import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor

__UpperCamelCase						:			List[str]     		=	random.Random()
def  A      (       _lowercase						,    _lowercase=1.0						,    _lowercase=None						,    _lowercase=None		):
						if rng is None:
												SCREAMING_SNAKE_CASE				:       Dict     							=				global_rng

						SCREAMING_SNAKE_CASE				:       int     							=				[]
						for batch_idx in range(shape[0]		):
												values.append([]		)
												for _ in range(shape[1]		):
																		values[-1].append(rng.random() * scale		)

						return values





@require_torch
@require_torchaudio
class    lowercase__		(  unittest.TestCase):


						def __init__(			self      :							int       ,						UpperCamelCase__      :							Any       ,						UpperCamelCase__      :							Optional[int]=7       ,						UpperCamelCase__      :							int=400       ,						UpperCamelCase__      :							Optional[Any]=2000       ,						UpperCamelCase__      :							Union[str, Any]=24       ,						UpperCamelCase__      :							List[Any]=24       ,						UpperCamelCase__      :							Dict=0.0       ,						UpperCamelCase__      :							List[str]=1_6000       ,						UpperCamelCase__      :							str=True       ,						UpperCamelCase__      :							int=True       ,						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Any     							=				parent
												SCREAMING_SNAKE_CASE				:       List[str]     							=				batch_size
												SCREAMING_SNAKE_CASE				:       str     							=				min_seq_length
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				max_seq_length
												SCREAMING_SNAKE_CASE				:       str     							=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				feature_size
												SCREAMING_SNAKE_CASE				:       Dict     							=				num_mel_bins
												SCREAMING_SNAKE_CASE				:       Dict     							=				padding_value
												SCREAMING_SNAKE_CASE				:       Tuple     							=				sampling_rate
												SCREAMING_SNAKE_CASE				:       Tuple     							=				return_attention_mask
												SCREAMING_SNAKE_CASE				:       List[Any]     							=				do_normalize


						def 			__A			(			self      :							Dict		):





												'''simple docstring'''





												return {
												    "feature_size": self.feature_size,
												    "num_mel_bins": self.num_mel_bins,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												    "do_normalize": self.do_normalize,
												}



						def 			__A			(			self      :							Union[str, Any]       ,						UpperCamelCase__      :							Dict=False       ,						UpperCamelCase__      :							Dict=False		):





												'''simple docstring'''





												def _flatten(UpperCamelCase__      :							Optional[Any]		):
																		return list(itertools.chain(*_lowercase		)		)

												if equal_length:
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
												else:
																		# make sure that inputs increase in size
																		SCREAMING_SNAKE_CASE				:       Tuple     							=				[
																		    floats_list((x, self.feature_size)		)
																		    for x in range(self.min_seq_length       ,						self.max_seq_length       ,						self.seq_length_diff		)
																		]
												if numpify:
																		SCREAMING_SNAKE_CASE				:       str     							=				[np.asarray(_lowercase		) for x in speech_inputs]
												return speech_inputs






@require_torch
@require_torchaudio
class    lowercase__		(  __a      ,				unittest.TestCase):
						UpperCamelCase_	=						SpeechaTextFeatureExtractor if is_speech_available() else None


						def 			__A			(			self      :							Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				SpeechaTextFeatureExtractionTester(self		)


						def 			__A			(			self      :							Dict       ,						UpperCamelCase__      :							int		):





												'''simple docstring'''





												self.assertTrue(np.all(np.mean(_lowercase       ,						axis=0		) < 1E-3		)		)
												self.assertTrue(np.all(np.abs(np.var(_lowercase       ,						axis=0		) - 1		) < 1E-3		)		)


						def 			__A			(			self      :							Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       List[str]     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
												# create three inputs of length 800, 1000, and 1200
												SCREAMING_SNAKE_CASE				:       int     							=				[floats_list((1, x)		)[0] for x in range(800       ,						1400       ,						200		)]
												SCREAMING_SNAKE_CASE				:       Any     							=				[np.asarray(_lowercase		) for speech_input in speech_inputs]

												# Test feature size
												SCREAMING_SNAKE_CASE				:       Tuple     							=				feature_extractor(_lowercase       ,						padding=_lowercase       ,						return_tensors='''np'''		).input_features
												self.assertTrue(input_features.ndim == 3		)
												self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

												# Test not batched input
												SCREAMING_SNAKE_CASE				:       Dict     							=				feature_extractor(speech_inputs[0]       ,						return_tensors='''np'''		).input_features
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				feature_extractor(np_speech_inputs[0]       ,						return_tensors='''np'''		).input_features
												self.assertTrue(np.allclose(_lowercase       ,						_lowercase       ,						atol=1E-3		)		)

