import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A     :  str					     =       logging.get_logger(__name__)


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  DPTConfig(embedding_type="hybrid"				)

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE_          =  1_0_2_4
            SCREAMING_SNAKE_CASE_          =  4_0_9_6
            SCREAMING_SNAKE_CASE_          =  2_4
            SCREAMING_SNAKE_CASE_          =  1_6
            SCREAMING_SNAKE_CASE_          =  [5, 1_1, 1_7, 2_3]
            SCREAMING_SNAKE_CASE_          =  [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
            SCREAMING_SNAKE_CASE_          =  (1, 3_8_4, 3_8_4)

      if "nyu" or "midas" in checkpoint_url:
            SCREAMING_SNAKE_CASE_          =  7_6_8
            SCREAMING_SNAKE_CASE_          =  [1, 1, 1, 0.5]
            SCREAMING_SNAKE_CASE_          =  [2_5_6, 5_1_2, 7_6_8, 7_6_8]
            SCREAMING_SNAKE_CASE_          =  1_5_0
            SCREAMING_SNAKE_CASE_          =  1_6
            SCREAMING_SNAKE_CASE_          =  (1, 3_8_4, 3_8_4)
            SCREAMING_SNAKE_CASE_          =  False
            SCREAMING_SNAKE_CASE_          =  "project"

      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE_          =  True
            SCREAMING_SNAKE_CASE_          =  7_6_8
            SCREAMING_SNAKE_CASE_          =  [1, 1, 1, 0.5]
            SCREAMING_SNAKE_CASE_          =  1_5_0
            SCREAMING_SNAKE_CASE_          =  1_6
            SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
            SCREAMING_SNAKE_CASE_          =  "ade20k-id2label.json"
            SCREAMING_SNAKE_CASE_          =  json.load(open(cached_download(hf_hub_url(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)				)     ,      "r"				)				)
            SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_          =  idalabel
            SCREAMING_SNAKE_CASE_          =  {v: k for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_          =  [1, 1_5_0, 4_8_0, 4_8_0]

      return config, expected_shape


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  ["pretrained.model.head.weight", "pretrained.model.head.bias"]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase     ,      __UpperCamelCase				)


def      a__ (		__UpperCamelCase				):
      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.model"     ,      "dpt.encoder"				)
      if "pretrained.model" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.model"     ,      "dpt.embeddings"				)
      if "patch_embed" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("patch_embed"     ,      ""				)
      if "pos_embed" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pos_embed"     ,      "position_embeddings"				)
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("attn.proj"     ,      "attention.output.dense"				)
      if "proj" in name and "project" not in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("proj"     ,      "projection"				)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("blocks"     ,      "layer"				)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("mlp.fc1"     ,      "intermediate.dense"				)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("mlp.fc2"     ,      "output.dense"				)
      if "norm1" in name and "backbone" not in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("norm1"     ,      "layernorm_before"				)
      if "norm2" in name and "backbone" not in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("norm2"     ,      "layernorm_after"				)
      if "scratch.output_conv" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("scratch.output_conv"     ,      "head"				)
      if "scratch" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("scratch"     ,      "neck"				)
      if "layer1_rn" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("layer1_rn"     ,      "convs.0"				)
      if "layer2_rn" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("layer2_rn"     ,      "convs.1"				)
      if "layer3_rn" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("layer3_rn"     ,      "convs.2"				)
      if "layer4_rn" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("layer4_rn"     ,      "convs.3"				)
      if "refinenet" in name:
            SCREAMING_SNAKE_CASE_          =  int(name[len("neck.refinenet"				) : len("neck.refinenet"				) + 1]				)
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            SCREAMING_SNAKE_CASE_          =  name.replace(F'''refinenet{layer_idx}'''     ,      F'''fusion_stage.layers.{abs(layer_idx-4				)}'''				)
      if "out_conv" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("out_conv"     ,      "projection"				)
      if "resConfUnit1" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("resConfUnit1"     ,      "residual_layer1"				)
      if "resConfUnit2" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("resConfUnit2"     ,      "residual_layer2"				)
      if "conv1" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("conv1"     ,      "convolution1"				)
      if "conv2" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("conv2"     ,      "convolution2"				)
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess1.0.project.0"     ,      "neck.reassemble_stage.readout_projects.0.0"				)
      if "pretrained.act_postprocess2.0.project.0" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess2.0.project.0"     ,      "neck.reassemble_stage.readout_projects.1.0"				)
      if "pretrained.act_postprocess3.0.project.0" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess3.0.project.0"     ,      "neck.reassemble_stage.readout_projects.2.0"				)
      if "pretrained.act_postprocess4.0.project.0" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess4.0.project.0"     ,      "neck.reassemble_stage.readout_projects.3.0"				)

