






"""simple docstring"""



import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :  Dict   =			logging.get_logger(__name__)


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   str							)->		Tuple:






					'''simple docstring'''



					UpperCAmelCase__	:						Optional[Any]       =       ConvNextVaConfig()

					if "atto" in checkpoint_url:
										UpperCAmelCase__	:						int       =       [2, 2, 6, 2]
										UpperCAmelCase__	:						int       =       [40, 80, 160, 320]
					if "femto" in checkpoint_url:
										UpperCAmelCase__	:						Dict       =       [2, 2, 6, 2]
										UpperCAmelCase__	:						Optional[Any]       =       [48, 96, 192, 384]
					if "pico" in checkpoint_url:
										UpperCAmelCase__	:						Any       =       [2, 2, 6, 2]
										UpperCAmelCase__	:						List[Any]       =       [64, 128, 256, 512]
					if "nano" in checkpoint_url:
										UpperCAmelCase__	:						Tuple       =       [2, 2, 8, 2]
										UpperCAmelCase__	:						List[Any]       =       [80, 160, 320, 640]
					if "tiny" in checkpoint_url:
										UpperCAmelCase__	:						Tuple       =       [3, 3, 9, 3]
										UpperCAmelCase__	:						Union[str, Any]       =       [96, 192, 384, 768]
					if "base" in checkpoint_url:
										UpperCAmelCase__	:						Dict       =       [3, 3, 27, 3]
										UpperCAmelCase__	:						int       =       [128, 256, 512, 1024]
					if "large" in checkpoint_url:
										UpperCAmelCase__	:						Optional[Any]       =       [3, 3, 27, 3]
										UpperCAmelCase__	:						Optional[int]       =       [192, 384, 768, 1536]
					if "huge" in checkpoint_url:
										UpperCAmelCase__	:						int       =       [3, 3, 27, 3]
										UpperCAmelCase__	:						Tuple       =       [352, 704, 1408, 2816]

					UpperCAmelCase__	:						List[str]       =       1000
					UpperCAmelCase__	:						List[Any]       =       "imagenet-1k-id2label.json"
					UpperCAmelCase__	:						Dict       =       (1, 1000)

					UpperCAmelCase__	:						List[Any]       =       "huggingface/label-files"
					UpperCAmelCase__	:						str       =       num_labels
					UpperCAmelCase__	:						List[str]       =       json.load(open(hf_hub_download(snake_case ,				snake_case ,				repo_type="dataset"							) ,				"r"							)							)
					UpperCAmelCase__	:						Tuple       =       {int(snake_case							): v for k, v in idalabel.items()}

					UpperCAmelCase__	:						Optional[Any]       =       idalabel
					UpperCAmelCase__	:						int       =       {v: k for k, v in idalabel.items()}
					UpperCAmelCase__	:						Dict       =       hidden_sizes
					UpperCAmelCase__	:						Optional[Any]       =       depths

					return config, expected_shape


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[int]							)->		Any:






					'''simple docstring'''



					if "downsample_layers.0.0" in name:
										UpperCAmelCase__	:						Union[str, Any]       =       name.replace("downsample_layers.0.0" ,				"embeddings.patch_embeddings"							)
					if "downsample_layers.0.1" in name:
										UpperCAmelCase__	:						str       =       name.replace("downsample_layers.0.1" ,				"embeddings.norm"							)  # we rename to layernorm later on
					if "downsample_layers.1.0" in name:
										UpperCAmelCase__	:						Dict       =       name.replace("downsample_layers.1.0" ,				"stages.1.downsampling_layer.0"							)
					if "downsample_layers.1.1" in name:
										UpperCAmelCase__	:						List[Any]       =       name.replace("downsample_layers.1.1" ,				"stages.1.downsampling_layer.1"							)
					if "downsample_layers.2.0" in name:
										UpperCAmelCase__	:						Optional[int]       =       name.replace("downsample_layers.2.0" ,				"stages.2.downsampling_layer.0"							)
					if "downsample_layers.2.1" in name:
										UpperCAmelCase__	:						Optional[int]       =       name.replace("downsample_layers.2.1" ,				"stages.2.downsampling_layer.1"							)
					if "downsample_layers.3.0" in name:
										UpperCAmelCase__	:						List[Any]       =       name.replace("downsample_layers.3.0" ,				"stages.3.downsampling_layer.0"							)
					if "downsample_layers.3.1" in name:
										UpperCAmelCase__	:						List[Any]       =       name.replace("downsample_layers.3.1" ,				"stages.3.downsampling_layer.1"							)
					if "stages" in name and "downsampling_layer" not in name:
										# stages.0.0. for instance should be renamed to stages.0.layers.0.
										UpperCAmelCase__	:						int       =       name[: len("stages.0"							)] + ".layers" + name[len("stages.0"							) :]
					if "gamma" in name:
										UpperCAmelCase__	:						Optional[Any]       =       name.replace("gamma" ,				"weight"							)
					if "beta" in name:
										UpperCAmelCase__	:						Dict       =       name.replace("beta" ,				"bias"							)
					if "stages" in name:
										UpperCAmelCase__	:						Optional[Any]       =       name.replace("stages" ,				"encoder.stages"							)
					if "norm" in name:
										UpperCAmelCase__	:						Tuple       =       name.replace("norm" ,				"layernorm"							)
					if "head" in name:
										UpperCAmelCase__	:						Dict       =       name.replace("head" ,				"classifier"							)

