


'''simple docstring'''







import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_SCREAMING_SNAKE_CASE       :   Optional[Any]        = {
    "vit_b32": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 32,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 16,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 7_68,
        "image_resolution": 8_40,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 24,
        "vision_width": 10_24,
        "vision_patch_size": 14,
        "transformer_width": 7_68,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__=""     ,   UpperCamelCase__="/"		):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       []

				for k, v in params.items():
								__magic_name__				:					str						       =       parent_key + sep + k if parent_key else k

								if isinstance(UpperCamelCase__     ,   collections.MutableMapping		):
												items.extend(flatten_nested_dict(UpperCamelCase__     ,   UpperCamelCase__     ,   sep=UpperCamelCase__		).items()		)
								else:
												items.append((new_key, v)		)
				return dict(UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				return jax.tree_util.tree_map(lambda UpperCamelCase__		: x.astype(jnp.floataa		) if x.dtype == jnp.bfloataa else x     ,   UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__       ,     __magic_name__       ,     __magic_name__				:					Any						       =       pt_attn_layer.in_proj_weight.chunk(3     ,   dim=0		)
				__magic_name__       ,     __magic_name__       ,     __magic_name__				:					Optional[int]						       =       pt_attn_layer.in_proj_bias.chunk(3     ,   dim=0		)

				__magic_name__				:					int						       =       pt_attn_layer.out_proj.weight
				__magic_name__				:					List[Any]						       =       pt_attn_layer.out_proj.bias

				__magic_name__				:					Any						       =       q_proj
				__magic_name__				:					Any						       =       q_proj_bias

				__magic_name__				:					Dict						       =       k_proj
				__magic_name__				:					Union[str, Any]						       =       k_proj_bias

				__magic_name__				:					Tuple						       =       v_proj
				__magic_name__				:					Optional[int]						       =       v_proj_bias

				__magic_name__				:					Dict						       =       out_proj_weights
				__magic_name__				:					Any						       =       out_proj_bias



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				copy_linear(hf_mlp.fca     ,   pt_mlp.c_fc		)
				copy_linear(hf_mlp.fca     ,   pt_mlp.c_proj		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[str]						       =       pt_linear.weight
				__magic_name__				:					Union[str, Any]						       =       pt_linear.bias



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				copy_linear(hf_layer.layer_norma     ,   pt_layer.ln_a		)
				copy_linear(hf_layer.layer_norma     ,   pt_layer.ln_a		)

				# copy MLP
				copy_mlp(hf_layer.mlp     ,   pt_layer.mlp		)

				# copy attn
				copy_attn_layer(hf_layer.self_attn     ,   pt_layer.attn		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				for hf_layer, pt_layer in zip(UpperCamelCase__     ,   UpperCamelCase__		):
								copy_layer(UpperCamelCase__     ,   UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       pt_model.token_embedding.weight
				__magic_name__				:					Tuple						       =       pt_model.positional_embedding

				# copy layer norm
				copy_linear(hf_encoder.final_layer_norm     ,   pt_model.ln_final		)

				# copy hidden layers
				copy_layers(hf_encoder.encoder.layers     ,   pt_model.transformer.resblocks		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       pt_model.text_projection.data.T

				# copy text encoder
				copy_encoder(hf_model.text_model     ,   UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Tuple						       =       pt_model.visual.proj.data.T

				# copy layer norms
				copy_linear(hf_model.vision_model.pre_layernorm     ,   pt_model.visual.ln_pre		)
				copy_linear(hf_model.vision_model.post_layernorm     ,   pt_model.visual.ln_post		)

				# copy embeds
				__magic_name__				:					Dict						       =       pt_model.visual.conva.weight.data
				__magic_name__				:					List[str]						       =       pt_model.visual.class_embedding
				__magic_name__				:					Dict						       =       pt_model.visual.positional_embedding.data

				# copy encoder
				copy_layers(hf_model.vision_model.encoder.layers     ,   pt_model.visual.transformer.resblocks		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					int						       =       flatten_nested_dict(flax_params["backbone"]["merged_class_token"]		)

				__magic_name__				:					Any						       =       torch.from_numpy(flax_class_token_params["scale"]		)
				__magic_name__				:					Union[str, Any]						       =       torch.from_numpy(flax_class_token_params["bias"]		)
				__magic_name__				:					Optional[int]						       =       nn.Parameter(UpperCamelCase__		)
				__magic_name__				:					Tuple						       =       nn.Parameter(UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					int						       =       hf_model.state_dict()
				__magic_name__				:					Optional[int]						       =       {}

