
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple       ):
						lowercase       :    Optional[int]											=  fname.split(os.path.sep       )[-1]
						return re.search(r'''^(.*)_\d+\.jpg$'''							,			UpperCAmelCase_       ).groups()[0]







class 							UpperCAmelCase	(   A__					):

					def __init__(			self      :					Any ,      lowerCAmelCase      :					List[str] ,      lowerCAmelCase      :					List[Any]=None ,      lowerCAmelCase      :					Optional[Any]=None					):
											lowercase       :    Optional[Any]											=  file_names
											lowercase       :    int											=  image_transform
											lowercase       :    Optional[Any]											=  label_to_id

					def __len__(			self      :					List[Any]					):
											return len(self.file_names					)




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





def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				Optional[int]       ):
						lowercase       :    int											=  Accelerator(cpu=args.cpu							,			mixed_precision=args.mixed_precision       )

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

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

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

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

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

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

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

						# Instantiate dataloaders.
						lowercase       :    List[Any]											=  DataLoader(UpperCAmelCase_							,			shuffle=UpperCAmelCase_							,			batch_size=UpperCAmelCase_							,			num_workers=4       )
						lowercase       :    Tuple											=  DataLoader(UpperCAmelCase_							,			shuffle=UpperCAmelCase_							,			batch_size=UpperCAmelCase_							,			num_workers=4       )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase       :    Optional[int]											=  create_model('''resnet50d'''							,			pretrained=UpperCAmelCase_							,			num_classes=len(UpperCAmelCase_       )       )

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

						# Freezing the base model
						for param in model.parameters():
												lowercase       :    int											=  False
						for param in model.get_classifier().parameters():
												lowercase       :    Dict											=  True

						# We normalize the batches of images to be a bit faster.
						lowercase       :    Optional[int]											=  torch.tensor(model.default_cfg['''mean''']       )[None, :, None, None].to(accelerator.device       )
						lowercase       :    int											=  torch.tensor(model.default_cfg['''std''']       )[None, :, None, None].to(accelerator.device       )

						# Instantiate optimizer
						lowercase       :    Any											=  torch.optim.Adam(params=model.parameters()							,			lr=lr / 25       )

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

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						lowercase				,				lowercase				,				lowercase				,				lowercase				,				lowercase       :    Union[str, Any]											=  accelerator.prepare(
						    UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_       )

						# Now we train the model
						for epoch in range(UpperCAmelCase_       ):
												model.train()
												for step, batch in enumerate(UpperCAmelCase_       ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		lowercase       :    Tuple											=  {k: v.to(accelerator.device       ) for k, v in batch.items()}
																		lowercase       :    str											=  (batch['''image'''] - mean) / std
																		lowercase       :    Any											=  model(UpperCAmelCase_       )
																		lowercase       :    Dict											=  torch.nn.functional.cross_entropy(UpperCAmelCase_							,			batch['''label''']       )
																		accelerator.backward(UpperCAmelCase_       )
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												lowercase       :    Optional[int]											=  0
												lowercase       :    int											=  0
												for _, batch in enumerate(UpperCAmelCase_       ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		lowercase       :    Optional[int]											=  {k: v.to(accelerator.device       ) for k, v in batch.items()}
																		lowercase       :    List[str]											=  (batch['''image'''] - mean) / std
																		with torch.no_grad():
																								lowercase       :    Any											=  model(UpperCAmelCase_       )
																		lowercase       :    Dict											=  outputs.argmax(dim=-1       )
																		lowercase				,				lowercase       :    Optional[Any]											=  accelerator.gather_for_metrics((predictions, batch['''label'''])       )
																		lowercase       :    Any											=  predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												lowercase       :    Dict											=  accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}: {1_00 * eval_metric:.2f}'''       )