      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess1.3"     ,      "neck.reassemble_stage.layers.0.projection"				)
      if "pretrained.act_postprocess1.4" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess1.4"     ,      "neck.reassemble_stage.layers.0.resize"				)
      if "pretrained.act_postprocess2.3" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess2.3"     ,      "neck.reassemble_stage.layers.1.projection"				)
      if "pretrained.act_postprocess2.4" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess2.4"     ,      "neck.reassemble_stage.layers.1.resize"				)
      if "pretrained.act_postprocess3.3" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess3.3"     ,      "neck.reassemble_stage.layers.2.projection"				)
      if "pretrained.act_postprocess4.3" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess4.3"     ,      "neck.reassemble_stage.layers.3.projection"				)
      if "pretrained.act_postprocess4.4" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained.act_postprocess4.4"     ,      "neck.reassemble_stage.layers.3.resize"				)
      if "pretrained" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("pretrained"     ,      "dpt"				)
      if "bn" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("bn"     ,      "batch_norm"				)
      if "head" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("head"     ,      "head.head"				)
      if "encoder.norm" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("encoder.norm"     ,      "layernorm"				)
      if "auxlayer" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("auxlayer"     ,      "auxiliary_head.head"				)
      if "backbone" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("backbone"     ,      "backbone.bit.encoder"				)

      if ".." in name:
            SCREAMING_SNAKE_CASE_          =  name.replace(".."     ,      "."				)

      if "stem.conv" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("stem.conv"     ,      "bit.embedder.convolution"				)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("blocks"     ,      "layers"				)
      if "convolution" in name and "backbone" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("convolution"     ,      "conv"				)
      if "layer" in name and "backbone" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("layer"     ,      "layers"				)
      if "backbone.bit.encoder.bit" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("backbone.bit.encoder.bit"     ,      "backbone.bit"				)
      if "embedder.conv" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("embedder.conv"     ,      "embedder.convolution"				)
      if "backbone.bit.encoder.stem.norm" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("backbone.bit.encoder.stem.norm"     ,      "backbone.bit.embedder.norm"				)
      return name


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      for i in range(config.num_hidden_layers				):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE_          =  state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''				)
            SCREAMING_SNAKE_CASE_          =  state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''				)
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE_          =  in_proj_weight[: config.hidden_size, :]
            SCREAMING_SNAKE_CASE_          =  in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE_          =  in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_          =  in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE_          =  in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_          =  in_proj_bias[-config.hidden_size :]


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__UpperCamelCase     ,      stream=__UpperCamelCase				).raw				)
      return im


@torch.no_grad()
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_dpt_config(__UpperCamelCase				)
      # load original state_dict from URL
      # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
      SCREAMING_SNAKE_CASE_          =  torch.load(__UpperCamelCase     ,      map_location="cpu"				)
      # remove certain keys
      remove_ignore_keys_(__UpperCamelCase				)
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_          =  state_dict.pop(__UpperCamelCase				)
            SCREAMING_SNAKE_CASE_          =  val
      # read in qkv matrices
      read_in_q_k_v(__UpperCamelCase     ,      __UpperCamelCase				)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_          =  DPTForSemanticSegmentation(__UpperCamelCase				) if "ade" in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase				)
      model.load_state_dict(__UpperCamelCase				)
      model.eval()

      # Check outputs on an image
      SCREAMING_SNAKE_CASE_          =  4_8_0 if "ade" in checkpoint_url else 3_8_4
      SCREAMING_SNAKE_CASE_          =  DPTImageProcessor(size=__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  prepare_img()
      SCREAMING_SNAKE_CASE_          =  image_processor(__UpperCamelCase     ,      return_tensors="pt"				)

      # forward pass
      SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				).logits if "ade" in checkpoint_url else model(**__UpperCamelCase				).predicted_depth

      if show_prediction:
            SCREAMING_SNAKE_CASE_          =  (
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1				)     ,      size=(image.size[1], image.size[0])     ,      mode="bicubic"     ,      align_corners=__UpperCamelCase     ,      )
                .squeeze()
                .cpu()
                .numpy()
            )

