





"""simple docstring"""





import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowerCamelCase       =	logging.getLogger(__name__)




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

 '''simple docstring'''






 random.seed(lowercase_		)
 np.random.seed(lowercase_		)
 torch.manual_seed(lowercase_		)
 torch.cuda.manual_seed_all(lowercase_		)




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

 '''simple docstring'''






 model.eval()
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       next(model.parameters()		).device
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       1
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch.zeros((eval_batch_size, context_len) ,      dtype=torch.long ,      device=lowercase_		)
 __SCREAMING_SNAKE_CASE :			Any												=       DataLoader(lowercase_ ,      shuffle=lowercase_ ,      batch_size=lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch.zeros(1 ,      device=lowercase_		)
 __SCREAMING_SNAKE_CASE :			int												=       0
 for batch in eval_dataloader:
  batch.to(lowercase_		)
  # pad
  context.zero_()
  for i in range(lowercase_		):
   __SCREAMING_SNAKE_CASE :			int												=       batch[i]
  __SCREAMING_SNAKE_CASE :			int												=       model(lowercase_ ,      labels=lowercase_		)
  eval_loss += outputs[0].sum().item()
  nb_eval_examples += batch.size(0		)
 __SCREAMING_SNAKE_CASE :			Any												=       eval_loss / nb_eval_examples
 __SCREAMING_SNAKE_CASE :			Dict												=       torch.exp(lowercase_		)
 model.train()
 return perplexity




def 		lowerCAmelCase_	(  lowercase_   :							Dict="gpt2"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       GPTaLMHeadModel.from_pretrained(lowercase_ ,      output_hidden_states=lowercase_		)
 torch.save(model.state_dict() ,      model_name + '''local.pt'''		)
 return model




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

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Any												=       copy.deepcopy(lowercase_		)
 model.to(lowercase_		)

 __SCREAMING_SNAKE_CASE :			str												=       ['''bias''', '''LayerNorm.weight''']
 __SCREAMING_SNAKE_CASE :			List[str]												=       [
     {
         '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
         '''weight_decay''': 0.0,
     },
     {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)], '''weight_decay''': 0.0},
 ]
 __SCREAMING_SNAKE_CASE :			str												=       AdamW(lowercase_ ,      lr=5E-5 ,      eps=1E-8		)
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       get_linear_schedule_with_warmup(lowercase_ ,      0 ,      lowercase_		)
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler




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

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       np.array(lowercase_		).mean()
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       np.array(lowercase_		).std()
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       (real_perps - avg) / std
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       list(zip(lowercase_ ,      lowercase_ ,      lowercase_		)		)
 joblib.dump(lowercase_ ,      lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							List[Any] ,      lowercase_   :							Dict ,      lowercase_   :							Dict ,      lowercase_   :							Optional[int] ,      lowercase_   :							List[str] ,      lowercase_   :							Tuple ,      lowercase_   :							str="dev.jbl" ,      lowercase_   :							Optional[int]=recopy_gpta ,      ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       []
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       []
 __SCREAMING_SNAKE_CASE :			List[Any]												=       []

 # Initialize the transformer model
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       copy.deepcopy(lowercase_		)
 orig_model.to(device='''cpu'''		)
 torch.cuda.empty_cache()

 # Compute perplexity of initial transformer model for comparison
 model.train()
 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       recopy_model(lowercase_ ,      lowercase_ ,      lowercase_		)

 for step in tqdm(range(lowercase_		)		):
  __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.zeros((1, context_len) ,      dtype=torch.long ,      device=lowercase_		)
  __SCREAMING_SNAKE_CASE :			Dict												=       random.choice(lowercase_		)
  __SCREAMING_SNAKE_CASE :			Any												=       random.randint(0 ,      len(story[0]		) - context_len - 1		)
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       story[0][start : start + context_len]
  lm_optimizer.zero_grad()
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(lowercase_ ,      labels=lowercase_		)
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       outputs[0]
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       compute_perplexity(lowercase_ ,      lowercase_ ,      lowercase_		)
  model.train()
  lm_loss.backward()
  # Do LM backprop
  torch.nn.utils.clip_grad_norm_(model.parameters() ,      3.0		)
  lm_optimizer.step()
  lm_scheduler.step()  # Update learning rate schedule

