





"""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  _a						(      _lowerCAmelCase				):






				def __init__( self		:    str, lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    str=False, lowerCAmelCase__		:    Tuple=1_0_2_4, lowerCAmelCase__		:    Optional[int]=1_0_2_4, lowerCAmelCase__		:    Union[str, Any]=3.6, lowerCAmelCase__		:    str=False, )	->      List[str]:




										'''simple docstring'''



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

										if self.tokenized:
																_UpperCamelCase			:					Union[str, Any]     =    seq_length * num_of_sequences
																_UpperCamelCase			:					int     =    '''input_ids'''
										else:
																_UpperCamelCase			:					str     =    seq_length * chars_per_token * num_of_sequences
																_UpperCamelCase			:					Tuple     =    '''content'''






				def __iter__( self		:    Any							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    iter(self.dataset							)
										_UpperCamelCase			:					List[str]     =    True
										while more_examples:
																_UpperCamelCase   ,							_UpperCamelCase			:					Any     =    [], 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:
																																		_UpperCamelCase			:					List[str]     =    iter(self.dataset							)
																																		self.epoch += 1
																																		logger.info(f"""Dataset epoch: {self.epoch}"""							)
																												else:
																																		_UpperCamelCase			:					List[Any]     =    False
																																		break
																if self.tokenized:
																						_UpperCamelCase			:					Any     =    buffer
																else:
																						_UpperCamelCase			:					Optional[Any]     =    self.tokenizer(lowerCAmelCase__, truncation=lowerCAmelCase__							)['''input_ids''']
																_UpperCamelCase			:					Optional[Any]     =    []
																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							):
																						_UpperCamelCase			:					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 				snake_case	( self		:    Union[str, Any], lowerCAmelCase__		:    Any=1_0_0_0							)	->      Optional[int]:




										'''simple docstring'''



										return ShufflerIterDataPipe(self, buffer_size=lowerCAmelCase__							)





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





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





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   ):
						_UpperCamelCase   ,							_UpperCamelCase			:					Optional[Any]     =    [], []
						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_				(							_lowercase       ,						_lowercase   ):
						logger.info(F"""Step {step}: {metrics}"""   )
						if accelerator.is_main_process:
												accelerator.log(_lowercase       ,						_lowercase   )





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
						_UpperCamelCase			:					Union[str, Any]     =    accelerator.unwrap_model(_lowercase   ).config
						_UpperCamelCase			:					str     =    4 if args.gradient_checkpointing else 3
						_UpperCamelCase			:					Optional[int]     =    args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						_UpperCamelCase			:					Tuple     =    24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						_UpperCamelCase			:					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))
						)
						_UpperCamelCase			:					Tuple     =    flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops





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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
UpperCamelCase_           =AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCamelCase_           =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`.
UpperCamelCase_      ,	UpperCamelCase_      ,	UpperCamelCase_      ,	UpperCamelCase_           =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)
												UpperCamelCase_           =os.path.basename(args.resume_from_checkpoint)
						else:
												# Get the most recent checkpoint
												UpperCamelCase_           =[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)
												UpperCamelCase_           =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract the step of the checkpoint to continue from there
						UpperCamelCase_           =os.path.splitext(path)[0]
						UpperCamelCase_           =int(training_difference.replace("""step_""", """"""))

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


_lowerCamelCase       =	{
    '''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 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							List[Any]="" ,      lowercase_   :							List[Any]="/"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       []

 for k, v in params.items():
  __SCREAMING_SNAKE_CASE :			List[str]												=       parent_key + sep + k if parent_key else k

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




def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






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

 __SCREAMING_SNAKE_CASE :			Any												=       pt_attn_layer.out_proj.weight
 __SCREAMING_SNAKE_CASE :			str												=       pt_attn_layer.out_proj.bias

 __SCREAMING_SNAKE_CASE :			Any												=       q_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       q_proj_bias