				# Rename class prediction head flax params to pytorch HF
				__magic_name__				:					Any						       =       flatten_nested_dict(flax_params["class_head"]		)

				for flax_key, v in flax_class_params.items():
								__magic_name__				:					List[Any]						       =       flax_key.replace("/"     ,   "."		)
								__magic_name__				:					Any						       =       torch_key.replace(".kernel"     ,   ".weight"		)
								__magic_name__				:					Dict						       =       torch_key.replace("Dense_0"     ,   "dense0"		)
								__magic_name__				:					Optional[Any]						       =       "class_head." + torch_key

								if "weight" in torch_key and v.ndim == 2:
												__magic_name__				:					Union[str, Any]						       =       v.T

								__magic_name__				:					Optional[int]						       =       nn.Parameter(torch.from_numpy(UpperCamelCase__		)		)

				# Rename box prediction box flax params to pytorch HF
				__magic_name__				:					Tuple						       =       flatten_nested_dict(flax_params["obj_box_head"]		)

				for flax_key, v in flax_box_params.items():
								__magic_name__				:					Tuple						       =       flax_key.replace("/"     ,   "."		)
								__magic_name__				:					Any						       =       torch_key.replace(".kernel"     ,   ".weight"		)
								__magic_name__				:					int						       =       torch_key.replace("_"     ,   ""		).lower()
								__magic_name__				:					Optional[int]						       =       "box_head." + torch_key

								if "weight" in torch_key and v.ndim == 2:
												__magic_name__				:					int						       =       v.T

								__magic_name__				:					Tuple						       =       nn.Parameter(torch.from_numpy(UpperCamelCase__		)		)

				# Copy flax params to PyTorch params
				for name, param in new_params.items():
								if name in pt_params.keys():
												pt_params[name].copy_(UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				for k, v in flax_attn_params.items():
								if k.startswith("transformer"		):
												__magic_name__				:					List[Any]						       =       k.replace("transformer.resblocks"     ,   "text_model.encoder.layers"		)
								else:
												__magic_name__				:					str						       =       k.replace("visual.transformer.resblocks"     ,   "vision_model.encoder.layers"		)

								__magic_name__				:					Dict						       =       torch_key.replace("attn"     ,   "self_attn"		)
								__magic_name__				:					Optional[Any]						       =       torch_key.replace("key"     ,   "k_proj"		)
								__magic_name__				:					Dict						       =       torch_key.replace("value"     ,   "v_proj"		)
								__magic_name__				:					Union[str, Any]						       =       torch_key.replace("query"     ,   "q_proj"		)
								__magic_name__				:					Optional[Any]						       =       torch_key.replace("out"     ,   "out_proj"		)

								if "bias" in torch_key and v.ndim == 2:
												__magic_name__				:					str						       =       v.shape[0] * v.shape[1]
												__magic_name__				:					Dict						       =       v.reshape(UpperCamelCase__		)

								if "weight" in torch_key and "out" in torch_key:
												__magic_name__				:					Optional[Any]						       =       (v.shape[0] * v.shape[1], v.shape[2])
												__magic_name__				:					Any						       =       v.reshape(UpperCamelCase__		).T

								if "weight" in torch_key and "out" not in torch_key:
												__magic_name__				:					List[Any]						       =       (v.shape[0], v.shape[1] * v.shape[2])
												__magic_name__				:					Tuple						       =       v.reshape(UpperCamelCase__		).T

								# Copy flax CLIP attn params to HF PyTorch params
								__magic_name__				:					Optional[int]						       =       torch.from_numpy(UpperCamelCase__		)
								hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Dict						       =       {}
				__magic_name__				:					Optional[int]						       =       []

				for k, v in params.items():
								if "attn." in k:
												__magic_name__				:					Union[str, Any]						       =       k[: k.rindex("attn."		) + 5]
												if base in processed_attn_layers:
																continue

												processed_attn_layers.append(UpperCamelCase__		)
												__magic_name__				:					str						       =       params[base + "out.weight"].shape[-1]
												__magic_name__				:					Any						       =       params[base + "out.weight"].reshape(UpperCamelCase__     ,   UpperCamelCase__		).T
												__magic_name__				:					Optional[int]						       =       params[base + "out.bias"]
								else:
												__magic_name__				:					Dict						       =       v
				return new_params



