

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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase           =					logging.get_logger()







@dataclass
class 						__magic_name__			:

   '''simple docstring'''




   lowerCamelCase__ :	nn.Module
   lowerCamelCase__ :	List[nn.Module]    =   field(default_factory=lowerCamelCase__							)
   lowerCamelCase__ :	list    =   field(default_factory=lowerCamelCase__							)



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =len(list(m.modules()       )       ) == 1 or isinstance(SCREAMING_SNAKE_CASE__,    nn.Convad       ) or isinstance(SCREAMING_SNAKE_CASE__,    nn.BatchNormad       )
     if has_not_submodules:
       self.traced.append(SCREAMING_SNAKE_CASE__       )



   def __call__(				self,    lowercase_       )							->    Optional[int]:






     """simple docstring"""






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



   @property
   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






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







@dataclass
class 						__magic_name__			:

   '''simple docstring'''




   lowerCamelCase__ :	nn.Module
   lowerCamelCase__ :	nn.Module
   lowerCamelCase__ :	int    =   0
   lowerCamelCase__ :	List    =   field(default_factory=lowerCamelCase__							)
   lowerCamelCase__ :	List    =   field(default_factory=lowerCamelCase__							)



   def __call__(				self,    lowercase_       )							->    Optional[Any]:






     """simple docstring"""






     a__  =Tracker(self.dest       )(SCREAMING_SNAKE_CASE__       ).parametrized
     a__  =Tracker(self.src       )(SCREAMING_SNAKE_CASE__       ).parametrized

     a__  =list(filter(lambda lowercase_       : type(SCREAMING_SNAKE_CASE__       ) not in self.src_skip,    SCREAMING_SNAKE_CASE__       )       )
     a__  =list(filter(lambda lowercase_       : type(SCREAMING_SNAKE_CASE__       ) not in self.dest_skip,    SCREAMING_SNAKE_CASE__       )       )

     if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
       raise Exception(
           F"""Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE__       )} operations while"""
           F""" destination module has {len(SCREAMING_SNAKE_CASE__       )}."""       )

     for dest_m, src_m in zip(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       ):
       dest_m.load_state_dict(src_m.state_dict()       )
       if self.verbose == 1:
         print(F"""Transfered from={src_m} to={dest_m}"""       )





def 			UpperCAmelCase__				(			_A      : str	,      _A      : ResNetConfig	,      _A      : Path	,      _A      : bool = True							):





  '''simple docstring'''







  print(F"""Converting {name}..."""							)
  with torch.no_grad():
    a__  =timm.create_model(a_	,      pretrained=a_							).eval()
    a__  =ResNetForImageClassification(a_							).eval()
    a__  =ModuleTransfer(src=a_	,      dest=a_							)
    a__  =torch.randn((1, 3, 2_24, 2_24)							)
    module_transfer(a_							)

  assert torch.allclose(from_model(a_							)	,      our_model(a_							).logits							), "The model logits don't match the original one."

  a__  =F"""resnet{"-".join(name.split("resnet"							)							)}"""
  print(a_							)

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

    # we can use the convnext one
    a__  =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=a_	,      )

    print(F"""Pushed {checkpoint_name}"""							)




def 			UpperCAmelCase__				(			_A      : Path	,      _A      : str = None	,      _A      : bool = True							):





  '''simple docstring'''







  a__  ='''imagenet-1k-id2label.json'''
  a__  =10_00
  a__  =(1, num_labels)

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

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

  a__  =partial(a_	,      num_labels=a_	,      idalabel=a_	,      labelaid=a_							)

  a__  ={
      '''resnet18''': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]	,      hidden_sizes=[64, 1_28, 2_56, 5_12]	,      layer_type='''basic'''							),
      '''resnet26''': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]	,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,      layer_type='''bottleneck'''							),
      '''resnet34''': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]	,      hidden_sizes=[64, 1_28, 2_56, 5_12]	,      layer_type='''basic'''							),
      '''resnet50''': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]	,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,      layer_type='''bottleneck'''							),
      '''resnet101''': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]	,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,      layer_type='''bottleneck'''							),
      '''resnet152''': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]	,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,      layer_type='''bottleneck'''							),
  }

  if model_name:
    convert_weight_and_push(a_	,      names_to_config[model_name]	,      a_	,      a_							)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(a_	,      a_	,      a_	,      a_							)
  return config, expected_shape


if __name__ == "__main__":
       lowerCamelCase           =					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: resnet18,26,34,50,101,152. 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(
           '''--push_to_hub''',
           default=True,
           type=bool,
           required=False,
           help='''If True, push model and image processor to the hub.''',
       )

       lowerCamelCase           =					parser.parse_args()
       lowerCamelCase           =					args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_								=		logging.getLogger(__name__)



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  argparse.ArgumentParser(
          description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''							)
      parser.add_argument('''--file_path''' ,   type=a_ ,   default='''data/dump.txt''' ,   help='''The path to the data.'''							)
      parser.add_argument('''--tokenizer_type''' ,   type=a_ ,   default='''bert''' ,   choices=['''bert''', '''roberta''', '''gpt2''']							)
      parser.add_argument('''--tokenizer_name''' ,   type=a_ ,   default='''bert-base-uncased''' ,   help='''The tokenizer to use.'''							)
      parser.add_argument('''--dump_file''' ,   type=a_ ,   default='''data/dump''' ,   help='''The dump file prefix.'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  parser.parse_args()

      logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''							)
      if args.tokenizer_type == "bert":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            __SCREAMING_SNAKE_CASE   :str						=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''cls_token''']  # `<s>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''sep_token''']  # `</s>`
      elif args.tokenizer_type == "gpt2":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

      logger.info(f'''Loading text from {args.file_path}'''							)
      with open(args.file_path ,   '''r''' ,   encoding='''utf8'''							) as fp:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  fp.readlines()

      logger.info('''Start encoding'''							)
      logger.info(f'''{len(a_							)} examples to process.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :List[str]						=  0
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  1_00_00
      __SCREAMING_SNAKE_CASE   :List[Any]						=  time.time()
      for text in data:
            __SCREAMING_SNAKE_CASE   :Any						=  f'''{bos} {text.strip()} {sep}'''
            __SCREAMING_SNAKE_CASE   :int						=  tokenizer.encode(a_ ,   add_special_tokens=a_							)
            rslt.append(a_							)

            iter += 1
            if iter % interval == 0:
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
                  logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''							)
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
      logger.info('''Finished binarization'''							)
      logger.info(f'''{len(a_							)} examples processed.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
      __SCREAMING_SNAKE_CASE   :str						=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [np.uintaa(a_							) for d in rslt]
      else:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  [np.intaa(a_							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f'''Dump to {dp_file}'''							)
      with open(a_ ,   '''wb'''							) as handle:
            pickle.dump(rslt_ ,   a_ ,   protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
  main()