 __SCREAMING_SNAKE_CASE :			Any												=       k_proj
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       k_proj_bias

 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj_bias

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       out_proj_weights
 __SCREAMING_SNAKE_CASE :			int												=       out_proj_bias




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							Dict		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_linear.weight
 __SCREAMING_SNAKE_CASE :			List[Any]												=       pt_linear.bias




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Any		):

 '''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 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Any		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.token_embedding.weight
 __SCREAMING_SNAKE_CASE :			str												=       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 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.text_projection.data.T

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




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       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
 __SCREAMING_SNAKE_CASE :			Dict												=       pt_model.visual.conva.weight.data
 __SCREAMING_SNAKE_CASE :			Tuple												=       pt_model.visual.class_embedding
 __SCREAMING_SNAKE_CASE :			Dict												=       pt_model.visual.positional_embedding.data

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




def 		lowerCAmelCase_	(  lowercase_   :							Union[str, Any] ,      lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']		)

 __SCREAMING_SNAKE_CASE :			Dict												=       torch.from_numpy(flax_class_token_params['''scale''']		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch.from_numpy(flax_class_token_params['''bias''']		)
 __SCREAMING_SNAKE_CASE :			Dict												=       nn.Parameter(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       nn.Parameter(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       hf_model.state_dict()
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {}

 # Rename class prediction head flax params to pytorch HF
 __SCREAMING_SNAKE_CASE :			Dict												=       flatten_nested_dict(flax_params['''class_head''']		)

 for flax_key, v in flax_class_params.items():
  __SCREAMING_SNAKE_CASE :			str												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Any												=       torch_key.replace('''Dense_0''' ,      '''dense0'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       '''class_head.''' + torch_key

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

  __SCREAMING_SNAKE_CASE :			List[str]												=       nn.Parameter(torch.from_numpy(lowercase_		)		)

 # Rename box prediction box flax params to pytorch HF
 __SCREAMING_SNAKE_CASE :			Any												=       flatten_nested_dict(flax_params['''obj_box_head''']		)

 for flax_key, v in flax_box_params.items():
  __SCREAMING_SNAKE_CASE :			Tuple												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''_''' ,      ''''''		).lower()
  __SCREAMING_SNAKE_CASE :			List[str]												=       '''box_head.''' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   __SCREAMING_SNAKE_CASE :			Dict												=       v.T

  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       nn.Parameter(torch.from_numpy(lowercase_		)		)

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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 for k, v in flax_attn_params.items():
  if k.startswith('''transformer'''		):
   __SCREAMING_SNAKE_CASE :			Optional[int]												=       k.replace('''transformer.resblocks''' ,      '''text_model.encoder.layers'''		)
  else:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       k.replace('''visual.transformer.resblocks''' ,      '''vision_model.encoder.layers'''		)

  __SCREAMING_SNAKE_CASE :			List[Any]												=       torch_key.replace('''attn''' ,      '''self_attn'''		)
  __SCREAMING_SNAKE_CASE :			Dict												=       torch_key.replace('''key''' ,      '''k_proj'''		)
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       torch_key.replace('''value''' ,      '''v_proj'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''query''' ,      '''q_proj'''		)
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch_key.replace('''out''' ,      '''out_proj'''		)

  if "bias" in torch_key and v.ndim == 2:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       v.shape[0] * v.shape[1]
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       v.reshape(lowercase_		)

  if "weight" in torch_key and "out" in torch_key:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       (v.shape[0] * v.shape[1], v.shape[2])
   __SCREAMING_SNAKE_CASE :			List[str]												=       v.reshape(lowercase_		).T

  if "weight" in torch_key and "out" not in torch_key:
   __SCREAMING_SNAKE_CASE :			Any												=       (v.shape[0], v.shape[1] * v.shape[2])
   __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       v.reshape(lowercase_		).T

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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       {}
 __SCREAMING_SNAKE_CASE :			int												=       []