def        lowerCamelCase_      (			):
						lowercase       :    List[str]											=  argparse.ArgumentParser(description='''Simple example of training script.'''       )
						parser.add_argument('''--data_dir'''							,			required=UpperCAmelCase_							,			help='''The data folder on disk.'''       )
						parser.add_argument(
						    '''--mixed_precision'''							,			type=UpperCAmelCase_							,			default=UpperCAmelCase_							,			choices=['''no''', '''fp16''', '''bf16''', '''fp8''']							,			help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''							,			)
						parser.add_argument(
						    '''--checkpointing_steps'''							,			type=UpperCAmelCase_							,			default=UpperCAmelCase_							,			help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''							,			)
						parser.add_argument('''--cpu'''							,			action='''store_true'''							,			help='''If passed, will train on the CPU.'''       )
						lowercase       :    Optional[int]											=  parser.parse_args()
						lowercase       :    str											=  {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
						training_function(UpperCAmelCase_							,			UpperCAmelCase_       )


if __name__ == "__main__":
				main()




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Optional[Any]							,			UpperCAmelCase_							:				int=0       ):
						# Format the message.
						if name is None:
												lowercase       :    List[str]											=  None
						else:
												lowercase       :    str											=  '''.''' * max(0							,			spaces - 2       ) + '''# {:''' + str(50 - spaces       ) + '''s}'''
												lowercase       :    List[str]											=  fmt.format(UpperCAmelCase_       )

						# Print and recurse (if needed).
						if isinstance(UpperCAmelCase_							,			UpperCAmelCase_       ):
												if msg is not None:
																		print(UpperCAmelCase_       )
												for k in val.keys():
																		recursive_print(UpperCAmelCase_							,			val[k]							,			spaces + 2       )
						elif isinstance(UpperCAmelCase_							,			torch.Tensor       ):
												print(UpperCAmelCase_							,			''':'''							,			val.size()       )
						else:
												print(UpperCAmelCase_							,			''':'''							,			UpperCAmelCase_       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				Tuple       ):
						# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
						# for compatibility with later versions of NVIDIA Megatron-LM.
						# The inverse operation is performed inside Megatron-LM to read checkpoints:
						# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
						# If param is the weight tensor of the self-attention block, the returned tensor
						# will have to be transposed one more time to be read by HuggingFace GPT2.
						lowercase       :    Optional[Any]											=  param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						lowercase       :    Any											=  (num_heads, num_splits, hidden_size) + input_shape[1:]
						lowercase       :    Optional[int]											=  param.view(*UpperCAmelCase_       )
						lowercase       :    List[Any]											=  param.transpose(0							,			1       ).contiguous()
						lowercase       :    int											=  param.view(*UpperCAmelCase_       )
						return param

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    str											=  config.n_positions
						lowercase       :    Union[str, Any]											=  config.n_layer
						lowercase       :    Optional[int]											=  config.vocab_size
						lowercase       :    Union[str, Any]											=  config.n_head
						lowercase       :    int											=  config.n_embd // config.n_head

						lowercase       :    Optional[Any]											=  sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
						lowercase       :    Optional[int]											=  {
						    '''transformer.wte.weight''': word_embeddings,
						    '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
						    '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
						    '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
						}

						lowercase       :    Dict											=  '''model.language_model.encoder.layers.'''
						for i in range(UpperCAmelCase_       ):
												lowercase       :    List[str]											=  torch.tril(torch.ones((n_positions, n_positions)							,			dtype=torch.bool       )       )
												lowercase       :    List[str]											=  causal_mask.view(1							,			1							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    List[str]											=  causal_mask
												lowercase       :    Optional[int]											=  torch.tensor(-1E4							,			dtype=torch.bfloataa       )

												lowercase       :    List[str]											=  sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

												lowercase       :    Any											=  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
												lowercase       :    Optional[int]											=  fix_query_key_value_ordering(UpperCAmelCase_							,			3							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    Tuple											=  vala.transpose(0							,			1       ).contiguous()

												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
												lowercase       :    Any											=  fix_query_key_value_ordering(UpperCAmelCase_							,			3							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    int											=  vala

