


'''simple docstring'''

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase:		Optional[Any]    						=  logging.get_logger(__name__)







@dataclass
class 			UpperCAmelCase			:
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       List[nn.Module]            =       field(default_factory=SCREAMING_SNAKE_CASE__)
 _lowerCamelCase			:       list            =       field(default_factory=SCREAMING_SNAKE_CASE__)

 def 		lowercase_    (			self			:						int,       a_			:						Dict,       a_			:						Tensor,       a_			:						Tensor       ):





    """simple docstring"""



    UpperCamelCase__			    =    len(list(m.modules()       )       ) == 1 or isinstance(a_,       nn.Convad       ) or isinstance(a_,       nn.BatchNormad       )
    if has_not_submodules:
       if not isinstance(a_,       a_       ):
          self.traced.append(a_       )

 def __call__(			self			:						Tuple,       a_			:						Tensor       ):





    """simple docstring"""



    for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook       )       )
    self.module(a_       )
    [x.remove() for x in self.handles]
    return self

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    return list(filter(lambda a_       : len(list(x.state_dict().keys()       )       ) > 0,       self.traced       )       )







@dataclass
class 			UpperCAmelCase			:
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       int            =       0
 _lowerCamelCase			:       List            =       field(default_factory=SCREAMING_SNAKE_CASE__)
 _lowerCamelCase			:       List            =       field(default_factory=SCREAMING_SNAKE_CASE__)

 def __call__(			self			:						List[str],       a_			:						Tensor       ):





    """simple docstring"""



    UpperCamelCase__			    =    Tracker(self.dest       )(a_       ).parametrized
    UpperCamelCase__			    =    Tracker(self.src       )(a_       ).parametrized

    UpperCamelCase__			    =    list(filter(lambda a_       : type(a_       ) not in self.src_skip,       a_       )       )
    UpperCamelCase__			    =    list(filter(lambda a_       : type(a_       ) not in self.dest_skip,       a_       )       )

    if len(a_       ) != len(a_       ):
       raise Exception(
           f'Numbers of operations are different. Source module has {len(a_       )} operations while'
           f' destination module has {len(a_       )}.'       )

    for dest_m, src_m in zip(a_,       a_       ):
       dest_m.load_state_dict(src_m.state_dict()       )
       if self.verbose == 1:
          print(f'Transfered from={src_m} to={dest_m}'       )







def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					nn.Module     ,		_UpperCamelCase   :					nn.Module )		->       nn.Module:





   '''simple docstring'''







   UpperCamelCase__			    =    from_model.state_dict()
   UpperCamelCase__			    =    our_model.state_dict()
   UpperCamelCase__			    =    our_model.config
   UpperCamelCase__			    =    []
   for stage_idx in range(len(config.hidden_sizes ) ):
      for block_id in range(config.depths[stage_idx] ):
         UpperCamelCase__			    =    F'block{stage_idx + 1}.{block_id}.layer_scale_1'
         UpperCamelCase__			    =    F'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

         all_keys.append((from_key, to_key) )
         UpperCamelCase__			    =    F'block{stage_idx + 1}.{block_id}.layer_scale_2'
         UpperCamelCase__			    =    F'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

         all_keys.append((from_key, to_key) )

   for from_key, to_key in all_keys:
      UpperCamelCase__			    =    from_state_dict.pop(_UpperCamelCase )

   our_model.load_state_dict(_UpperCamelCase )
   return our_model



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					VanConfig     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					nn.Module     ,		_UpperCamelCase   :					Path     ,		_UpperCamelCase   :					bool = True     ,		)		->       Tuple:





   '''simple docstring'''







   print(F'Downloading weights for {name}...' )
   UpperCamelCase__			    =    cached_download(_UpperCamelCase )
   print(F'Converting {name}...' )
   UpperCamelCase__			    =    torch.load(_UpperCamelCase )["state_dict"]
   from_model.load_state_dict(_UpperCamelCase )
   from_model.eval()
   with torch.no_grad():
      UpperCamelCase__			    =    VanForImageClassification(_UpperCamelCase ).eval()
      UpperCamelCase__			    =    ModuleTransfer(src=_UpperCamelCase     ,		dest=_UpperCamelCase )
      UpperCamelCase__			    =    torch.randn((1, 3, 2_24, 2_24) )
      module_transfer(_UpperCamelCase )
      UpperCamelCase__			    =    copy_parameters(_UpperCamelCase     ,		_UpperCamelCase )

   if not torch.allclose(from_model(_UpperCamelCase )     ,		our_model(_UpperCamelCase ).logits ):
      raise ValueError("The model logits don't match the original one." )

   UpperCamelCase__			    =    name
   print(_UpperCamelCase )

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name     ,		commit_message="Add model"     ,		use_temp_dir=_UpperCamelCase     ,		)

      # we can use the convnext one
      UpperCamelCase__			    =    AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name     ,		commit_message="Add image processor"     ,		use_temp_dir=_UpperCamelCase     ,		)

      print(F'Pushed {checkpoint_name}' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Path     ,		_UpperCamelCase   :					str = None     ,		_UpperCamelCase   :					bool = True )		->       Union[str, Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    "imagenet-1k-id2label.json"
   UpperCamelCase__			    =    10_00