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

   processed_attn_layers.append(lowercase_		)
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       params[base + '''out.weight'''].shape[-1]
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       params[base + '''out.weight'''].reshape(lowercase_ ,      lowercase_		).T
   __SCREAMING_SNAKE_CASE :			Dict												=       params[base + '''out.bias''']
  else:
   __SCREAMING_SNAKE_CASE :			Any												=       v
 return new_params




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       CLIP(**lowercase_		)
 torch_model.eval()
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch_model.state_dict()

 __SCREAMING_SNAKE_CASE :			str												=       flatten_nested_dict(flax_params['''backbone''']['''clip''']		)
 __SCREAMING_SNAKE_CASE :			str												=       {}

 for flax_key, v in flax_clip_params.items():
  __SCREAMING_SNAKE_CASE :			int												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       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'''		)
  ):
   __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key[5:]

  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch_key.replace('''text_projection.kernel''' ,      '''text_projection'''		)
  __SCREAMING_SNAKE_CASE :			str												=       torch_key.replace('''visual.proj.kernel''' ,      '''visual.proj'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''.scale''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Dict												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   __SCREAMING_SNAKE_CASE :			Optional[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
   __SCREAMING_SNAKE_CASE :			str												=       v.T

  __SCREAMING_SNAKE_CASE :			Any												=       v

 __SCREAMING_SNAKE_CASE :			Optional[int]												=       _convert_attn_layers(lowercase_		)
 new_torch_params.update(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {}

 # Copy flax CLIP backbone params to PyTorch params
 for name, param in new_torch_params.items():
  if name in torch_clip_params.keys():
   __SCREAMING_SNAKE_CASE :			Any												=       torch.from_numpy(new_torch_params[name]		)
   torch_clip_params[name].copy_(lowercase_		)
  else:
   __SCREAMING_SNAKE_CASE :			Tuple												=       param

 return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any] ,      lowercase_   :							str ,      lowercase_   :							Optional[Any] ,      lowercase_   :							List[Any]=None		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       Repository(lowercase_ ,      clone_from=F'''google/{pytorch_dump_folder_path}'''		)
 repo.git_pull()

 if config_path is not None:
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       OwlViTConfig.from_pretrained(lowercase_		)
 else:
  __SCREAMING_SNAKE_CASE :			int												=       OwlViTConfig()

 __SCREAMING_SNAKE_CASE :			List[str]												=       OwlViTModel(lowercase_		).eval()
 __SCREAMING_SNAKE_CASE :			Tuple												=       OwlViTForObjectDetection(lowercase_		).eval()

 copy_text_model_and_projection(lowercase_ ,      lowercase_		)
 copy_vision_model_and_projection(lowercase_ ,      lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_backbone.logit_scale
 copy_flax_attn_params(lowercase_ ,      lowercase_		)

 __SCREAMING_SNAKE_CASE :			Tuple												=       hf_backbone
 copy_class_merge_token(lowercase_ ,      lowercase_		)
 copy_class_box_heads(lowercase_ ,      lowercase_		)

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

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

 # Initialize processor
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       OwlViTProcessor(image_processor=lowercase_ ,      tokenizer=lowercase_		)
 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__":
    _lowerCamelCase       =	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.'''
    )
    _lowerCamelCase       =	parser.parse_args()

    # Initialize PyToch clip model
    _lowerCamelCase       =	args.owlvit_version
    if model_name == "clip_b16":
        _lowerCamelCase       =	CONFIGS['''vit_b16''']
    elif model_name == "clip_b32":
        _lowerCamelCase       =	CONFIGS['''vit_b32''']
    elif model_name == "clip_l14":
        _lowerCamelCase       =	CONFIGS['''vit_l14''']

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

    # Convert CLIP backbone
    _lowerCamelCase     ,		_lowerCamelCase     ,		_lowerCamelCase       =	convert_clip_backbone(flax_params, torch_config)

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

