code: 
  
  
  import argparse
  import os
  import re
  
  import numpy as np
  import PIL
  import torch
  from timm import create_model
  from torch.optim.lr_scheduler import OneCycleLR
  from torch.utils.data import DataLoader, Dataset
  from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
  
  from accelerate import Accelerator
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		fname.split(os.path.sep      )[-1]
  						return re.search(r"""^(.*)_\d+\.jpg$"""       ,    UpperCAmelCase__      ).groups()[0]
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		file_names
  													a_					=		image_transform
  													a_					=		label_to_id
  
  
  							def __len__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return len(self.file_names		)
  
  
  
  
  
  
  							def __getitem__(   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.file_names[idx]
  													a_					=		PIL.Image.open(_UpperCAmelCase		)
  													a_					=		raw_image.convert("""RGB"""		)
  													if self.image_transform is not None:
  																			a_					=		self.image_transform(_UpperCAmelCase		)
  													a_					=		extract_label(_UpperCAmelCase		)
  													if self.label_to_id is not None:
  																			a_					=		self.label_to_id[label]
  													return {"image": image, "label": label}
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		Accelerator(cpu=args.cpu       ,    mixed_precision=args.mixed_precision      )
  
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						a_					=		config["""lr"""]
  						a_					=		int(config["""num_epochs"""]      )
  						a_					=		int(config["""seed"""]      )
  						a_					=		int(config["""batch_size"""]      )
  						a_					=		config["""image_size"""]
  						if not isinstance(UpperCAmelCase__       ,    (list, tuple)      ):
  												a_					=		(image_size, image_size)
  
  						# Grab all the image filenames
  						a_					=		[os.path.join(args.data_dir       ,    UpperCAmelCase__      ) for fname in os.listdir(args.data_dir      ) if fname.endswith(""".jpg"""      )]
  
  						# Build the label correspondences
  						a_					=		[extract_label(UpperCAmelCase__      ) for fname in file_names]
  						a_					=		list(set(UpperCAmelCase__      )      )
  						id_to_label.sort()
  						a_					=		{lbl: i for i, lbl in enumerate(UpperCAmelCase__      )}
  
  						# Set the seed before splitting the data.
  						np.random.seed(UpperCAmelCase__      )
  						torch.manual_seed(UpperCAmelCase__      )
  						torch.cuda.manual_seed_all(UpperCAmelCase__      )
  
  						# Split our filenames between train and validation
  						a_					=		np.random.permutation(len(UpperCAmelCase__      )      )
  						a_					=		int(0.8 * len(UpperCAmelCase__      )      )
  						a_					=		random_perm[:cut]
  						a_					=		random_perm[cut:]
  
  						# For training we use a simple RandomResizedCrop
  						a_					=		Compose([RandomResizedCrop(UpperCAmelCase__       ,    scale=(0.5, 1.0)      ), ToTensor()]      )
  						a_					=		PetsDataset(
  						    [file_names[i] for i in train_split]       ,    image_transform=UpperCAmelCase__       ,    label_to_id=UpperCAmelCase__      )
  
  						# For evaluation, we use a deterministic Resize
  						a_					=		Compose([Resize(UpperCAmelCase__      ), ToTensor()]      )
  						a_					=		PetsDataset([file_names[i] for i in eval_split]       ,    image_transform=UpperCAmelCase__       ,    label_to_id=UpperCAmelCase__      )
  
  						# Instantiate dataloaders.
  						a_					=		DataLoader(UpperCAmelCase__       ,    shuffle=UpperCAmelCase__       ,    batch_size=UpperCAmelCase__       ,    num_workers=4      )
  						a_					=		DataLoader(UpperCAmelCase__       ,    shuffle=UpperCAmelCase__       ,    batch_size=UpperCAmelCase__       ,    num_workers=4      )
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						a_					=		create_model("""resnet50d"""       ,    pretrained=UpperCAmelCase__       ,    num_classes=len(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).
  						a_					=		model.to(accelerator.device      )
  
  						# Freezing the base model
  						for param in model.parameters():
  												a_					=		False
  						for param in model.get_classifier().parameters():
  												a_					=		True
  
  						# We normalize the batches of images to be a bit faster.
  						a_					=		torch.tensor(model.default_cfg["""mean"""]      )[None, :, None, None].to(accelerator.device      )
  						a_					=		torch.tensor(model.default_cfg["""std"""]      )[None, :, None, None].to(accelerator.device      )
  
  						# Instantiate optimizer
  						a_					=		torch.optim.Adam(params=model.parameters()       ,    lr=lr / 25      )
  
  						# Instantiate learning rate scheduler
  						a_					=		OneCycleLR(optimizer=UpperCAmelCase__       ,    max_lr=UpperCAmelCase__       ,    epochs=UpperCAmelCase__       ,    steps_per_epoch=len(UpperCAmelCase__      )      )
  
