UpperCamelCase__	     =      config["lr"]
    UpperCamelCase__	     =      int(config["num_epochs"]						)
    UpperCamelCase__	     =      int(config["seed"]						)
    UpperCamelCase__	     =      int(config["batch_size"]						)

    UpperCamelCase__	     =      evaluate.load("glue"       ,	"mrpc"						)

    # New Code #
    # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
    # half each time. From this, we can calculate the number of gradient accumulation steps needed
    # and modify the Accelerator object as a result
    @find_executable_batch_size(starting_batch_size=int(lowerCAmelCase_						)						)
    def inner_training_loop(lowerCAmelCase_						):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator

        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        UpperCamelCase__	     =      observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        UpperCamelCase__	     =      num_gradient_accumulation_steps

        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()

        # And set the seed so our results are reproducable each reset
        set_seed(lowerCAmelCase_						)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        UpperCamelCase__	     =      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).
        UpperCamelCase__	     =      model.to(accelerator.device						)

        # Instantiate optimizer
        UpperCamelCase__	     =      AdamW(params=model.parameters()       ,	lr=lowerCAmelCase_						)
        UpperCamelCase__     ,	UpperCamelCase__	     =      get_dataloaders(lowerCAmelCase_       ,	lowerCAmelCase_						)

        # Instantiate scheduler
        UpperCamelCase__	     =      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.
        UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      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_						):
                # And perform gradient accumulation
                with accelerator.accumulate(lowerCAmelCase_						):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device						)
                    UpperCamelCase__	     =      model(**lowerCAmelCase_						)
                    UpperCamelCase__	     =      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():
                    UpperCamelCase__	     =      model(**lowerCAmelCase_						)
                UpperCamelCase__	     =      outputs.logits.argmax(dim=-1						)
                UpperCamelCase__     ,	UpperCamelCase__	     =      accelerator.gather_for_metrics((predictions, batch["labels"])						)
                metric.add_batch(
                    predictions=lowerCAmelCase_       ,	references=lowerCAmelCase_       ,	)

            UpperCamelCase__	     =      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     snake_case__			():
    UpperCamelCase__	     =      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."						)
    UpperCamelCase__	     =      parser.parse_args()
    # New Code #
    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
    UpperCamelCase__	     =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
    training_function(lowerCAmelCase_       ,	lowerCAmelCase_						)


if __name__ == "__main__":
      main()