   UpperCamelCase__			    =    "huggingface/label-files"
   UpperCamelCase__			    =    num_labels
   UpperCamelCase__			    =    json.load(open(hf_hub_download(_UpperCamelCase     ,		_UpperCamelCase     ,		repo_type="dataset" )     ,		"r" ) )
   UpperCamelCase__			    =    {int(_UpperCamelCase ): v for k, v in idalabel.items()}

   UpperCamelCase__			    =    idalabel
   UpperCamelCase__			    =    {v: k for k, v in idalabel.items()}

   UpperCamelCase__			    =    partial(_UpperCamelCase     ,		num_labels=_UpperCamelCase     ,		idalabel=_UpperCamelCase     ,		labelaid=_UpperCamelCase )

   UpperCamelCase__			    =    {
       "van-tiny": ImageNetPreTrainedConfig(
           hidden_sizes=[32, 64, 1_60, 2_56]     ,		depths=[3, 3, 5, 2]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-small": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[2, 2, 4, 2]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-base": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[3, 3, 12, 3]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-large": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[3, 5, 27, 3]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
   }

   UpperCamelCase__			    =    {
       "van-tiny": van_tiny,
       "van-small": van_small,
       "van-base": van_base,
       "van-large": van_large,
   }

   UpperCamelCase__			    =    {
       "van-tiny": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
       ),
       "van-small": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
       ),
       "van-base": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
       ),
       "van-large": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
       ),
   }

   if model_name:
      convert_weight_and_push(
          _UpperCamelCase     ,		names_to_config[model_name]     ,		checkpoint=names_to_original_checkpoints[model_name]     ,		from_model=names_to_original_models[model_name]()     ,		save_directory=_UpperCamelCase     ,		push_to_hub=_UpperCamelCase     ,		)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             _UpperCamelCase     ,		_UpperCamelCase     ,		checkpoint=names_to_original_checkpoints[model_name]     ,		from_model=names_to_original_models[model_name]()     ,		save_directory=_UpperCamelCase     ,		push_to_hub=_UpperCamelCase     ,		)


if __name__ == "__main__":
       __lowercase:		Optional[Any]    						=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model-name",
           default=None,
           type=str,
           help=(
               "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
               " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=Path,
           required=True,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument(
           "--van_dir",
           required=True,
           type=Path,
           help=(
               "A path to VAN's original implementation directory. You can download from here:"
               " https://github.com/Visual-Attention-Network/VAN-Classification"
           ),
       )
       parser.add_argument(
           "--push_to_hub",
           default=True,
           type=bool,
           required=False,
           help="If True, push model and image processor to the hub.",
       )

       __lowercase:		Tuple    						=  parser.parse_args()
       __lowercase:		Path    						=  args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       __lowercase:		Dict    						=  args.van_dir
       # append the path to the parents to maskformer dir
       sys.path.append(str(van_dir.parent))
       from van.models.van import van_base, van_large, van_small, van_tiny

       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
'''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 :				Any														= logging.getLogger(__name__)



def  _lowerCAmelCase				(				__a      )  ->      Tuple:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      Union[str, Any]:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a="gpt2"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :Tuple       =GPTaLMHeadModel.from_pretrained(__a			,      output_hidden_states=__a      )
     torch.save(model.state_dict()			,      model_name + """local.pt"""      )
     return model



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :Any       =copy.deepcopy(__a      )
     model.to(__a      )

     _UpperCamelCase  :List[Any]       =["""bias""", """LayerNorm.weight"""]
     _UpperCamelCase  :Dict       =[
         {
             """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},
     ]
     _UpperCamelCase  :List[str]       =AdamW(__a			,      lr=5e-5			,      eps=1e-8      )
     _UpperCamelCase  :List[Any]       =get_linear_schedule_with_warmup(__a			,      0			,      __a      )
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a      )  ->      Tuple:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =np.array(__a      ).mean()
     _UpperCamelCase  :Dict       =np.array(__a      ).std()
     _UpperCamelCase  :Tuple       =(real_perps - avg) / std
     _UpperCamelCase  :Optional[Any]       =list(zip(__a			,      __a			,      __a      )      )
     joblib.dump(__a			,      __a      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a="dev.jbl"			,      __a=recopy_gpta			,      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :List[Any]       =[]
     _UpperCamelCase  :List[str]       =[]
     _UpperCamelCase  :Dict       =[]

     # Initialize the transformer model
     _UpperCamelCase  :Any       =copy.deepcopy(__a      )
     orig_model.to(device="""cpu"""      )
     torch.cuda.empty_cache()

