






import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
class 							__snake_case(       __a   ):



 def __init__(						self : Any			,		UpperCAmelCase__ : Dict			,		UpperCAmelCase__ : int			,		UpperCAmelCase__ : Dict=False			,		UpperCAmelCase__ : Optional[int]=1_024			,		UpperCAmelCase__ : int=1_024			,		UpperCAmelCase__ : List[str]=3.6			,		UpperCAmelCase__ : str=False			,		):

    """simple docstring"""






    lowercase__    :						str       =			tokenizer
    lowercase__    :						Union[str, Any]       =			tokenizer.bos_token_id
    lowercase__    :						Union[str, Any]       =			dataset
    lowercase__    :						List[str]       =			seq_length
    lowercase__    :						Union[str, Any]       =			0
    lowercase__    :						Optional[Any]       =			infinite
    lowercase__    :						List[str]       =			0
    lowercase__    :						Any       =			tokenized

    if self.tokenized:
       lowercase__    :						Any       =			seq_length * num_of_sequences
       lowercase__    :						List[Any]       =			"""input_ids"""
    else:
       lowercase__    :						Tuple       =			seq_length * chars_per_token * num_of_sequences
       lowercase__    :						List[Any]       =			"""content"""



 def __iter__(						self : Any						):

    """simple docstring"""






    lowercase__    :						int       =			iter(self.dataset						)
    lowercase__    :						Optional[Any]       =			True
    while more_examples:
       lowercase__  ,       lowercase__    :						Dict       =			[], 0
       while True:
          if buffer_len >= self.max_buffer_size:
             break
          try:
             buffer.append(next(lowerCAmelCase_						)[self.content_field]						)
             buffer_len += len(buffer[-1]						)
          except StopIteration:
             if self.infinite:
                lowercase__    :						int       =			iter(self.dataset						)
                self.epoch += 1
                logger.info(f'''Dataset epoch: {self.epoch}'''						)
             else:
                lowercase__    :						Optional[Any]       =			False
                break
       if self.tokenized:
          lowercase__    :						Any       =			buffer
       else:
          lowercase__    :						int       =			self.tokenizer(lowerCAmelCase_			,		truncation=lowerCAmelCase_						)["""input_ids"""]
       lowercase__    :						int       =			[]
       for tokenized_input in tokenized_inputs:
          all_token_ids.extend(tokenized_input + [self.concat_token_id]						)
       for i in range(0			,		len(lowerCAmelCase_						)			,		self.seq_length						):
          lowercase__    :						Dict       =			all_token_ids[i : i + self.seq_length]
          if len(lowerCAmelCase_						) == self.seq_length:
             self.current_size += 1
             yield torch.tensor(lowerCAmelCase_						)







 def     UpperCAmelCase     (						self : Optional[int]			,		UpperCAmelCase__ : Union[str, Any]=1_000						):

    """simple docstring"""






