






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 		_a       ( a					:Any					)				->     Tuple:
 a						    = fname.split(os.path.sep					)[-1]
 return re.search(r'''^(.*)_\d+\.jpg$'''      ,				a					).groups()[0]






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      def __init__( self :		Tuple					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		Tuple=None					,   __UpperCAmelCase :		Optional[Any]=None    )       ->Any:




       """simple docstring"""


       a						    = file_names
       a						    = image_transform
       a						    = label_to_id







      def __len__( self :		List[Any]    )       ->int:




       """simple docstring"""


       return len(self.file_names    )







      def __getitem__( self :		List[str]					,   __UpperCAmelCase :		Optional[int]    )       ->Optional[int]:




       """simple docstring"""


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



 if args.with_tracking:
  a						    = Accelerator(
      cpu=args.cpu      ,				mixed_precision=args.mixed_precision      ,				log_with='''all'''      ,				project_dir=args.project_dir					)
 else:
  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(a      ,				(list, tuple)					):
  a						    = (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":
   a						    = args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   a						    = int(args.checkpointing_steps					)
  else:
   raise ValueError(
       F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""					)
 else:
  a						    = None

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

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

 # Build the label correspondences
 a						    = [extract_label(a					) for fname in file_names]
 a						    = list(set(a					)					)
 id_to_label.sort()
 a						    = {lbl: i for i, lbl in enumerate(a					)}

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

 # Split our filenames between train and validation
 a						    = np.random.permutation(len(a					)					)
 a						    = int(0.8 * len(a					)					)
 a						    = random_perm[:cut]
 a						    = random_perm[cut:]

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

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

 # Instantiate dataloaders.
 a						    = DataLoader(a      ,				shuffle=a      ,				batch_size=a      ,				num_workers=4					)
 a						    = DataLoader(a      ,				shuffle=a      ,				batch_size=a      ,				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=a      ,				num_classes=len(a					)					)

 # 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=a      ,				max_lr=a      ,				epochs=a      ,				steps_per_epoch=len(a					)					)

 # 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						    = accelerator.prepare(
     a      ,				a      ,				a      ,				a      ,				a					)
 # We need to keep track of how many total steps we have iterated over
 a						    = 0
 # We also need to keep track of the starting epoch so files are named properly
 a						    = 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					)
   a						    = os.path.basename(args.resume_from_checkpoint					)
  else:
   # Get the most recent checkpoint
   a						    = [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
   dirs.sort(key=os.path.getctime					)
   a						    = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  a						    = os.path.splitext(a					)[0]

  if "epoch" in training_difference:
   a						    = int(training_difference.replace('''epoch_'''      ,				''''''					)					) + 1
   a						    = None
  else:
   a						    = int(training_difference.replace('''step_'''      ,				''''''					)					)
   a						    = resume_step // len(a					)
   resume_step -= starting_epoch * len(a					)

    # Now we train the model
 for epoch in range(a      ,				a					):
  model.train()
  if args.with_tracking:
   a						    = 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
   a						    = accelerator.skip_first_batches(a      ,				a					)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   a						    = train_dataloader
  for batch in active_dataloader:
   # 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(a					)
   a						    = torch.nn.functional.cross_entropy(a      ,				batch['''label''']					)
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(a					)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(a      ,				a					):
    a						    = F"""step_{overall_step}"""
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      a						    = os.path.join(args.output_dir      ,				a					)
     accelerator.save_state(a					)
  model.eval()
  a						    = 0
  a						    = 0
  for step, batch in enumerate(a					):
   # 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(a					)
   a						    = outputs.argmax(dim=-1					)
   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}"""					)
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': 100 * eval_metric,
           '''train_loss''': total_loss.item() / len(a					),
           '''epoch''': epoch,
       }      ,				step=a      ,				)
  if checkpointing_steps == "epoch":
   a						    = F"""epoch_{epoch}"""
   if args.output_dir is not None:
    a						    = os.path.join(args.output_dir      ,				a					)
   accelerator.save_state(a					)

 if args.with_tracking:
  accelerator.end_training()
def 		_a       ( )				->     Any:
 a						    = argparse.ArgumentParser(description='''Simple example of training script.'''					)
 parser.add_argument('''--data_dir'''      ,				required=a      ,				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=a      ,				default=a      ,				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=a      ,				default=a      ,				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=a      ,				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=a      ,				default=a      ,				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=a      ,				default='''logs'''      ,				help='''Location on where to store experiment tracking logs` and relevent project information'''      ,				)
 a						    = parser.parse_args()
 a						    = {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
 training_function(a      ,				a					)


if __name__ == "__main__":
     main()










"""simple docstring"""



import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_lowerCAmelCase	:	Optional[int]				=      {
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