  # Compute perplexity after back-propagating on the selected context
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       compute_perplexity(lowercase_ ,      lowercase_ ,      lowercase_		)

  # Periodically save the stored (X, IG(X)) pairs
  if step % 1000 == 0 and step > 1:
   intermittent_save(lowercase_ ,      lowercase_ ,      lowercase_ ,      lowercase_		)

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       recopy_model(lowercase_ ,      lowercase_ ,      lowercase_		)

  past_perps.append(past_perp.item()		)
  real_perps.append(orig_perp - real_perp.item()		)
  contexts.append(np.array(context.cpu()		)		)

 intermittent_save(lowercase_ ,      lowercase_ ,      lowercase_ ,      lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Union[str, Any] ,      lowercase_   :							List[Any]="data/tokenized_stories_train_wikitext103.jbl" ,      lowercase_   :							List[str]=100 ,      lowercase_   :							Union[str, Any]=1026 ,      lowercase_   :							Any=True		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       joblib.load(lowercase_		)
 print('''data loaded'''		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       []
 if trim:
  for i, example in enumerate(lowercase_		):
   if len(example[0]		) > min_len:
    __SCREAMING_SNAKE_CASE :			List[Any]												=       random.randint(0 ,      len(example[0]		) - context_len - 1		)
    objective_set.append(example[0, start : start + context_len]		)
   if len(lowercase_		) >= number:
    break
  __SCREAMING_SNAKE_CASE :			List[Any]												=       []
  for j in range(i + 1 ,      len(lowercase_		)		):
   if len(data[j][0]		) > min_len:
    train_data.append(data[j]		)
 else:
  __SCREAMING_SNAKE_CASE :			int												=       data[0:number]
  __SCREAMING_SNAKE_CASE :			Dict												=       data[number:]

 joblib.dump(lowercase_ ,      '''objective_set.jbl'''		)
 print('''objective set saved'''		)
 return train_data, objective_set




def 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							Any ,      lowercase_   :							List[Any] ,      lowercase_   :							Union[str, Any] ,      lowercase_   :							List[Any]=50 ,      lowercase_   :							List[str]="secondary_learner.pt"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)
 # We will use the first 512 pairs from our dataset as a test set for
 # our secondary learner and the rest to train
 __SCREAMING_SNAKE_CASE :			Dict												=       train_dataset[:512]
 __SCREAMING_SNAKE_CASE :			Tuple												=       train_dataset[512:]
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       DataLoader(lowercase_ ,      shuffle=lowercase_ ,      batch_size=lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       DataLoader(lowercase_ ,      shuffle=lowercase_ ,      batch_size=lowercase_		)

 # secondary learner model set up
 __SCREAMING_SNAKE_CASE :			List[Any]												=       nn.MSELoss()
 __SCREAMING_SNAKE_CASE :			str												=       nn.MSELoss(reduction='''sum'''		)
 secondary_learner.to(lowercase_		)
 __SCREAMING_SNAKE_CASE :			int												=       torch.optim.Adam(secondary_learner.parameters() ,      lr=0.00_001		)
 secondary_learner.train()

 # TODO in original code this is written as number of actual batches seen
 # not number of items seen but other places it is number of items instead.
 # improve consistency! changed this to epochs for clarity
 __SCREAMING_SNAKE_CASE :			List[Any]												=       float('''inf'''		)
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(lowercase_		)		):
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       0.0
  secondary_learner.train()
  for step, batch in enumerate(lowercase_		):
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       batch[0].to(lowercase_		)
   __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       batch[1].to(lowercase_		)
   __SCREAMING_SNAKE_CASE :			List[Any]												=       secondary_learner(lowercase_		)
   q_optimizer.zero_grad()
   __SCREAMING_SNAKE_CASE :			Tuple												=       loss(lowercase_ ,      real_q.float()		)
   q_loss.backward()
   q_optimizer.step()
   tr_q_loss += q_loss.item()