    return ShufflerIterDataPipe(self			,		buffer_size=lowerCAmelCase_						)
def    a__    (	A       ):
   lowercase__    :						Tuple       =			args.model_ckpt.split("""/"""       )[-1]
   lowercase__    :						Optional[int]       =			logging.getLogger(__name__       )
   lowercase__    :						str       =			Path(args.save_dir       ) / """log/"""
   log_dir.mkdir(exist_ok=lowercase__       )
   lowercase__    :						Optional[Any]       =			F'''debug_{accelerator.process_index}.log'''
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,					datefmt="""%m/%d/%Y %H:%M:%S"""		,					level=logging.INFO		,					handlers=[logging.FileHandler(log_dir / filename       ), logging.StreamHandler()]		,					)
   if accelerator.is_main_process:  # we only want to setup logging once
      accelerator.init_trackers(lowercase__		,					vars(lowercase__       )       )
      lowercase__    :						Optional[int]       =			accelerator.trackers[0].run.name
      logger.setLevel(logging.INFO       )
      datasets.utils.logging.set_verbosity_info()
      transformers.utils.logging.set_verbosity_info()
   else:
      lowercase__    :						Any       =			""""""
      logger.setLevel(logging.ERROR       )
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   return logger, run_name
def    a__    (	A       ):
   lowercase__    :						Any       =			{"""streaming""": True}
   lowercase__    :						Dict       =			load_dataset(args.dataset_name_train		,					split="""train"""		,					**lowercase__       )
   lowercase__    :						Union[str, Any]       =			train_data.shuffle(buffer_size=args.shuffle_buffer		,					seed=args.seed       )
   lowercase__    :						Tuple       =			load_dataset(args.dataset_name_valid		,					split="""train"""		,					**lowercase__       )
   lowercase__    :						Dict       =			ConstantLengthDataset(
       lowercase__		,					lowercase__		,					infinite=lowercase__		,					seq_length=args.seq_length		,					tokenized=args.tokenized       )
   lowercase__    :						Dict       =			ConstantLengthDataset(
       lowercase__		,					lowercase__		,					infinite=lowercase__		,					seq_length=args.seq_length		,					tokenized=args.tokenized       )
   lowercase__    :						Dict       =			train_dataset.shuffle(buffer_size=args.shuffle_buffer       )
   lowercase__    :						List[str]       =			DataLoader(lowercase__		,					batch_size=args.train_batch_size		,					shuffle=lowercase__       )
   lowercase__    :						List[Any]       =			DataLoader(lowercase__		,					batch_size=args.valid_batch_size       )
   return train_dataloader, eval_dataloader
def    a__    (	A		,					A		,					A=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]       ):
   lowercase__  ,       lowercase__    :						Tuple       =			[], []
   for n, p in model.named_parameters():
      if any(nd in n for nd in no_decay       ):
         params_without_wd.append(lowercase__       )
      else:
         params_with_wd.append(lowercase__       )
   return [
       {"params": params_with_wd, "weight_decay": args.weight_decay},
       {"params": params_without_wd, "weight_decay": 0.0},
   ]
def    a__    (	A		,					A       ):
   logger.info(F'''Step {step}: {metrics}'''       )
   if accelerator.is_main_process:
      accelerator.log(lowercase__		,					lowercase__       )
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						List[str]       =			accelerator.unwrap_model(lowercase__       ).config
   lowercase__    :						List[Any]       =			4 if args.gradient_checkpointing else 3
   lowercase__    :						int       =			args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
   lowercase__    :						List[str]       =			24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
   lowercase__    :						List[Any]       =			factor * (
       1.0
       + (args.seq_length / (6.0 * config_model.n_embd))
       + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
   )
   lowercase__    :						int       =			flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
   return tflops
def    a__    (	A       ):
   model.eval()
   lowercase__    :						Tuple       =			[]
   for step, batch in enumerate(lowercase__       ):
      with torch.no_grad():
         lowercase__    :						List[Any]       =			model(lowercase__		,					labels=lowercase__       )
      lowercase__    :						List[str]       =			outputs.loss.repeat(args.valid_batch_size       )
      losses.append(accelerator.gather(lowercase__       )       )
      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   lowercase__    :						str       =			torch.cat(lowercase__       )
   lowercase__    :						Union[str, Any]       =			losses[: eval_dataloader.dataset.current_size].mean()
   try:
      lowercase__    :						Optional[Any]       =			torch.exp(lowercase__       )
   except OverflowError:
      lowercase__    :						List[str]       =			float("""inf"""       )
   return loss.item(), perplexity.item()


# Settings
a 			=							HfArgumentParser(TrainingArguments)
a 			=							parser.parse_args()

# Accelerator
a 			=							ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
a 			=							Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
a 			=							{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

a 			=							Namespace(**vars(args), **acc_state)
a 			=							accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
 a 			=							Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
a			,   a 			=							setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
 hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
a 			=							AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
 model.gradient_checkpointing_enable()
a 			=							AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
a			,   a 			=							create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
a 			=							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
a 			=							get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)
def    a__    (	):
   return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
a			,   a			,   a			,   a 			=							accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# 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(args.save_dir) if f.is_dir() and 'step' in str(f)]
  dirs.sort(key=os.path.getctime)
  a 			=							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
 # Extract the step of the checkpoint to continue from there
 a 			=							os.path.splitext(path)[0]
 a 			=							int(training_difference.replace('step_', ''))