												# Test batched
												SCREAMING_SNAKE_CASE				:       Any     							=				feature_extractor(_lowercase       ,						return_tensors='''np'''		).input_features
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				feature_extractor(_lowercase       ,						return_tensors='''np'''		).input_features
												for enc_seq_a, enc_seq_a in zip(_lowercase       ,						_lowercase		):
																		self.assertTrue(np.allclose(_lowercase       ,						_lowercase       ,						atol=1E-3		)		)

												# Test 2-D numpy arrays are batched.
												SCREAMING_SNAKE_CASE				:       str     							=				[floats_list((1, x)		)[0] for x in (800, 800, 800)]
												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				np.asarray(_lowercase		)
												SCREAMING_SNAKE_CASE				:       str     							=				feature_extractor(_lowercase       ,						return_tensors='''np'''		).input_features
												SCREAMING_SNAKE_CASE				:       Dict     							=				feature_extractor(_lowercase       ,						return_tensors='''np'''		).input_features
												for enc_seq_a, enc_seq_a in zip(_lowercase       ,						_lowercase		):
																		self.assertTrue(np.allclose(_lowercase       ,						_lowercase       ,						atol=1E-3		)		)


						def 			__A			(			self      :							int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
												SCREAMING_SNAKE_CASE				:       str     							=				[floats_list((1, x)		)[0] for x in range(800       ,						1400       ,						200		)]

												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				['''longest''', '''max_length''', '''do_not_pad''']
												SCREAMING_SNAKE_CASE				:       Tuple     							=				[None, 16, None]
												for max_length, padding in zip(_lowercase       ,						_lowercase		):
																		SCREAMING_SNAKE_CASE				:       Dict     							=				feature_extractor(
																		    _lowercase       ,						padding=_lowercase       ,						max_length=_lowercase       ,						return_attention_mask=_lowercase		)
																		SCREAMING_SNAKE_CASE				:       int     							=				inputs.input_features
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				inputs.attention_mask
																		SCREAMING_SNAKE_CASE				:       Dict     							=				[np.sum(_lowercase		) for x in attention_mask]

																		self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
																		self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
																		self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


						def 			__A			(			self      :							List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				[floats_list((1, x)		)[0] for x in range(800       ,						1400       ,						200		)]

												SCREAMING_SNAKE_CASE				:       List[Any]     							=				['''longest''', '''max_length''', '''do_not_pad''']
												SCREAMING_SNAKE_CASE				:       Tuple     							=				[None, 16, None]
												for max_length, padding in zip(_lowercase       ,						_lowercase		):
																		SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				feature_extractor(
																		    _lowercase       ,						max_length=_lowercase       ,						padding=_lowercase       ,						return_tensors='''np'''       ,						return_attention_mask=_lowercase		)
																		SCREAMING_SNAKE_CASE				:       Any     							=				inputs.input_features
																		SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				inputs.attention_mask
																		SCREAMING_SNAKE_CASE				:       List[Any]     							=				[np.sum(_lowercase		) for x in attention_mask]

																		self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
																		self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6		)
																		self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
																		self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6		)
																		self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


						def 			__A			(			self      :							Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
												SCREAMING_SNAKE_CASE				:       List[str]     							=				[floats_list((1, x)		)[0] for x in range(800       ,						1400       ,						200		)]
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				feature_extractor(
												    _lowercase       ,						padding='''max_length'''       ,						max_length=4       ,						truncation=_lowercase       ,						return_tensors='''np'''       ,						return_attention_mask=_lowercase       ,						)
												SCREAMING_SNAKE_CASE				:       Tuple     							=				inputs.input_features
												SCREAMING_SNAKE_CASE				:       str     							=				inputs.attention_mask
												SCREAMING_SNAKE_CASE				:       Dict     							=				np.sum(attention_mask == 1       ,						axis=1		)

												self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
												self._check_zero_mean_unit_variance(input_features[1]		)
												self._check_zero_mean_unit_variance(input_features[2]		)


						def 			__A			(			self      :							List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
												SCREAMING_SNAKE_CASE				:       int     							=				[floats_list((1, x)		)[0] for x in range(800       ,						1400       ,						200		)]
												SCREAMING_SNAKE_CASE				:       List[str]     							=				feature_extractor(
												    _lowercase       ,						padding='''longest'''       ,						max_length=4       ,						truncation=_lowercase       ,						return_tensors='''np'''       ,						return_attention_mask=_lowercase       ,						)
												SCREAMING_SNAKE_CASE				:       Any     							=				inputs.input_features
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				inputs.attention_mask
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				np.sum(attention_mask == 1       ,						axis=1		)

												self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
												self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
												self._check_zero_mean_unit_variance(input_features[2]		)