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					int						       =       CLIP(**UpperCamelCase__		)
				torch_model.eval()
				__magic_name__				:					Dict						       =       torch_model.state_dict()

				__magic_name__				:					Optional[int]						       =       flatten_nested_dict(flax_params["backbone"]["clip"]		)
				__magic_name__				:					Dict						       =       {}

				for flax_key, v in flax_clip_params.items():
								__magic_name__				:					int						       =       flax_key.replace("/"     ,   "."		)
								__magic_name__				:					int						       =       torch_key.replace("text.token_embedding.embedding"     ,   "token_embedding.kernel"		)

								if (
								    torch_key.startswith("text.transformer"		)
								    or torch_key.startswith("text.text_projection"		)
								    or torch_key.startswith("text.ln_final"		)
								    or torch_key.startswith("text.positional_embedding"		)
								):
												__magic_name__				:					int						       =       torch_key[5:]

								__magic_name__				:					str						       =       torch_key.replace("text_projection.kernel"     ,   "text_projection"		)
								__magic_name__				:					Optional[int]						       =       torch_key.replace("visual.proj.kernel"     ,   "visual.proj"		)
								__magic_name__				:					List[str]						       =       torch_key.replace(".scale"     ,   ".weight"		)
								__magic_name__				:					Optional[int]						       =       torch_key.replace(".kernel"     ,   ".weight"		)

								if "conv" in torch_key or "downsample.0.weight" in torch_key:
												__magic_name__				:					int						       =       v.transpose(3     ,   2     ,   0     ,   1		)

								elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
												# Fully connected layers are transposed, embeddings are not
												__magic_name__				:					Optional[Any]						       =       v.T

								__magic_name__				:					Optional[Any]						       =       v

				__magic_name__				:					List[str]						       =       _convert_attn_layers(UpperCamelCase__		)
				new_torch_params.update(UpperCamelCase__		)
				__magic_name__				:					Optional[Any]						       =       {}

				# Copy flax CLIP backbone params to PyTorch params
				for name, param in new_torch_params.items():
								if name in torch_clip_params.keys():
												__magic_name__				:					Union[str, Any]						       =       torch.from_numpy(new_torch_params[name]		)
												torch_clip_params[name].copy_(UpperCamelCase__		)
								else:
												__magic_name__				:					Optional[int]						       =       param

				return torch_clip_params, torch_model, attn_params



@torch.no_grad()
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None		):






				"""simple docstring"""



				__magic_name__				:					Any						       =       Repository(UpperCamelCase__     ,   clone_from=F"""google/{pytorch_dump_folder_path}"""		)
				repo.git_pull()

				if config_path is not None:
								__magic_name__				:					Any						       =       OwlViTConfig.from_pretrained(UpperCamelCase__		)
				else:
								__magic_name__				:					Any						       =       OwlViTConfig()

				__magic_name__				:					List[str]						       =       OwlViTModel(UpperCamelCase__		).eval()
				__magic_name__				:					Any						       =       OwlViTForObjectDetection(UpperCamelCase__		).eval()

				copy_text_model_and_projection(UpperCamelCase__     ,   UpperCamelCase__		)
				copy_vision_model_and_projection(UpperCamelCase__     ,   UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       pt_backbone.logit_scale
				copy_flax_attn_params(UpperCamelCase__     ,   UpperCamelCase__		)

				__magic_name__				:					str						       =       hf_backbone
				copy_class_merge_token(UpperCamelCase__     ,   UpperCamelCase__		)
				copy_class_box_heads(UpperCamelCase__     ,   UpperCamelCase__		)

				# Save HF model
				hf_model.save_pretrained(repo.local_dir		)

				# Initialize image processor
				__magic_name__				:					Optional[Any]						       =       OwlViTImageProcessor(
				    size=config.vision_config.image_size     ,   crop_size=config.vision_config.image_size		)
				# Initialize tokenizer
				__magic_name__				:					List[str]						       =       CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"     ,   pad_token="!"     ,   model_max_length=16		)

				# Initialize processor
				__magic_name__				:					Optional[int]						       =       OwlViTProcessor(image_processor=UpperCamelCase__     ,   tokenizer=UpperCamelCase__		)
				image_processor.save_pretrained(repo.local_dir		)
				processor.save_pretrained(repo.local_dir		)

				repo.git_add()
				repo.git_commit("Upload model and processor"		)
				repo.git_push()