# Train model
model.train()
a 			=							0
a 			=							time.time()
a 			=							0
for step, batch in enumerate(train_dataloader, start=1):
 if args.resume_from_checkpoint and step < resume_step:
  continue  # we need to skip steps until we reach the resumed step
 a 			=							model(batch, labels=batch, use_cache=False).loss
 a 			=							accelerator.gather(loss.repeat(args.train_batch_size)).mean()
 loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
 log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
 a 			=							loss / args.gradient_accumulation_steps
 if step % args.gradient_accumulation_steps != 0:
  # Prevent backward from doing gradient all_reduce in every step
  if accelerator.distributed_type == DistributedType.MULTI_GPU:
   with model.no_sync():
    accelerator.backward(loss)
  else:
   accelerator.backward(loss)
 else:
  a 			=							get_lr()
  accelerator.backward(loss)
  accelerator.clip_grad_norm_(model.parameters(), 1.0)
  optimizer.step()
  lr_scheduler.step()
  optimizer.zero_grad()
  a 			=							time.time() - t_start
  a 			=							compute_tflops(elapsed_time, accelerator, args)
  log_metrics(
      step,
      {
          'steps': completed_steps,
          'loss/train': loss_tracking,
          'lr': lr,
          'tflops': tflops,
          'time_per_iteration': elapsed_time,
      },
  )
  a 			=							time.time()
  a 			=							0
  completed_steps += 1
 if step % args.save_checkpoint_steps == 0:
  logger.info('Evaluating and saving model checkpoint')
  a			,   a 			=							evaluate(args)
  log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  accelerator.wait_for_everyone()
  a 			=							os.path.join(args.save_dir, F'step_{step}')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
   hf_repo.push_to_hub(commit_message=F'step {step}')
  model.train()
 if completed_steps >= args.max_train_steps:
  break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
a			,   a 			=							evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
a 			=							accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
a 			=							os.path.join(args.save_dir, F'step_{step}')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
 hf_repo.push_to_hub(commit_message='final model')







'''simple docstring'''


import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


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






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=ConvNextConfig()

     if "tiny" in checkpoint_url:
          a_									=[3, 3, 9, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "small" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "base" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
     if "large" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
     if "xlarge" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

     if "1k" in checkpoint_url:
          a_									=1_0_0_0
          a_									="imagenet-1k-id2label.json"
          a_									=(1, 1_0_0_0)
     else:
          a_									=2_1_8_4_1
          a_									="imagenet-22k-id2label.json"
          a_									=(1, 2_1_8_4_1)

     a_									="huggingface/label-files"
     a_									=num_labels
     a_									=json.load(open(hf_hub_download(lowercase__							,						lowercase__							,						repo_type="dataset"							)							,						"r"							)							)
     a_									={int(lowercase__							): v for k, v in idalabel.items()}
     if "1k" not in checkpoint_url:
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9_2_0_5]
          del idalabel[1_5_0_2_7]
     a_									=idalabel
     a_									={v: k for k, v in idalabel.items()}
     a_									=hidden_sizes
     a_									=depths