												# make sure that if max_length < longest -> then pad to max_length
												self.assertEqual(input_features.shape       ,						(3, 4, 24)		)

												SCREAMING_SNAKE_CASE				:       Tuple     							=				[floats_list((1, x)		)[0] for x in range(800       ,						1400       ,						200		)]
												SCREAMING_SNAKE_CASE				:       Dict     							=				feature_extractor(
												    _lowercase       ,						padding='''longest'''       ,						max_length=16       ,						truncation=_lowercase       ,						return_tensors='''np'''       ,						return_attention_mask=_lowercase       ,						)
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				inputs.input_features
												SCREAMING_SNAKE_CASE				:       List[str]     							=				inputs.attention_mask
												SCREAMING_SNAKE_CASE				:       Dict     							=				np.sum(attention_mask == 1       ,						axis=1		)

												self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
												self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
												self._check_zero_mean_unit_variance(input_features[2]		)

												# make sure that if max_length < longest -> then pad to max_length
												self.assertEqual(input_features.shape       ,						(3, 6, 24)		)


						def 			__A			(			self      :							Optional[Any]		):





												'''simple docstring'''





												import torch

												SCREAMING_SNAKE_CASE				:       str     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
												SCREAMING_SNAKE_CASE				:       Dict     							=				np.random.rand(100       ,						32		).astype(np.floataa		)
												SCREAMING_SNAKE_CASE				:       Tuple     							=				np_speech_inputs.tolist()

												for inputs in [py_speech_inputs, np_speech_inputs]:
																		SCREAMING_SNAKE_CASE				:       int     							=				feature_extractor.pad([{'''input_features''': inputs}]       ,						return_tensors='''np'''		)
																		self.assertTrue(np_processed.input_features.dtype == np.floataa		)
																		SCREAMING_SNAKE_CASE				:       Dict     							=				feature_extractor.pad([{'''input_features''': inputs}]       ,						return_tensors='''pt'''		)
																		self.assertTrue(pt_processed.input_features.dtype == torch.floataa		)


						def 			__A			(			self      :							List[Any]       ,						UpperCamelCase__      :							List[Any]		):





												'''simple docstring'''





												from datasets import load_dataset

												SCREAMING_SNAKE_CASE				:       str     							=				load_dataset('''hf-internal-testing/librispeech_asr_dummy'''       ,						'''clean'''       ,						split='''validation'''		)
												# automatic decoding with librispeech
												SCREAMING_SNAKE_CASE				:       int     							=				ds.sort('''id'''		).select(range(_lowercase		)		)[:num_samples]['''audio''']

												return [x["array"] for x in speech_samples]



						def 			__A			(			self      :							Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Tuple     							=				np.array([
												    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
												    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
												    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
												]		)
												# fmt: on

												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				self._load_datasamples(1		)
												SCREAMING_SNAKE_CASE				:       Tuple     							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				feature_extractor(_lowercase       ,						return_tensors='''pt'''		).input_features
												self.assertEquals(input_features.shape       ,						(1, 584, 24)		)
												self.assertTrue(np.allclose(input_features[0, 0, :30]       ,						_lowercase       ,						atol=1E-4		)		)






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_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			fname.split(os.path.sep		)[-1]
 return re.search(R'''^(.*)_\d+\.jpg$'''	,       lowercase		).groups()[0]




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	Any		,	_lowercase       :	Any=None		,	_lowercase       :	List[str]=None				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			file_names
    _UpperCamelCase:  List[Any]    						=			image_transform
    _UpperCamelCase:  Tuple    						=			label_to_id



   def __len__(						self       :	List[str]				):



    """simple docstring"""



    return len(self.file_names				)



   def __getitem__(						self       :	List[str]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.file_names[idx]
    _UpperCamelCase:  Optional[int]    						=			PIL.Image.open(_lowercase				)
    _UpperCamelCase:  List[str]    						=			raw_image.convert('''RGB'''				)
    if self.image_transform is not None:
     _UpperCamelCase:  Optional[Any]    						=			self.image_transform(_lowercase				)
    _UpperCamelCase:  Tuple    						=			extract_label(_lowercase				)
    if self.label_to_id is not None:
     _UpperCamelCase:  Any    						=			self.label_to_id[label]
    return {"image": image, "label": label}