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE       :   str        = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--owlvit_version",
		    default=None,
		    type=str,
		    required=True,
		    help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
		)
		parser.add_argument(
		    "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
		)
		parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
		parser.add_argument(
		    "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
		)
		_SCREAMING_SNAKE_CASE       :   Dict        = parser.parse_args()

		# Initialize PyToch clip model
		_SCREAMING_SNAKE_CASE       :   Optional[int]        = args.owlvit_version
		if model_name == "clip_b16":
				_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = CONFIGS["vit_b16"]
		elif model_name == "clip_b32":
				_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = CONFIGS["vit_b32"]
		elif model_name == "clip_l14":
				_SCREAMING_SNAKE_CASE       :   Any        = CONFIGS["vit_l14"]

		# Load from checkpoint and convert params to float-32
		_SCREAMING_SNAKE_CASE       :   Dict        = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
		_SCREAMING_SNAKE_CASE       :   Any        = jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
		del variables

		# Convert CLIP backbone
		_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Optional[int]        = convert_clip_backbone(flax_params, torch_config)

		convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)



'''simple docstring'''







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    (				snake_case_		):






						'''simple docstring'''







						def __init__(  self:	List[Any]      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	Optional[Any]      ,							__UpperCamelCase:	Optional[int]=False      ,							__UpperCamelCase:	Optional[int]=1024      ,							__UpperCamelCase:	Union[str, Any]=1024      ,							__UpperCamelCase:	Optional[int]=3.6      ,							__UpperCamelCase:	str=False      ,							)							->	Optional[int]:
										__magic_name__				:					Union[str, Any]						       =       tokenizer
										__magic_name__				:					Any						       =       tokenizer.bos_token_id
										__magic_name__				:					Optional[int]						       =       dataset
										__magic_name__				:					List[Any]						       =       seq_length
										__magic_name__				:					Union[str, Any]						       =       0
										__magic_name__				:					Dict						       =       infinite
										__magic_name__				:					Any						       =       0
										__magic_name__				:					int						       =       tokenized

										if self.tokenized:
														__magic_name__				:					Dict						       =       seq_length * num_of_sequences
														__magic_name__				:					Optional[Any]						       =       "input_ids"
										else:
														__magic_name__				:					str						       =       seq_length * chars_per_token * num_of_sequences
														__magic_name__				:					Tuple						       =       "content"







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







						def lowerCAmelCase__ (  self:	Tuple      ,							__UpperCamelCase:	str=1000     )							->	List[str]:
										return ShufflerIterDataPipe(self      ,							buffer_size=__UpperCamelCase     )






def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       args.model_ckpt.split("/"		)[-1]
				__magic_name__				:					Union[str, Any]						       =       logging.getLogger(__name__		)
				__magic_name__				:					Optional[int]						       =       Path(args.save_dir		) / "log/"
				log_dir.mkdir(exist_ok=UpperCamelCase__		)
				__magic_name__				:					List[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(UpperCamelCase__     ,   vars(UpperCamelCase__		)		)
								__magic_name__				:					Union[str, Any]						       =       accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO		)
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								__magic_name__				:					Dict						       =       ""
								logger.setLevel(logging.ERROR		)
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Tuple						       =       {"streaming": True}
				__magic_name__				:					Any						       =       load_dataset(args.dataset_name_train     ,   split="train"     ,   **UpperCamelCase__		)
				__magic_name__				:					Any						       =       train_data.shuffle(buffer_size=args.shuffle_buffer     ,   seed=args.seed		)
				__magic_name__				:					Optional[int]						       =       load_dataset(args.dataset_name_valid     ,   split="train"     ,   **UpperCamelCase__		)
				__magic_name__				:					int						       =       ConstantLengthDataset(
				    UpperCamelCase__     ,   UpperCamelCase__     ,   infinite=UpperCamelCase__     ,   seq_length=args.seq_length     ,   tokenized=args.tokenized		)
				__magic_name__				:					List[str]						       =       ConstantLengthDataset(
				    UpperCamelCase__     ,   UpperCamelCase__     ,   infinite=UpperCamelCase__     ,   seq_length=args.seq_length     ,   tokenized=args.tokenized		)
				__magic_name__				:					Tuple						       =       train_dataset.shuffle(buffer_size=args.shuffle_buffer		)
				__magic_name__				:					List[str]						       =       DataLoader(UpperCamelCase__     ,   batch_size=args.train_batch_size     ,   shuffle=UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       DataLoader(UpperCamelCase__     ,   batch_size=args.valid_batch_size		)
				return train_dataloader, eval_dataloader