_lowerCAmelCase	:	Optional[Any]				=      {
    """169M""": 768,
    """430M""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							list(state_dict.keys()				)
     for name in state_dict_keys:
          UpperCAmelCase__						:    Dict							=							state_dict.pop(snake_case				)
          # emb -> embedding
          if name.startswith("emb."				):
               UpperCAmelCase__						:    str							=							name.replace("emb."			,					"embeddings."				)
          # ln_0 -> pre_ln (only present at block 0)
          if name.startswith("blocks.0.ln0"				):
               UpperCAmelCase__						:    List[str]							=							name.replace("blocks.0.ln0"			,					"blocks.0.pre_ln"				)
          # att -> attention
          UpperCAmelCase__						:    Optional[int]							=							re.sub(r"blocks\.(\d+)\.att"			,					r"blocks.\1.attention"			,					snake_case				)
          # ffn -> feed_forward
          UpperCAmelCase__						:    Any							=							re.sub(r"blocks\.(\d+)\.ffn"			,					r"blocks.\1.feed_forward"			,					snake_case				)
          # time_mix_k -> time_mix_key and reshape
          if name.endswith(".time_mix_k"				):
               UpperCAmelCase__						:    List[Any]							=							name.replace(".time_mix_k"			,					".time_mix_key"				)
          # time_mix_v -> time_mix_value and reshape
          if name.endswith(".time_mix_v"				):
               UpperCAmelCase__						:    int							=							name.replace(".time_mix_v"			,					".time_mix_value"				)
          # time_mix_r -> time_mix_key and reshape
          if name.endswith(".time_mix_r"				):
               UpperCAmelCase__						:    Optional[Any]							=							name.replace(".time_mix_r"			,					".time_mix_receptance"				)

          if name != "head.weight":
               UpperCAmelCase__						:    int							=							"rwkv." + name

          UpperCAmelCase__						:    Dict							=							weight
     return state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : Optional[int]			,					snake_case       : Optional[Any]			,					snake_case       : Dict=None			,					snake_case       : List[Any]=None			,					snake_case       : List[str]=False			,					snake_case       : str=None				)->		int:






     '''simple docstring'''



     if tokenizer_file is None:
          print("No `--tokenizer_file` provided, we will use the default tokenizer."				)
          UpperCAmelCase__						:    str							=							5_0277
          UpperCAmelCase__						:    List[Any]							=							AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"				)
     else:
          UpperCAmelCase__						:    Union[str, Any]							=							PreTrainedTokenizerFast(tokenizer_file=snake_case				)
          UpperCAmelCase__						:    Tuple							=							len(snake_case				)
     tokenizer.save_pretrained(snake_case				)

     # 2. Build the config
     UpperCAmelCase__						:    Dict							=							list(NUM_HIDDEN_LAYERS_MAPPING.keys()				)
     if size is None:
          # Try to infer size from the checkpoint name
          for candidate in possible_sizes:
               if candidate in checkpoint_file:
                    UpperCAmelCase__						:    List[Any]							=							candidate
                    break
          if size is None:
               raise ValueError("Could not infer the size, please provide it with the `--size` argument."				)
     if size not in possible_sizes:
          raise ValueError(f'`size` should be one of {possible_sizes}, got {size}.'				)

     UpperCAmelCase__						:    int							=							RwkvConfig(
         vocab_size=snake_case			,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,					hidden_size=HIDEN_SIZE_MAPPING[size]			,					)
     config.save_pretrained(snake_case				)

     # 3. Download model file then convert state_dict
     UpperCAmelCase__						:    Optional[int]							=							hf_hub_download(snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Tuple							=							convert_state_dict(snake_case				)

     # 4. Split in shards and save
     UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							shard_checkpoint(snake_case				)
     for shard_file, shard in shards.items():
          torch.save(snake_case			,					os.path.join(snake_case			,					snake_case				)				)

     if index is not None:
          UpperCAmelCase__						:    Tuple							=							os.path.join(snake_case			,					snake_case				)
          # Save the index as well
          with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
               UpperCAmelCase__						:    Any							=							json.dumps(snake_case			,					indent=2			,					sort_keys=snake_case				) + "\n"
               f.write(snake_case				)

          # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
          print(
              "Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."				)
          UpperCAmelCase__						:    List[str]							=							list(shards.keys()				)

          del state_dict
          del shards
          gc.collect()

          for shard_file in shard_files:
               UpperCAmelCase__						:    Any							=							torch.load(os.path.join(snake_case			,					snake_case				)				)
               torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,					os.path.join(snake_case			,					snake_case				)				)

     del state_dict
     gc.collect()

     if push_to_hub:
          if model_name is None:
               raise ValueError("Please provide a `model_name` to push the model to the Hub."				)
          UpperCAmelCase__						:    Optional[Any]							=							AutoModelForCausalLM.from_pretrained(snake_case				)
          model.push_to_hub(snake_case			,					max_shard_size="2GB"				)
          tokenizer.push_to_hub(snake_case				)


if __name__ == "__main__":
 _lowerCAmelCase	:	Optional[Any]				=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
 )
 parser.add_argument(
     """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
 )
 parser.add_argument(
     """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
 )
 parser.add_argument(
     """--tokenizer_file""",
     default=None,
     type=str,
     help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
 )
 parser.add_argument(
     """--size""",
     default=None,
     type=str,
     help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
     help="""Push to the Hub the converted model.""",
 )
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help="""Name of the pushed model on the Hub, including the username / organization.""",
 )

 _lowerCAmelCase	:	Dict				=      parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )


