"""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 			_lowerCamelCase      (_UpperCamelCase  ):







				def __init__(			self     :							Union[str, Any]      ,       lowerCamelCase_     :							Dict      ,       lowerCamelCase_     :							Optional[Any]      ,       lowerCamelCase_     :							Optional[int]=False      ,       lowerCamelCase_     :							Optional[Any]=1_0_2_4      ,       lowerCamelCase_     :							str=1_0_2_4      ,       lowerCamelCase_     :							Any=3.6      ,       lowerCamelCase_     :							List[Any]=False      ,       ):

					"""simple docstring"""






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

					if self.tokenized:
						_lowercase			:     Optional[int]      					=				seq_length * num_of_sequences
						_lowercase			:     str      					=				'input_ids'
					else:
						_lowercase			:     Optional[int]      					=				seq_length * chars_per_token * num_of_sequences
						_lowercase			:     int      					=				'content'







				def __iter__(			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     Optional[Any]      					=				iter(self.dataset    )
					_lowercase			:     Optional[int]      					=				True
					while more_examples:
						_lowercase					,   _lowercase			:     Optional[int]      					=				[], 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			:     Optional[Any]      					=				iter(self.dataset    )
									self.epoch += 1
									logger.info(F'''Dataset epoch: {self.epoch}'''    )
								else:
									_lowercase			:     Tuple      					=				False
									break
						if self.tokenized:
							_lowercase			:     int      					=				buffer
						else:
							_lowercase			:     int      					=				self.tokenizer(__lowerCamelCase      ,       truncation=__lowerCamelCase    )['input_ids']
						_lowercase			:     Dict      					=				[]
						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			:     Optional[Any]      					=				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     :							List[str]      ,       lowerCamelCase_     :							Optional[int]=1_0_0_0    ):

					"""simple docstring"""






					return ShufflerIterDataPipe(self      ,       buffer_size=__lowerCamelCase    )



def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





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


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):






	"""simple docstring"""





	_lowercase					,   _lowercase			:     Dict      					=				[], []
	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   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Any      					=				accelerator.unwrap_model(__UpperCAmelCase      ).config
	_lowercase			:     Any      					=				4 if args.gradient_checkpointing else 3
	_lowercase			:     Tuple      					=				args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
	_lowercase			:     Optional[Any]      					=				24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
	_lowercase			:     Union[str, 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			:     Optional[int]      					=				flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
	return tflops


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	model.eval()
	_lowercase			:     str      					=				[]
	for step, batch in enumerate(__UpperCAmelCase      ):
		with torch.no_grad():
			_lowercase			:     Optional[int]      					=				model(__UpperCAmelCase  ,labels=__UpperCAmelCase      )
		_lowercase			:     Any      					=				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
	_lowercase			:     int      					=				torch.cat(__UpperCAmelCase      )
	_lowercase			:     Optional[Any]      					=				losses[: eval_dataloader.dataset.current_size].mean()
	try:
		_lowercase			:     Any      					=				torch.exp(__UpperCAmelCase      )
	except OverflowError:
		_lowercase			:     Union[str, Any]      					=				float('inf'      )
	return loss.item(), perplexity.item()


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

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

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

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

# Logging
SCREAMING_SNAKE_CASE						=    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						=    AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE						=    AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE						=    create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE						=    AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE						=    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   __lowerCAmelCase(		):






	"""simple docstring"""





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


# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE						=    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						=    os.path.basename(args.resume_from_checkpoint)
						else:
												# Get the most recent checkpoint
												SCREAMING_SNAKE_CASE						=    [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						=    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						=    os.path.splitext(path)[0]
						SCREAMING_SNAKE_CASE						=    int(training_difference.replace('step_', ''))

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









import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel




def      __magic_name__							(					A						:   str,    A						:   List[str],    A						:   int  ):