  						# 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.
  						a_			,   a_			,   a_			,   a_			,   a_					=		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`.
  																		a_					=		{k: v.to(accelerator.device      ) for k, v in batch.items()}
  																		a_					=		(batch["""image"""] - mean) / std
  																		a_					=		model(UpperCAmelCase__      )
  																		a_					=		torch.nn.functional.cross_entropy(UpperCAmelCase__       ,    batch["""label"""]      )
  																		accelerator.backward(UpperCAmelCase__      )
  																		optimizer.step()
  																		lr_scheduler.step()
  																		optimizer.zero_grad()
  
  												model.eval()
  												a_					=		0
  												a_					=		0
  												for _, batch in enumerate(UpperCAmelCase__      ):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		a_					=		{k: v.to(accelerator.device      ) for k, v in batch.items()}
  																		a_					=		(batch["""image"""] - mean) / std
  																		with torch.no_grad():
  																								a_					=		model(UpperCAmelCase__      )
  																		a_					=		outputs.argmax(dim=-1      )
  																		a_			,   a_					=		accelerator.gather_for_metrics((predictions, batch["""label"""])      )
  																		a_					=		predictions == references
  																		num_elems += accurate_preds.shape[0]
  																		accurate += accurate_preds.long().sum()
  
  												a_					=		accurate.item() / num_elems
  												# Use accelerator.print to print only on the main process.
  												accelerator.print(F"epoch {epoch}: {100 * eval_metric:.2f}"      )
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		argparse.ArgumentParser(description="""Simple example of training script."""      )
  						parser.add_argument("""--data_dir"""       ,    required=UpperCAmelCase__       ,    help="""The data folder on disk."""      )
  						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."""       ,    )
  						parser.add_argument(
  						    """--checkpointing_steps"""       ,    type=UpperCAmelCase__       ,    default=UpperCAmelCase__       ,    help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""       ,    )
  						parser.add_argument("""--cpu"""       ,    action="""store_true"""       ,    help="""If passed, will train on the CPU."""      )
  						a_					=		parser.parse_args()
  						a_					=		{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
  						training_function(UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  
  if __name__ == "__main__":
  	main()
style_context: 
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		BartTokenizer
  							snake_case_        =		BartTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		filter_roberta_detectors
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  													a_					=		[
  													    """l""",
  													    """o""",
  													    """w""",
  													    """e""",
  													    """r""",
  													    """s""",
  													    """t""",
  													    """i""",
  													    """d""",
  													    """n""",
  													    """\u0120""",
  													    """\u0120l""",
  													    """\u0120n""",
  													    """\u0120lo""",
  													    """\u0120low""",
  													    """er""",
  													    """\u0120lowest""",
  													    """\u0120newer""",
  													    """\u0120wider""",
  													    """<unk>""",
  													]
  													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
  													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
  													a_					=		{"""unk_token""": """<unk>"""}
  
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""merges_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write(json.dumps(_UpperCAmelCase		) + """\n"""		)
  													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write("""\n""".join(_UpperCAmelCase		)		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return "lower newer", "lower newer"
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizer.from_pretrained("""facebook/bart-large"""		)
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizerFast.from_pretrained("""facebook/bart-large"""		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													a_					=		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    max_length=len(_UpperCAmelCase		)			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																			self.assertEqual((2, 9)			,    batch.input_ids.shape		)
  																			self.assertEqual((2, 9)			,    batch.attention_mask.shape		)
  																			a_					=		batch.input_ids.tolist()[0]
  																			self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			# Test that special tokens are reset
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			# check if input_ids are returned and no labels
  																			self.assertIn("""input_ids"""			,    _UpperCAmelCase		)
  																			self.assertIn("""attention_mask"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""labels"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""decoder_attention_mask"""			,    _UpperCAmelCase		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		[
  													    """Summary of the text.""",
  													    """Another summary.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    max_length=32			,    padding="""max_length"""			,    return_tensors="""pt"""		)
  																			self.assertEqual(32			,    targets["""input_ids"""].shape[1]		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(
  																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			self.assertEqual(batch.input_ids.shape			,    (2, 1_024)		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization."""]
  													a_					=		[
  													    """Summary of the text.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		inputs["""input_ids"""]
  																			a_					=		targets["""input_ids"""]
  																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
  																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													pass
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
  																									a_					=		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		self.tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		"""A, <mask> AllenNLP sentence."""
  																									a_					=		tokenizer_r.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  																									a_					=		tokenizer_p.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  
  																									# token_type_ids should put 0 everywhere
  																									self.assertEqual(sum(tokens_r["""token_type_ids"""]		)			,    sum(tokens_p["""token_type_ids"""]		)		)
  
  																									# attention_mask should put 1 everywhere, so sum over length should be 1
  																									self.assertEqual(
  																									    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)			,    sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)			,    )
  
  																									a_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
  																									a_					=		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)
  
  																									# Rust correctly handles the space before the mask while python doesnt
  																									self.assertSequenceEqual(tokens_p["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  																									self.assertSequenceEqual(tokens_r["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)