





'''simple docstring'''




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_SCREAMING_SNAKE_CASE							     =			re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_SCREAMING_SNAKE_CASE							     =			10
_SCREAMING_SNAKE_CASE							     =			256





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  Optional[MinHash]:
   if len(__lowerCAmelCase		) < MIN_NUM_TOKENS:
      return None
   snake_case 						=   MinHash(num_perm=__lowerCAmelCase		)
   for token in set(__lowerCAmelCase		):
      min_hash.update(token.encode()		)
   return min_hash





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  Set[str]:
   return {t for t in NON_ALPHA.split(__lowerCAmelCase		) if len(t.strip()		) > 0}
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			Tuple   ,       *,
         __snake_case							:			float = 0.85   ,       )->	Tuple:
        snake_case 						=   duplication_jaccard_threshold
        snake_case 						=   NUM_PERM
        snake_case 						=   MinHashLSH(threshold=self._duplication_jaccard_threshold   ,       num_perm=self._num_perm						)

        snake_case 						=   defaultdict(__snake_case						)



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			Tuple   ,       __snake_case							:			MinHash						)->	None:
        snake_case 						=   self._index.query(__snake_case						)
        if code_key in self._index.keys:
           print(f'''Duplicate key {code_key}'''						)
           return

        self._index.insert(__snake_case   ,       __snake_case						)
        if len(__snake_case						) > 0:
           for base_duplicate in close_duplicates:
              if base_duplicate in self._duplicate_clusters:
                 self._duplicate_clusters[base_duplicate].add(__snake_case						)
                 break
           else:
              self._duplicate_clusters[close_duplicates[0]].add(__snake_case						)



     def      lowerCAmelCase			(   self							:			int						)->	List[List[Dict]]:
        snake_case 						=   []
        for base, duplicates in self._duplicate_clusters.items():
           snake_case 						=   [base] + list(__snake_case						)
           # reformat the cluster to be a list of dict
           snake_case 						=   [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
           duplicate_clusters.append(__snake_case						)
        return duplicate_clusters



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			Dict						)->	None:
        snake_case 						=   self.get_duplicate_clusters()
        with open(__snake_case   ,       """w"""						) as f:
           json.dump(__snake_case   ,       __snake_case						)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  int:
   snake_case						,    snake_case 						=   element
   snake_case 						=   get_min_hash([t for t in NON_ALPHA.split(data["""content"""]		) if len(t.strip()		) > 0]		)
   if min_hash is not None:
      return (index, data["repo_name"], data["path"]), min_hash





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Type[Dataset]		)		->  Optional[int]:
   with mp.Pool() as pool:
      for data in pool.imap_unordered(
          _compute_min_hash      ,    ThreadedIterator(__lowerCAmelCase      ,    max_queue_size=1_00_00		)      ,    chunksize=1_00      ,    ):
         if data is not None:
            yield data





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Type[Dataset]      ,    __lowerCAmelCase       :							float		)		->  List[Any]:
   snake_case 						=   DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase		)

   for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase		)		)      ,    max_queue_size=1_00		)		):
      di.add(__lowerCAmelCase      ,    __lowerCAmelCase		)

   # Returns a List[Cluster] where Cluster is List[str] with the filenames.
   return di.get_duplicate_clusters()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str		)		->  float:
   snake_case 						=   get_tokens(__lowerCAmelCase		)
   snake_case 						=   get_tokens(__lowerCAmelCase		)
   return len(tokensa & tokensa		) / len(tokensa | tokensa		)


_SCREAMING_SNAKE_CASE							     =			None





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Tuple		)		->  int:
   snake_case 						=   []
   for elementa in cluster:
      snake_case 						=   _shared_dataset[elementa["""base_index"""]]["""content"""]
      for elementa in extremes:
         snake_case 						=   _shared_dataset[elementa["""base_index"""]]["""content"""]
         if jaccard_similarity(__lowerCAmelCase      ,    __lowerCAmelCase		) >= jaccard_threshold:
            elementa["copies"] += 1
            break
      else:
         snake_case 						=   1
         extremes.append(__lowerCAmelCase		)
   return extremes