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		):






				"""simple docstring"""



				__magic_name__       ,     __magic_name__				:					List[str]						       =       [], []
				for n, p in model.named_parameters():
								if any(nd in n for nd in no_decay		):
												params_without_wd.append(UpperCamelCase__		)
								else:
												params_with_wd.append(UpperCamelCase__		)
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				logger.info(F"""Step {step}: {metrics}"""		)
				if accelerator.is_main_process:
								accelerator.log(UpperCamelCase__     ,   UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					str						       =       accelerator.unwrap_model(UpperCamelCase__		).config
				__magic_name__				:					str						       =       4 if args.gradient_checkpointing else 3
				__magic_name__				:					Any						       =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				__magic_name__				:					int						       =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				__magic_name__				:					Optional[Any]						       =       factor * (
				    1.0
				    + (args.seq_length / (6.0 * config_model.n_embd))
				    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
				)
				__magic_name__				:					Union[str, Any]						       =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				model.eval()
				__magic_name__				:					str						       =       []
				for step, batch in enumerate(UpperCamelCase__		):
								with torch.no_grad():
												__magic_name__				:					Any						       =       model(UpperCamelCase__     ,   labels=UpperCamelCase__		)
								__magic_name__				:					str						       =       outputs.loss.repeat(args.valid_batch_size		)
								losses.append(accelerator.gather(UpperCamelCase__		)		)
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				__magic_name__				:					Tuple						       =       torch.cat(UpperCamelCase__		)
				__magic_name__				:					Tuple						       =       losses[: eval_dataloader.dataset.current_size].mean()
				try:
								__magic_name__				:					int						       =       torch.exp(UpperCamelCase__		)
				except OverflowError:
								__magic_name__				:					List[Any]						       =       float("inf"		)
				return loss.item(), perplexity.item()


# Settings
_SCREAMING_SNAKE_CASE       :   Optional[int]        = HfArgumentParser(TrainingArguments)
_SCREAMING_SNAKE_CASE       :   Any        = parser.parse_args()

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

_SCREAMING_SNAKE_CASE       :   Optional[Any]        = Namespace(**vars(args), **acc_state)
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = 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
_SCREAMING_SNAKE_CASE       :   Any        = AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_SCREAMING_SNAKE_CASE       :   Tuple        = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_SCREAMING_SNAKE_CASE       :   str        = 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 						_UpperCamelCase							( ):






				"""simple docstring"""



				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   List[Any]        = 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)
				_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				_SCREAMING_SNAKE_CASE       :   Any        = [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)
				_SCREAMING_SNAKE_CASE       :   str        = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		_SCREAMING_SNAKE_CASE       :   List[str]        = os.path.splitext(path)[0]
		_SCREAMING_SNAKE_CASE       :   Optional[Any]        = int(training_difference.replace("step_", ""))

# Train model
model.train()
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = 0
_SCREAMING_SNAKE_CASE       :   int        = time.time()
_SCREAMING_SNAKE_CASE       :   Optional[int]        = 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
		_SCREAMING_SNAKE_CASE       :   Tuple        = model(batch, labels=batch, use_cache=False).loss
		_SCREAMING_SNAKE_CASE       :   int        = 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()})
		_SCREAMING_SNAKE_CASE       :   str        = 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:
				_SCREAMING_SNAKE_CASE       :   str        = get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				_SCREAMING_SNAKE_CASE       :   Tuple        = time.time() - t_start
				_SCREAMING_SNAKE_CASE       :   List[str]        = 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,
				    },
				)
				_SCREAMING_SNAKE_CASE       :   int        = time.time()
				_SCREAMING_SNAKE_CASE       :   Tuple        = 0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("Evaluating and saving model checkpoint")
				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   str        = evaluate(args)
				log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
				accelerator.wait_for_everyone()
				_SCREAMING_SNAKE_CASE       :   Dict        = 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")
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Optional[int]        = evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
_SCREAMING_SNAKE_CASE       :   Optional[int]        = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_SCREAMING_SNAKE_CASE       :   List[Any]        = 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")