



"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase  			=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase  			=		[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
  )
  rename_keys.append(
      (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
  )
  rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
  rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
  rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
  rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
  rename_keys.append(
      (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
  )
  rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
  rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
  rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
  )
  rename_keys.append(
      (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
  )
  rename_keys.append(
      (
          F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
          F"decoder.layers.{i}.encoder_attn.out_proj.weight",
      )
  )
  rename_keys.append(
      (
          F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
          F"decoder.layers.{i}.encoder_attn.out_proj.bias",
      )
  )
  rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
  rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
  rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
  rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
  rename_keys.append(
      (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
  )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
  rename_keys.append(
      (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
  )
  rename_keys.append(
      (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
  )
  rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
  rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
      UpperCAmelCase_     					=				val
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  UpperCAmelCase_     					=				key.replace("backbone.0.body"      ,     "backbone.conv_encoder.model" )
                  UpperCAmelCase_     					=				value
            else:
                  UpperCAmelCase_     					=				value

      return new_state_dict
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				""

      # first: transformer encoder
      for i in range(6 ):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_     					=				in_proj_weight[:256, :]
            UpperCAmelCase_     					=				in_proj_bias[:256]
            UpperCAmelCase_     					=				in_proj_weight[256:512, :]
            UpperCAmelCase_     					=				in_proj_bias[256:512]
            UpperCAmelCase_     					=				in_proj_weight[-256:, :]
            UpperCAmelCase_     					=				in_proj_bias[-256:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6 ):
            # read in weights + bias of input projection layer of self-attention
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_     					=				in_proj_weight[:256, :]
            UpperCAmelCase_     					=				in_proj_bias[:256]
            UpperCAmelCase_     					=				in_proj_weight[256:512, :]
            UpperCAmelCase_     					=				in_proj_bias[256:512]
            UpperCAmelCase_     					=				in_proj_weight[-256:, :]
            UpperCAmelCase_     					=				in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            UpperCAmelCase_     					=				state_dict.pop(
                f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            UpperCAmelCase_     					=				in_proj_weight_cross_attn[:256, :]
            UpperCAmelCase_     					=				in_proj_bias_cross_attn[:256]
            UpperCAmelCase_     					=				in_proj_weight_cross_attn[256:512, :]
            UpperCAmelCase_     					=				in_proj_bias_cross_attn[256:512]
            UpperCAmelCase_     					=				in_proj_weight_cross_attn[-256:, :]
            UpperCAmelCase_     					=				in_proj_bias_cross_attn[-256:]
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_					,       UpperCAmelCase_     					=				image.size
      UpperCAmelCase_     					=				max(lowerCAmelCase__      ,     lowerCAmelCase__ )
      UpperCAmelCase_     					=				800 if "detection" in checkpoint_url else 1000
      UpperCAmelCase_     					=				target_max_size / current_max_size
      UpperCAmelCase_     					=				image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )

      return resized_image
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				F.to_tensor(lowerCAmelCase__ )
      UpperCAmelCase_     					=				F.normalize(lowerCAmelCase__      ,     mean=[0.485, 0.456, 0.406]      ,     std=[0.229, 0.224, 0.225] )
      return image
@torch.no_grad()
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      logger.info("Converting model..." )

      # load original state dict
      UpperCAmelCase_     					=				torch.hub.load_state_dict_from_url(lowerCAmelCase__      ,     map_location="cpu" )
      # rename keys
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ )
      UpperCAmelCase_     					=				rename_backbone_keys(lowerCAmelCase__ )
      # query, key and value matrices need special treatment
      read_in_q_k_v(lowerCAmelCase__ )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      UpperCAmelCase_     					=				"model."
      for key in state_dict.copy().keys():
            if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
                  UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                  UpperCAmelCase_     					=				val
    # create HuggingFace model and load state dict
      UpperCAmelCase_     					=				TableTransformerConfig(
          backbone="resnet18"      ,     mask_loss_coefficient=1      ,     dice_loss_coefficient=1      ,     ce_loss_coefficient=1      ,     bbox_loss_coefficient=5      ,     giou_loss_coefficient=2      ,     eos_coefficient=0.4      ,     class_cost=1      ,     bbox_cost=5      ,     giou_cost=2      ,     )

      if "detection" in checkpoint_url:
            UpperCAmelCase_     					=				15
            UpperCAmelCase_     					=				2
            UpperCAmelCase_     					=				{0: "table", 1: "table rotated"}
            UpperCAmelCase_     					=				idalabel
            UpperCAmelCase_     					=				{v: k for k, v in idalabel.items()}
      else:
            UpperCAmelCase_     					=				125
            UpperCAmelCase_     					=				6
            UpperCAmelCase_     					=				{
                0: "table",
                1: "table column",
                2: "table row",
                3: "table column header",
                4: "table projected row header",
                5: "table spanning cell",
            }
            UpperCAmelCase_     					=				idalabel
            UpperCAmelCase_     					=				{v: k for k, v in idalabel.items()}

      UpperCAmelCase_     					=				DetrImageProcessor(
          format="coco_detection"      ,     max_size=800 if "detection" in checkpoint_url else 1000 )
      UpperCAmelCase_     					=				TableTransformerForObjectDetection(lowerCAmelCase__ )
      model.load_state_dict(lowerCAmelCase__ )
      model.eval()

      # verify our conversion
      UpperCAmelCase_     					=				"example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
      UpperCAmelCase_     					=				hf_hub_download(repo_id="nielsr/example-pdf"      ,     repo_type="dataset"      ,     filename=lowerCAmelCase__ )
      UpperCAmelCase_     					=				Image.open(lowerCAmelCase__ ).convert("RGB" )
      UpperCAmelCase_     					=				normalize(resize(lowerCAmelCase__      ,     lowerCAmelCase__ ) ).unsqueeze(0 )

      UpperCAmelCase_     					=				model(lowerCAmelCase__ )

      if "detection" in checkpoint_url:
            UpperCAmelCase_     					=				(1, 15, 3)
            UpperCAmelCase_     					=				torch.tensor(
                [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
            UpperCAmelCase_     					=				torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )

      else:
            UpperCAmelCase_     					=				(1, 125, 7)
            UpperCAmelCase_     					=				torch.tensor(
                [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
            UpperCAmelCase_     					=				torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )

      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, :3, :3]      ,     lowerCAmelCase__      ,     atol=1e-4 )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,     lowerCAmelCase__      ,     atol=1e-4 )
      print("Looks ok!" )

      if pytorch_dump_folder_path is not None:
            # Save model and image processor
            logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
            Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
            model.save_pretrained(lowerCAmelCase__ )
            image_processor.save_pretrained(lowerCAmelCase__ )

      if push_to_hub:
            # Push model to HF hub
            logger.info("Pushing model to the hub..." )
            UpperCAmelCase_     					=				(
                "microsoft/table-transformer-detection"
                if "detection" in checkpoint_url
                else "microsoft/table-transformer-structure-recognition"
            )
            model.push_to_hub(lowerCAmelCase__ )
            image_processor.push_to_hub(lowerCAmelCase__ )


if __name__ == "__main__":
  lowerCamelCase  			=		argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
      type=str,
      choices=[
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
      ],
      help="""URL of the Table Transformer checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )
  lowerCamelCase  			=		parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





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()