												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
												    0							,			1       )
												lowercase       :    Optional[int]											=  sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
												lowercase       :    Union[str, Any]											=  sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
												lowercase       :    Optional[Any]											=  sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0							,			1       )
												lowercase       :    List[Any]											=  sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
												lowercase       :    Dict											=  sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
												    0							,			1       )
												lowercase       :    str											=  sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

						# For LM head, transformers' wants the matrix to weight embeddings.
						lowercase       :    int											=  word_embeddings

						return sd_hf

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				str       ):
						lowercase       :    str											=  6_40_00
						lowercase       :    Union[str, Any]											=  config_megatron['''encoder_seq_length''']
						lowercase       :    Union[str, Any]											=  config_megatron['''hidden_size''']
						lowercase       :    Optional[Any]											=  config_megatron['''num_layers''']
						lowercase       :    int											=  config_megatron['''num_attention_heads''']
						lowercase       :    str											=  config_megatron['''ffn_hidden_size''']
						lowercase       :    Union[str, Any]											=  '''gelu'''
						lowercase       :    Tuple											=  0.1
						lowercase       :    str											=  0.1
						lowercase       :    str											=  0.1
						lowercase       :    Dict											=  config_megatron['''layernorm_epsilon''']  # 1e-5
						lowercase       :    str											=  config_megatron['''init_method_std''']  # 0.02
						lowercase       :    Optional[int]											=  config_megatron['''apply_query_key_layer_scaling''']  # True
						lowercase       :    Union[str, Any]											=  True
						lowercase       :    int											=  True

						# This identifies the 6.7B (7B) model which uses a different tokenizer
						if config_megatron["hidden_size"] == 40_96:
												lowercase       :    Dict											=  1  # <|endoftext|>
												lowercase       :    str											=  1  # <|endoftext|>
												lowercase       :    Dict											=  0  # <unk>
						else:
												lowercase       :    List[Any]											=  2  # <s>
												lowercase       :    List[str]											=  3  # <|endoftext|>
												lowercase       :    Optional[Any]											=  0  # <pad>

						return config_hf

def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple       ):
						print(UpperCAmelCase_       )

						lowercase       :    List[Any]											=  args.checkpoint_path
						lowercase       :    Dict											=  args.save_path
						if isfile(UpperCAmelCase_       ):
												raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''       )

						# Load the model.
						lowercase       :    Optional[int]											=  torch.load(UpperCAmelCase_							,			map_location='''cpu'''       )

						# Load the config.
						lowercase       :    Any											=  checkpoint['''hyper_parameters''']['''cfg''']
						lowercase       :    Dict											=  GPTaConfig()
						lowercase       :    Dict											=  copy_config(config_hf=UpperCAmelCase_							,			config_megatron=UpperCAmelCase_       )
						lowercase       :    Any											=  ['''GPT2LMHeadModel''']

						lowercase       :    List[Any]											=  checkpoint['''state_dict''']

						# Convert.
						print('''Converting'''       )
						lowercase       :    Dict											=  convert_megatron_checkpoint(UpperCAmelCase_							,			UpperCAmelCase_       )

						# Print the structure of converted state dict.
						if args.print_checkpoint_structure:
												recursive_print(UpperCAmelCase_							,			UpperCAmelCase_       )

						lowercase       :    List[Any]											=  '''GPTSw3Tokenizer'''

						# Store the config to file.
						print('''Saving config'''       )
						config_hf.save_pretrained(UpperCAmelCase_       )

						# Store the state_dict to file.
						lowercase       :    Union[str, Any]											=  os.path.join(UpperCAmelCase_							,			'''pytorch_model.bin'''       )
						print(f'''Saving checkpoint to "{output_checkpoint_file}"'''       )
						torch.save(UpperCAmelCase_							,			UpperCAmelCase_       )


if __name__ == "__main__":
				snake_case__  							=							argparse.ArgumentParser()
				parser.add_argument(
				    """--checkpoint_path""",
				    type=str,
				    required=True,
				    help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
				)
				parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
				parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
				snake_case__  							=							parser.parse_args()
				main(_args)