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							List[str]		)		->  List[Any]:
   global _shared_dataset
   snake_case 						=   dataset
   snake_case 						=   []
   snake_case 						=   partial(_find_cluster_extremes_shared      ,    jaccard_threshold=__lowerCAmelCase		)
   with mp.Pool() as pool:
      for extremes in tqdm(
          pool.imap_unordered(
              __lowerCAmelCase      ,    __lowerCAmelCase      ,    )      ,    total=len(__lowerCAmelCase		)      ,    ):
         extremes_list.append(__lowerCAmelCase		)
   return extremes_list





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Type[Dataset]      ,    __lowerCAmelCase       :							float = 0.85		)		->  Tuple[Type[Dataset], List[List[Dict]]]:
   snake_case 						=   make_duplicate_clusters(__lowerCAmelCase      ,    __lowerCAmelCase		)
   snake_case 						=   {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
   snake_case 						=   {}
   snake_case 						=   find_extremes(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   for extremes in extremes_clusters:
      for element in extremes:
         snake_case 						=   element
   snake_case 						=   duplicate_indices - set(extreme_dict.keys()		)
   snake_case 						=   dataset.filter(lambda __lowerCAmelCase      ,    __lowerCAmelCase		: idx not in remove_indices      ,    with_indices=__lowerCAmelCase		)

   # update duplicate_clusters
   for cluster in duplicate_clusters:
      for element in cluster:
         snake_case 						=   element["""base_index"""] in extreme_dict
         if element["is_extreme"]:
            snake_case 						=   extreme_dict[element["""base_index"""]]["""copies"""]

   print(F'''Original dataset size: {len(__lowerCAmelCase		)}'''		)
   print(F'''Number of duplicate clusters: {len(__lowerCAmelCase		)}'''		)
   print(F'''Files in duplicate cluster: {len(__lowerCAmelCase		)}'''		)
   print(F'''Unique files in duplicate cluster: {len(__lowerCAmelCase		)}'''		)
   print(F'''Filtered dataset size: {len(__lowerCAmelCase		)}'''		)

   return ds_filter, duplicate_clusters








'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							LevitConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True		)		->  int:
   print(F'''Converting {name}...'''		)

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            snake_case 						=   timm.create_model("""levit_128s"""      ,    pretrained=__lowerCAmelCase		)
         else:
            snake_case 						=   timm.create_model("""levit_128"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 1_92:
         snake_case 						=   timm.create_model("""levit_192"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 2_56:
         snake_case 						=   timm.create_model("""levit_256"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 3_84:
         snake_case 						=   timm.create_model("""levit_384"""      ,    pretrained=__lowerCAmelCase		)

      from_model.eval()
      snake_case 						=   LevitForImageClassificationWithTeacher(__lowerCAmelCase		).eval()
      snake_case 						=   OrderedDict()

      snake_case 						=   from_model.state_dict()
      snake_case 						=   list(from_model.state_dict().keys()		)
      snake_case 						=   list(our_model.state_dict().keys()		)
      print(len(__lowerCAmelCase		)      ,    len(__lowerCAmelCase		)		)
      for i in range(len(__lowerCAmelCase		)		):
         snake_case 						=   weights[og_keys[i]]
      our_model.load_state_dict(__lowerCAmelCase		)

      snake_case 						=   torch.randn((2, 3, 2_24, 2_24)		)
      snake_case 						=   from_model(__lowerCAmelCase		)
      snake_case 						=   our_model(__lowerCAmelCase		).logits

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

   snake_case 						=   name
   print(__lowerCAmelCase		)

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name		)
      snake_case 						=   LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name		)

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  List[Any]:
   snake_case 						=   """imagenet-1k-id2label.json"""
   snake_case 						=   10_00
   snake_case 						=   (1, num_labels)

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

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

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

   snake_case 						=   {
       """levit-128S""": 1_28,
       """levit-128""": 1_28,
       """levit-192""": 1_92,
       """levit-256""": 2_56,
       """levit-384""": 3_84,
   }

   snake_case 						=   {
       """levit-128S""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[2, 3, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-128""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 8, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-192""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]      ,    num_attention_heads=[3, 5, 6]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-256""": ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-384""": ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]      ,    num_attention_heads=[6, 9, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0.1      ,    ),
   }

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


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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 Levit* architecture,",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="levit-dump-folder/",
     type=Path,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 _SCREAMING_SNAKE_CASE							     =			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)