					return name


def   SCREAMING_SNAKE_CASE__					(     )->		Tuple:






					'''simple docstring'''



					UpperCAmelCase__	:						List[str]       =       "http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCAmelCase__	:						Dict       =       Image.open(requests.get(snake_case ,				stream=snake_case							).raw							)
					return im


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[Any]							)->		str:






					'''simple docstring'''



					if "224" in checkpoint_url:
										UpperCAmelCase__	:						Optional[int]       =       224
										UpperCAmelCase__	:						str       =       224 / 256
					elif "384" in checkpoint_url:
										UpperCAmelCase__	:						Tuple       =       384
										UpperCAmelCase__	:						Tuple       =       None
					else:
										UpperCAmelCase__	:						Optional[Any]       =       512
										UpperCAmelCase__	:						int       =       None

					return ConvNextImageProcessor(
					    size=snake_case ,				crop_pct=snake_case ,				image_mean=[0.485, 0.456, 0.406] ,				image_std=[0.229, 0.224, 0.225] ,				resample=PILImageResampling.BICUBIC ,				)


@torch.no_grad()
def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Tuple ,				snake_case					:   Tuple ,				snake_case					:   List[str] ,				snake_case					:   Union[str, Any]							)->		Optional[int]:






					'''simple docstring'''



					print("Downloading original model from checkpoint..."							)
					# define ConvNeXTV2 configuration based on URL
					UpperCAmelCase__	:						List[Any]       =       get_convnextva_config(snake_case							)
					# load original state_dict from URL
					UpperCAmelCase__	:						Optional[int]       =       torch.hub.load_state_dict_from_url(snake_case							)["model"]

					print("Converting model parameters..."							)
					# rename keys
					for key in state_dict.copy().keys():
										UpperCAmelCase__	:						List[Any]       =       state_dict.pop(snake_case							)
										UpperCAmelCase__	:						Optional[int]       =       val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										UpperCAmelCase__	:						Optional[Any]       =       state_dict.pop(snake_case							)
										if not key.startswith("classifier"							):
															UpperCAmelCase__	:						str       =       "convnextv2." + key
										UpperCAmelCase__	:						List[str]       =       val

					# load HuggingFace model
					UpperCAmelCase__	:						List[str]       =       ConvNextVaForImageClassification(snake_case							)
					model.load_state_dict(snake_case							)
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					UpperCAmelCase__	:						Optional[Any]       =       convert_preprocessor(snake_case							)
					UpperCAmelCase__	:						Any       =       preprocessor(images=prepare_img() ,				return_tensors="pt"							)
					UpperCAmelCase__	:						Dict       =       model(**snake_case							).logits

					# note: the logits below were obtained without center cropping
					if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
										UpperCAmelCase__	:						Dict       =       torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
										UpperCAmelCase__	:						List[Any]       =       torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
										UpperCAmelCase__	:						int       =       torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
										UpperCAmelCase__	:						str       =       torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
										UpperCAmelCase__	:						Optional[int]       =       torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
										UpperCAmelCase__	:						Dict       =       torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
										UpperCAmelCase__	:						Union[str, Any]       =       torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
										UpperCAmelCase__	:						List[Any]       =       torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
										UpperCAmelCase__	:						Union[str, Any]       =       torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
										UpperCAmelCase__	:						str       =       torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
										UpperCAmelCase__	:						Optional[Any]       =       torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
										UpperCAmelCase__	:						List[Any]       =       torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
										UpperCAmelCase__	:						List[str]       =       torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
										UpperCAmelCase__	:						Tuple       =       torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
										UpperCAmelCase__	:						List[Any]       =       torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
										UpperCAmelCase__	:						str       =       torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
										UpperCAmelCase__	:						int       =       torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]							)
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
										UpperCAmelCase__	:						Union[str, Any]       =       torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]							)
					else:
										raise ValueError(f'Unknown URL: {checkpoint_url}'							)

					assert torch.allclose(logits[0, :5] ,				snake_case ,				atol=1E-3							)
					assert logits.shape == expected_shape
					print("Model outputs match the original results!"							)

					if save_model:
										print("Saving model to local..."							)
										# Create folder to save model
										if not os.path.isdir(snake_case							):
															os.mkdir(snake_case							)

										model.save_pretrained(snake_case							)
										preprocessor.save_pretrained(snake_case							)