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Any		)     ->					str:







 '''simple docstring'''
 # Initialize accelerator
 if args.with_tracking:
  _UpperCamelCase:  Tuple    						=			Accelerator(
      cpu=args.cpu	,       mixed_precision=args.mixed_precision	,       log_with='''all'''	,       project_dir=args.project_dir		)
 else:
  _UpperCamelCase:  Dict    						=			Accelerator(cpu=args.cpu	,       mixed_precision=args.mixed_precision		)

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:  List[str]    						=			config['''lr''']
 _UpperCamelCase:  int    						=			int(config['''num_epochs''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''seed''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''batch_size''']		)
 _UpperCamelCase:  List[str]    						=			config['''image_size''']
 if not isinstance(lowercase	,       (list, tuple)		):
  _UpperCamelCase:  Optional[int]    						=			(image_size, image_size)

 # Parse out whether we are saving every epoch or after a certain number of batches
 if hasattr(args.checkpointing_steps	,       '''isdigit'''		):
  if args.checkpointing_steps == "epoch":
   _UpperCamelCase:  Any    						=			args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _UpperCamelCase:  List[str]    						=			int(args.checkpointing_steps		)
  else:
   raise ValueError(
       F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
 else:
  _UpperCamelCase:  Any    						=			None

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

 # Grab all the image filenames
 _UpperCamelCase:  List[str]    						=			[os.path.join(args.data_dir	,       lowercase		) for fname in os.listdir(args.data_dir		) if fname.endswith('''.jpg'''		)]

 # Build the label correspondences
 _UpperCamelCase:  Optional[Any]    						=			[extract_label(lowercase		) for fname in file_names]
 _UpperCamelCase:  int    						=			list(set(lowercase		)		)
 id_to_label.sort()
 _UpperCamelCase:  Tuple    						=			{lbl: i for i, lbl in enumerate(lowercase		)}

 # Set the seed before splitting the data.
 np.random.seed(lowercase		)
 torch.manual_seed(lowercase		)
 torch.cuda.manual_seed_all(lowercase		)

 # Split our filenames between train and validation
 _UpperCamelCase:  List[str]    						=			np.random.permutation(len(lowercase		)		)
 _UpperCamelCase:  Dict    						=			int(0.8 * len(lowercase		)		)
 _UpperCamelCase:  Optional[int]    						=			random_perm[:cut]
 _UpperCamelCase:  Optional[int]    						=			random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 _UpperCamelCase:  List[Any]    						=			Compose([RandomResizedCrop(lowercase	,       scale=(0.5, 1.0)		), ToTensor()]		)
 _UpperCamelCase:  int    						=			PetsDataset(
     [file_names[i] for i in train_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # For evaluation, we use a deterministic Resize
 _UpperCamelCase:  Dict    						=			Compose([Resize(lowercase		), ToTensor()]		)
 _UpperCamelCase:  str    						=			PetsDataset([file_names[i] for i in eval_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # Instantiate dataloaders.
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  str    						=			create_model('''resnet50d'''	,       pretrained=lowercase	,       num_classes=len(lowercase		)		)

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 _UpperCamelCase:  Optional[int]    						=			model.to(accelerator.device		)

 # Freezing the base model
 for param in model.parameters():
  _UpperCamelCase:  Optional[Any]    						=			False
 for param in model.get_classifier().parameters():
  _UpperCamelCase:  Optional[int]    						=			True

 # We normalize the batches of images to be a bit faster.
 _UpperCamelCase:  Union[str, Any]    						=			torch.tensor(model.default_cfg['''mean''']		)[None, :, None, None].to(accelerator.device		)
 _UpperCamelCase:  List[Any]    						=			torch.tensor(model.default_cfg['''std''']		)[None, :, None, None].to(accelerator.device		)

 # Instantiate optimizer
 _UpperCamelCase:  List[str]    						=			torch.optim.Adam(params=model.parameters()	,       lr=lr / 25		)

 # Instantiate learning rate scheduler
 _UpperCamelCase:  Any    						=			OneCycleLR(optimizer=lowercase	,       max_lr=lowercase	,       epochs=lowercase	,       steps_per_epoch=len(lowercase		)		)