   # model trains fairly quickly so we won't wait for a full epoch
   # eval is triggered at eval_freq and end of epochs
   if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowercase_		)):
    __SCREAMING_SNAKE_CASE :			List[Any]												=       tr_q_loss / (step + 1)

    secondary_learner.eval()
    __SCREAMING_SNAKE_CASE :			Optional[Any]												=       0.0
    __SCREAMING_SNAKE_CASE :			Tuple												=       0.0
    __SCREAMING_SNAKE_CASE :			Optional[int]												=       []
    __SCREAMING_SNAKE_CASE :			Any												=       []
    # Compute performance of the secondary learner after this batch
    for stepa, batcha in enumerate(lowercase_		):
     __SCREAMING_SNAKE_CASE :			str												=       batcha[0].to(lowercase_		)
     __SCREAMING_SNAKE_CASE :			int												=       batcha[1].to(lowercase_		)
     __SCREAMING_SNAKE_CASE :			int												=       secondary_learner(lowercase_		)
     q_lossa += test_loss(lowercase_ ,      lowercase_		).item()
     sum_qa += torch.sum(lowercase_		).item()
     for ei, i in enumerate(predicted_qa.cpu().detach().numpy()		):
      predicted.append(i.item()		)
     for ei, i in enumerate(real_qa.cpu().detach().numpy()		):
      actual.append(i.item()		)

    q_lossa /= len(lowercase_		)
    print(
        '''Epoch: ''' ,      lowercase_ ,      '''step: ''' ,      lowercase_ ,      '''Avg. q:''' ,      sum_qa / len(lowercase_		) ,      '''Train Loss: ''' ,      lowercase_ ,      '''Test Loss: ''' ,      lowercase_ ,      )
    if q_lossa < best_test_loss:
     joblib.dump((predicted, actual) ,      '''pred_vs_actual.jbl'''		)
     torch.save(secondary_learner.state_dict() ,      lowercase_		)
     __SCREAMING_SNAKE_CASE :			Any												=       q_lossa

   secondary_learner.train()
 return secondary_learner






class 			snake_case		(   nn.Module   ):

      def __init__(			self   :List[str]						,							_lowerCamelCase   :Optional[Any]						):
       super(_lowerCamelCase						,							self						).__init__()
       __SCREAMING_SNAKE_CASE :			int												=       model.transformer.wte
       __SCREAMING_SNAKE_CASE :			Dict												=       copy.deepcopy(model.transformer.wte.weight						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       nn.Convad(self.embeddings.weight.size(1						)						,							2_5_6						,							3						,							padding=1						)
       __SCREAMING_SNAKE_CASE :			int												=       nn.Sequential(nn.Linear(2_5_6						,							3_2						)						,							nn.Dropout(p=0.1						)						,							nn.Linear(3_2						,							3_2						)						,							nn.Linear(3_2						,							1						)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :str						):
       __SCREAMING_SNAKE_CASE :			Any												=       torch.max(self.conv(self.embeddings(_lowerCamelCase						).squeeze(1						).transpose(1						,							2						)						)						,							2						)[0]
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.fc(_lowerCamelCase						)
       return qs.squeeze(1						)



      @classmethod
      def 						SCREAMING_SNAKE_CASE_			(			cls   :List[str]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :str						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       cls(_lowerCamelCase						)  # this calls __init__
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       torch.load(_lowerCamelCase						)
       secondary_learner.load_state_dict(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Dict												=       model.transformer.wte
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       copy.deepcopy(model.transformer.wte.weight						)
       return secondary_learner








"""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)