     # Compute perplexity of initial transformer model for comparison
     model.train()
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =recopy_model(__a			,      __a			,      __a      )

     for step in tqdm(range(__a      )      ):
          _UpperCamelCase  :List[Any]       =torch.zeros((1, context_len)			,      dtype=torch.long			,      device=__a      )
          _UpperCamelCase  :str       =random.choice(__a      )
          _UpperCamelCase  :List[Any]       =random.randint(0			,      len(story[0]      ) - context_len - 1      )
          _UpperCamelCase  :str       =story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          _UpperCamelCase  :Optional[Any]       =model(__a			,      labels=__a      )
          _UpperCamelCase  :Union[str, Any]       =outputs[0]
          _UpperCamelCase  :Optional[Any]       =compute_perplexity(__a			,      __a			,      __a      )
          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
          _UpperCamelCase  :str       =compute_perplexity(__a			,      __a			,      __a      )

          # Periodically save the stored (X, IG(X)) pairs
          if step % 10_00 == 0 and step > 1:
               intermittent_save(__a			,      __a			,      __a			,      __a      )

          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =recopy_model(__a			,      __a			,      __a      )

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

     intermittent_save(__a			,      __a			,      __a			,      __a      )



def  _lowerCAmelCase				(				__a			,      __a="data/tokenized_stories_train_wikitext103.jbl"			,      __a=1_00			,      __a=10_26			,      __a=True      )  ->      Optional[int]:
     '''simple docstring'''







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

     joblib.dump(__a			,      """objective_set.jbl"""      )
     print("""objective set saved"""      )
     return train_data, objective_set



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a=50			,      __a="secondary_learner.pt"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =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
     _UpperCamelCase  :Any       =train_dataset[:5_12]
     _UpperCamelCase  :List[str]       =train_dataset[5_12:]
     _UpperCamelCase  :int       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )
     _UpperCamelCase  :Tuple       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )

     # secondary learner model set up
     _UpperCamelCase  :Any       =nn.MSELoss()
     _UpperCamelCase  :List[Any]       =nn.MSELoss(reduction="""sum"""      )
     secondary_learner.to(__a      )
     _UpperCamelCase  :int       =torch.optim.Adam(secondary_learner.parameters()			,      lr=0.0_00_01      )
     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
     _UpperCamelCase  :Union[str, Any]       =float("""inf"""      )
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(__a      )      ):
          _UpperCamelCase  :int       =0.0
          secondary_learner.train()
          for step, batch in enumerate(__a      ):
               _UpperCamelCase  :List[Any]       =batch[0].to(__a      )
               _UpperCamelCase  :List[str]       =batch[1].to(__a      )
               _UpperCamelCase  :str       =secondary_learner(__a      )
               q_optimizer.zero_grad()
               _UpperCamelCase  :Tuple       =loss(__a			,      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(__a      )):
                    _UpperCamelCase  :List[Any]       =tr_q_loss / (step + 1)

                    secondary_learner.eval()
                    _UpperCamelCase  :str       =0.0
                    _UpperCamelCase  :List[Any]       =0.0
                    _UpperCamelCase  :Optional[int]       =[]
                    _UpperCamelCase  :str       =[]
                    # Compute performance of the secondary learner after this batch
                    for stepa, batcha in enumerate(__a      ):
                         _UpperCamelCase  :str       =batcha[0].to(__a      )
                         _UpperCamelCase  :List[Any]       =batcha[1].to(__a      )
                         _UpperCamelCase  :Tuple       =secondary_learner(__a      )
                         q_lossa += test_loss(__a			,      __a      ).item()
                         sum_qa += torch.sum(__a      ).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(__a      )
                    print(
                        """Epoch: """			,      __a			,      """step: """			,      __a			,      """Avg. q:"""			,      sum_qa / len(__a      )			,      """Train Loss: """			,      __a			,      """Test Loss: """			,      __a			,      )
                    if q_lossa < best_test_loss:
                         joblib.dump((predicted, actual)			,      """pred_vs_actual.jbl"""      )
                         torch.save(secondary_learner.state_dict()			,      __a      )
                         _UpperCamelCase  :List[str]       =q_lossa

               secondary_learner.train()
     return secondary_learner




class    lowerCamelCase__  (   nn.Module     ):



   def __init__(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        super(lowerCAmelCase__					,      self     ).__init__()
        _UpperCamelCase  :Optional[Any]       =model.transformer.wte
        _UpperCamelCase  :Any       =copy.deepcopy(model.transformer.wte.weight     )
        _UpperCamelCase  :int       =nn.Convad(self.embeddings.weight.size(1     )					,      256					,      3					,      padding=1     )
        _UpperCamelCase  :int       =nn.Sequential(nn.Linear(256					,      32     )					,      nn.Dropout(p=0.1     )					,      nn.Linear(32					,      32     )					,      nn.Linear(32					,      1     )     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =torch.max(self.conv(self.embeddings(lowerCAmelCase__     ).squeeze(1     ).transpose(1					,      2     )     )					,      2     )[0]
        _UpperCamelCase  :List[Any]       =self.fc(lowerCAmelCase__     )
        return qs.squeeze(1     )




   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =cls(lowerCAmelCase__     )  # this calls __init__
        _UpperCamelCase  :Optional[Any]       =torch.load(lowerCAmelCase__     )
        secondary_learner.load_state_dict(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =model.transformer.wte
        _UpperCamelCase  :Tuple       =copy.deepcopy(model.transformer.wte.weight     )
        return secondary_learner