 # 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:  List[str]    						=			accelerator.prepare(
     lowercase	,       lowercase	,       lowercase	,       lowercase	,       lowercase		)
 # We need to keep track of how many total steps we have iterated over
 _UpperCamelCase:  Tuple    						=			0
 # We also need to keep track of the starting epoch so files are named properly
 _UpperCamelCase:  List[str]    						=			0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""		)
   accelerator.load_state(args.resume_from_checkpoint		)
   _UpperCamelCase:  Union[str, Any]    						=			os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   _UpperCamelCase:  Union[str, Any]    						=			[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   _UpperCamelCase:  Union[str, Any]    						=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _UpperCamelCase:  List[Any]    						=			os.path.splitext(lowercase		)[0]

  if "epoch" in training_difference:
   _UpperCamelCase:  Any    						=			int(training_difference.replace('''epoch_'''	,       ''''''		)		) + 1
   _UpperCamelCase:  List[str]    						=			None
  else:
   _UpperCamelCase:  int    						=			int(training_difference.replace('''step_'''	,       ''''''		)		)
   _UpperCamelCase:  List[Any]    						=			resume_step // len(lowercase		)
   resume_step -= starting_epoch * len(lowercase		)

    # Now we train the model
 for epoch in range(lowercase	,       lowercase		):
  model.train()
  if args.with_tracking:
   _UpperCamelCase:  Optional[int]    						=			0
  if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
   # We need to skip steps until we reach the resumed step
   _UpperCamelCase:  Union[str, Any]    						=			accelerator.skip_first_batches(lowercase	,       lowercase		)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _UpperCamelCase:  Optional[int]    						=			train_dataloader
  for batch in active_dataloader:
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  int    						=			(batch['''image'''] - mean) / std
   _UpperCamelCase:  List[str]    						=			model(lowercase		)
   _UpperCamelCase:  Dict    						=			torch.nn.functional.cross_entropy(lowercase	,       batch['''label''']		)
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(lowercase		)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(lowercase	,       lowercase		):
    _UpperCamelCase:  Any    						=			F"""step_{overall_step}"""
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _UpperCamelCase:  List[Any]    						=			os.path.join(args.output_dir	,       lowercase		)
     accelerator.save_state(lowercase		)
  model.eval()
  _UpperCamelCase:  List[str]    						=			0
  _UpperCamelCase:  Union[str, Any]    						=			0
  for step, batch in enumerate(lowercase		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  Union[str, Any]    						=			(batch['''image'''] - mean) / std
   with torch.no_grad():
    _UpperCamelCase:  List[Any]    						=			model(lowercase		)
   _UpperCamelCase:  Optional[Any]    						=			outputs.argmax(dim=-1		)
   _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			accelerator.gather_for_metrics((predictions, batch['''label'''])		)
   _UpperCamelCase:  List[str]    						=			predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  _UpperCamelCase:  Optional[int]    						=			accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""		)
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': 100 * eval_metric,
           '''train_loss''': total_loss.item() / len(lowercase		),
           '''epoch''': epoch,
       }	,       step=lowercase	,       )
  if checkpointing_steps == "epoch":
   _UpperCamelCase:  Tuple    						=			F"""epoch_{epoch}"""
   if args.output_dir is not None:
    _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       lowercase		)
   accelerator.save_state(lowercase		)

 if args.with_tracking:
  accelerator.end_training()




def    lowerCAmelCase_  (		)     ->					Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:  str    						=			argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument('''--data_dir'''	,       required=lowercase	,       help='''The data folder on disk.'''		)
 parser.add_argument('''--fp16'''	,       action='''store_true'''	,       help='''If passed, will use FP16 training.'''		)
 parser.add_argument(
     '''--mixed_precision'''	,       type=lowercase	,       default=lowercase	,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,       help='''Whether to use mixed precision. Choose'''
     '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
     '''and an Nvidia Ampere GPU.'''	,       )
 parser.add_argument('''--cpu'''	,       action='''store_true'''	,       help='''If passed, will train on the CPU.'''		)
 parser.add_argument(
     '''--checkpointing_steps'''	,       type=lowercase	,       default=lowercase	,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,       )
 parser.add_argument(
     '''--output_dir'''	,       type=lowercase	,       default='''.'''	,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,       )
 parser.add_argument(
     '''--resume_from_checkpoint'''	,       type=lowercase	,       default=lowercase	,       help='''If the training should continue from a checkpoint folder.'''	,       )
 parser.add_argument(
     '''--with_tracking'''	,       action='''store_true'''	,       help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,       )
 parser.add_argument(
     '''--project_dir'''	,       type=lowercase	,       default='''logs'''	,       help='''Location on where to store experiment tracking logs` and relevent project information'''	,       )
 _UpperCamelCase:  Any    						=			parser.parse_args()
 _UpperCamelCase:  Optional[int]    						=			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()