					UpperCAmelCase__	:						Optional[Any]       =       "convnextv2"
					if "atto" in checkpoint_url:
										model_name += "-atto"
					if "femto" in checkpoint_url:
										model_name += "-femto"
					if "pico" in checkpoint_url:
										model_name += "-pico"
					if "nano" in checkpoint_url:
										model_name += "-nano"
					elif "tiny" in checkpoint_url:
										model_name += "-tiny"
					elif "base" in checkpoint_url:
										model_name += "-base"
					elif "large" in checkpoint_url:
										model_name += "-large"
					elif "huge" in checkpoint_url:
										model_name += "-huge"
					if "22k" in checkpoint_url and "1k" not in checkpoint_url:
										model_name += "-22k"
					elif "22k" in checkpoint_url and "1k" in checkpoint_url:
										model_name += "-22k-1k"
					elif "1k" in checkpoint_url:
										model_name += "-1k"
					if "224" in checkpoint_url:
										model_name += "-224"
					elif "384" in checkpoint_url:
										model_name += "-384"
					elif "512" in checkpoint_url:
										model_name += "-512"

					if push_to_hub:
										print(f'Pushing {model_name} to the hub...'							)
										model.push_to_hub(snake_case							)
										preprocessor.push_to_hub(snake_case							)


if __name__ == "__main__":
	_lowerCAmelCase  :  List[str]   =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt""",
	    type=str,
	    help="""URL of the original ConvNeXTV2 checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""model""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
	parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image preprocessor to the hub""")

	_lowerCAmelCase  :  Tuple   =			parser.parse_args()
	convert_convnextva_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
	)










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

_lowerCAmelCase  :  int   =			32


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Accelerator ,				snake_case					:   int = 16							)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__	:						Dict       =       AutoTokenizer.from_pretrained("bert-base-cased"							)
     UpperCAmelCase__	:						Union[str, Any]       =       load_dataset("glue" ,				"mrpc"							)

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

     def collate_fn(snake_case					:   int							):
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               UpperCAmelCase__	:						Optional[Any]       =       16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase__	:						int       =       8
          else:
               UpperCAmelCase__	:						Union[str, Any]       =       None

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

     # Instantiate dataloaders.
     UpperCAmelCase__	:						int       =       DataLoader(
         tokenized_datasets["train"] ,				shuffle=snake_case ,				collate_fn=snake_case ,				batch_size=snake_case							)
     UpperCAmelCase__	:						Optional[int]       =       DataLoader(
         tokenized_datasets["validation"] ,				shuffle=snake_case ,				collate_fn=snake_case ,				batch_size=snake_case							)

     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

 _lowerCAmelCase  :  Optional[int]   =			mocked_dataloaders  # noqa: F811


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[str] ,				snake_case					:   Tuple							)->		Optional[int]:






     '''simple docstring'''



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

     UpperCAmelCase__	:						Dict       =       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(snake_case							)							)
     def inner_training_loop(snake_case					:   Optional[int]							):
          # 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__	:						int       =       observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          UpperCAmelCase__	:						List[Any]       =       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(snake_case							)

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

          # 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__	:						Tuple       =       model.to(accelerator.device							)

          # Instantiate optimizer
          UpperCAmelCase__	:						str       =       AdamW(params=model.parameters() ,				lr=snake_case							)
          UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       get_dataloaders(snake_case ,				snake_case							)

          # Instantiate scheduler
          UpperCAmelCase__	:						Dict       =       get_linear_schedule_with_warmup(
              optimizer=snake_case ,				num_warmup_steps=100 ,				num_training_steps=(len(snake_case							) * 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__	:						Union[str, Any]       =       accelerator.prepare(
              snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case							)

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

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

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

    # 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   SCREAMING_SNAKE_CASE__					(     )->		str:






     '''simple docstring'''



     UpperCAmelCase__	:						str       =       argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision" ,				type=snake_case ,				default=snake_case ,				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__	:						str       =       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__	:						Union[str, Any]       =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
     training_function(snake_case ,				snake_case							)


if __name__ == "__main__":
 main()