            Image.fromarray((prediction / prediction.max()) * 2_5_5				).show()

      if pytorch_dump_folder_path is not None:
            Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
            print(F'''Saving model to {pytorch_dump_folder_path}'''				)
            model.save_pretrained(__UpperCamelCase				)
            print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
            image_processor.save_pretrained(__UpperCamelCase				)

      if push_to_hub:
            model.push_to_hub("ybelkada/dpt-hybrid-midas"				)
            image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"				)


if __name__ == "__main__":
      A     :  Tuple					     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint_url",
          default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
          type=str,
          help="URL of the original DPT checkpoint you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=False,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
      )
      parser.add_argument(
          "--model_name",
          default="dpt-large",
          type=str,
          help="Name of the model, in case you're pushing to the hub.",
      )
      parser.add_argument(
          "--show_prediction",
          action="store_true",
      )

      A     :  Any					     =       parser.parse_args()
      convert_dpt_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
      )



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
# and perform 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)
#
# 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
#
########################################################################


A     :  Optional[Any]					     =       16
A     :  Optional[Any]					     =       32


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 1_6				):
      SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained("bert-base-cased"				)
      SCREAMING_SNAKE_CASE_          =  load_dataset("glue"     ,      "mrpc"				)

      def tokenize_function(__UpperCamelCase				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_          =  tokenizer(examples["sentence1"]     ,      examples["sentence2"]     ,      truncation=__UpperCamelCase     ,      max_length=__UpperCamelCase				)
            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_          =  datasets.map(
                __UpperCamelCase     ,      batched=__UpperCamelCase     ,      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_          =  tokenized_datasets.rename_column("label"     ,      "labels"				)

      def collate_fn(__UpperCamelCase				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_          =  1_2_8 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_          =  1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_          =  8
            else:
                  SCREAMING_SNAKE_CASE_          =  None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_          =  DataLoader(
          tokenized_datasets["train"]     ,      shuffle=__UpperCamelCase     ,      collate_fn=__UpperCamelCase     ,      batch_size=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  DataLoader(
          tokenized_datasets["validation"]     ,      shuffle=__UpperCamelCase     ,      collate_fn=__UpperCamelCase     ,      batch_size=__UpperCamelCase				)

      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

      A     :  Tuple					     =       mocked_dataloaders  # noqa: F811


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      __UpperCamelCase				) == "1":
            SCREAMING_SNAKE_CASE_          =  2
      # New Code #
      SCREAMING_SNAKE_CASE_          =  int(args.gradient_accumulation_steps				)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE_          =  Accelerator(
          cpu=args.cpu     ,      mixed_precision=args.mixed_precision     ,      gradient_accumulation_steps=__UpperCamelCase				)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_          =  config["lr"]
      SCREAMING_SNAKE_CASE_          =  int(config["num_epochs"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["seed"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["batch_size"]				)

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

      set_seed(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_dataloaders(__UpperCamelCase     ,      __UpperCamelCase				)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE_          =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"     ,      return_dict=__UpperCamelCase				)

      # 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_          =  model.to(accelerator.device				)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE_          =  AdamW(params=model.parameters()     ,      lr=__UpperCamelCase				)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE_          =  get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase     ,      num_warmup_steps=1_0_0     ,      num_training_steps=(len(__UpperCamelCase				) * 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_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.prepare(
          __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # Now we train the model
      for epoch in range(__UpperCamelCase				):
            model.train()
            for step, batch in enumerate(__UpperCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                        SCREAMING_SNAKE_CASE_          =  output.loss
                        accelerator.backward(__UpperCamelCase				)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__UpperCamelCase				):
                  # 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_          =  model(**__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  outputs.logits.argmax(dim=-1				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
                  metric.add_batch(
                      predictions=__UpperCamelCase     ,      references=__UpperCamelCase     ,      )

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


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  argparse.ArgumentParser(description="Simple example of training script."				)
      parser.add_argument(
          "--mixed_precision"     ,      type=__UpperCamelCase     ,      default=__UpperCamelCase     ,      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."     ,      )
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps"     ,      type=__UpperCamelCase     ,      default=1     ,      help="The number of minibatches to be ran before gradients are accumulated."     ,      )
      parser.add_argument("--cpu"     ,      action="store_true"     ,      help="If passed, will train on the CPU."				)
      SCREAMING_SNAKE_CASE_          =  parser.parse_args()
      SCREAMING_SNAKE_CASE_          =  {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__UpperCamelCase     ,      __UpperCamelCase				)


if __name__ == "__main__":
      main()