     return config, expected_shape






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if "downsample_layers.0.0" in name:
          a_									=name.replace("downsample_layers.0.0"							,						"embeddings.patch_embeddings"							)
     if "downsample_layers.0.1" in name:
          a_									=name.replace("downsample_layers.0.1"							,						"embeddings.norm"							)  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          a_									=name.replace("downsample_layers.1.0"							,						"stages.1.downsampling_layer.0"							)
     if "downsample_layers.1.1" in name:
          a_									=name.replace("downsample_layers.1.1"							,						"stages.1.downsampling_layer.1"							)
     if "downsample_layers.2.0" in name:
          a_									=name.replace("downsample_layers.2.0"							,						"stages.2.downsampling_layer.0"							)
     if "downsample_layers.2.1" in name:
          a_									=name.replace("downsample_layers.2.1"							,						"stages.2.downsampling_layer.1"							)
     if "downsample_layers.3.0" in name:
          a_									=name.replace("downsample_layers.3.0"							,						"stages.3.downsampling_layer.0"							)
     if "downsample_layers.3.1" in name:
          a_									=name.replace("downsample_layers.3.1"							,						"stages.3.downsampling_layer.1"							)
     if "stages" in name and "downsampling_layer" not in name:
          # stages.0.0. for instance should be renamed to stages.0.layers.0.
          a_									=name[: len("stages.0"							)] + ".layers" + name[len("stages.0"							) :]
     if "stages" in name:
          a_									=name.replace("stages"							,						"encoder.stages"							)
     if "norm" in name:
          a_									=name.replace("norm"							,						"layernorm"							)
     if "gamma" in name:
          a_									=name.replace("gamma"							,						"layer_scale_parameter"							)
     if "head" in name:
          a_									=name.replace("head"							,						"classifier"							)

     return name






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									="http://images.cocodataset.org/val2017/000000039769.jpg"
     a_									=Image.open(requests.get(lowercase__							,						stream=lowercase__							).raw							)
     return im






@torch.no_grad()
def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_		,    a_									=get_convnext_config(lowercase__							)
     # load original state_dict from URL
     a_									=torch.hub.load_state_dict_from_url(lowercase__							)["model"]
     # rename keys
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          a_									=val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          if not key.startswith("classifier"							):
               a_									="convnext." + key
          a_									=val

     # load HuggingFace model
     a_									=ConvNextForImageClassification(lowercase__							)
     model.load_state_dict(lowercase__							)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     a_									=2_2_4 if "224" in checkpoint_url else 3_8_4
     a_									=ConvNextImageProcessor(size=lowercase__							)
     a_									=image_processor(images=prepare_img()							,						return_tensors="pt"							).pixel_values

     a_									=model(lowercase__							).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          a_									=torch.tensor([-0.1210, -0.6605, 0.1918]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          a_									=torch.tensor([-0.4473, -0.1847, -0.6365]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          a_									=torch.tensor([0.4525, 0.7539, 0.0308]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          a_									=torch.tensor([0.3561, 0.6350, -0.0384]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          a_									=torch.tensor([0.4174, -0.0989, 0.1489]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          a_									=torch.tensor([0.2513, -0.1349, -0.1613]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          a_									=torch.tensor([1.2980, 0.3631, -0.1198]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          a_									=torch.tensor([1.2963, 0.1227, 0.1723]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          a_									=torch.tensor([1.7956, 0.8390, 0.2820]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          a_									=torch.tensor([-0.2822, -0.0502, -0.0878]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          a_									=torch.tensor([-0.5672, -0.0730, -0.4348]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          a_									=torch.tensor([0.2681, 0.2365, 0.6246]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          a_									=torch.tensor([-0.2642, 0.3931, 0.5116]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          a_									=torch.tensor([-0.6677, -0.1873, -0.8379]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          a_									=torch.tensor([-0.7749, -0.2967, -0.6444]							)
     else:
          raise ValueError(F"""Unknown URL: {checkpoint_url}"""							)

     assert torch.allclose(logits[0, :3]							,						lowercase__							,						atol=1E-3							)
     assert logits.shape == expected_shape

     Path(lowercase__							).mkdir(exist_ok=lowercase__							)
     print(F"""Saving model to {pytorch_dump_folder_path}"""							)
     model.save_pretrained(lowercase__							)
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
     image_processor.save_pretrained(lowercase__							)

     print("Pushing model to the hub..."							)
     a_									="convnext"
     if "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "small" in checkpoint_url:
          model_name += "-small"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
     elif "large" in checkpoint_url:
          model_name += "-large"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"

     model.push_to_hub(
         repo_path_or_name=Path(lowercase__							,						lowercase__							)							,						organization="nielsr"							,						commit_message="Add model"							,						)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
      type=str,
      help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to the output PyTorch model directory.''',
  )

  lowercase											=      parser.parse_args()
  convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