			'''simple docstring'''







			a       ,						a       ,						a       =     pt_weights[F"""{prefix}.in_proj_weight"""].chunk(3,    dim=0  )
			a       ,						a       ,						a       =     pt_weights[F"""{prefix}.in_proj_bias"""].chunk(3,    dim=0  )

			a       =     pt_weights[F"""{prefix}.out_proj.weight"""]
			a       =     pt_weights[F"""{prefix}.out_proj.bias"""]

			a       =     q_proj
			a       =     q_proj_bias

			a       =     k_proj
			a       =     k_proj_bias

			a       =     v_proj
			a       =     v_proj_bias

			a       =     out_proj_weights
			a       =     out_proj_bias




def      __magic_name__							(					A						:   Optional[int],    A						:   str,    A						:   Union[str, Any]  ):






			'''simple docstring'''







			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_fc"""  )
			copy_linear(hf_mlp.fca,    A,    F"""{prefix}.c_proj"""  )




def      __magic_name__							(					A						:   int,    A						:   Tuple,    A						:   Optional[int]  ):






			'''simple docstring'''







			a       =     pt_weights[F"""{prefix}.weight"""].data
			a       =     pt_weights[F"""{prefix}.bias"""].data




def      __magic_name__							(					A						:   List[Any],    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			copy_linear(hf_layer.layer_norma,    A,    F"""{prefix}.ln_1"""  )
			copy_linear(hf_layer.layer_norma,    A,    F"""{prefix}.ln_2"""  )

			# copy MLP
			copy_mlp(hf_layer.mlp,    A,    F"""{prefix}.mlp"""  )

			# copy attn
			copy_attn_layer(hf_layer.self_attn,    A,    F"""{prefix}.attn"""  )




def      __magic_name__							(					A						:   int,    A						:   List[str],    A						:   Any  ):






			'''simple docstring'''







			for layer_id, hf_layer in enumerate(A  ):
						copy_layer(A,    A,    F"""{prefix}.{layer_id}"""  )




def      __magic_name__							(					A						:   Any,    A						:   Any  ):






			'''simple docstring'''







			a       =     pt_weights["text_projection"].data.T

			# copy text encoder
			for name, param in hf_model.text_model.named_parameters():
						a       =     pt_weights[F"""bert.{name}"""].data




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any]  ):






			'''simple docstring'''







			a       =     pt_weights["visual.proj"].data.T

			# copy layer norms
			copy_linear(hf_model.vision_model.pre_layrnorm,    A,    "visual.ln_pre"  )
			copy_linear(hf_model.vision_model.post_layernorm,    A,    "visual.ln_post"  )

			# copy embeddings
			a       =     pt_weights["visual.conv1.weight"].data
			a       =     pt_weights["visual.class_embedding"].data
			a       =     pt_weights["visual.positional_embedding"].data

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




@torch.no_grad()
def      __magic_name__							(					A						:   Optional[Any],    A						:   List[str],    A						:   Dict=None  ):






			'''simple docstring'''







			assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
			a       =     ChineseCLIPConfig.from_pretrained(A  )

			a       =     ChineseCLIPModel(A  ).eval()

			a       =     torch.load(A,    map_location="cpu"  )["state_dict"]
			a       =     {(name[7:] if name.startswith("module."  ) else name): value for name, value in pt_weights.items()}

			copy_text_model_and_projection(A,    A  )
			copy_vision_model_and_projection(A,    A  )
			a       =     pt_weights["logit_scale"].data

			hf_model.save_pretrained(A  )


if __name__ == "__main__":
		__lowerCAmelCase						:							Dict			     =						argparse.ArgumentParser()
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    help='Path to the output folder storing converted hf PyTorch model.',
		)
		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, help='Path to original github format ChineseCLIP checkpoint.'
		)
		parser.add_argument(
		    '--config_path', default=None, required=True, type=str, help='Path to hf config.json of model to convert.'
		)
		__lowerCAmelCase						:							Dict			     =						parser.parse_args()

		convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
		print('The conversion is finished!')






