



'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def lowerCAmelCase    (__A):



     """simple docstring"""



     if "img_encoder.pos_embed" in name:
          _a           =    name.replace('''img_encoder.pos_embed'''    ,      '''vision_model.embeddings.position_embeddings''')
     if "img_encoder.patch_embed.proj" in name:
          _a           =    name.replace('''img_encoder.patch_embed.proj'''    ,      '''vision_model.embeddings.patch_embeddings.projection''')
     if "img_encoder.patch_embed.norm" in name:
          _a           =    name.replace('''img_encoder.patch_embed.norm'''    ,      '''vision_model.embeddings.layernorm''')
     if "img_encoder.layers" in name:
          _a           =    name.replace('''img_encoder.layers'''    ,      '''vision_model.encoder.stages''')
     if "blocks" in name and "res" not in name:
          _a           =    name.replace('''blocks'''    ,      '''layers''')
     if "attn" in name and "pre_assign" not in name:
          _a           =    name.replace('''attn'''    ,      '''self_attn''')
     if "proj" in name and "self_attn" in name and "text" not in name:
          _a           =    name.replace('''proj'''    ,      '''out_proj''')
     if "pre_assign_attn.attn.proj" in name:
          _a           =    name.replace('''pre_assign_attn.attn.proj'''    ,      '''pre_assign_attn.attn.out_proj''')
     if "norm1" in name:
          _a           =    name.replace('''norm1'''    ,      '''layer_norm1''')
     if "norm2" in name and "pre_assign" not in name:
          _a           =    name.replace('''norm2'''    ,      '''layer_norm2''')
     if "img_encoder.norm" in name:
          _a           =    name.replace('''img_encoder.norm'''    ,      '''vision_model.layernorm''')
     # text encoder
     if "text_encoder.token_embedding" in name:
          _a           =    name.replace('''text_encoder.token_embedding'''    ,      '''text_model.embeddings.token_embedding''')
     if "text_encoder.positional_embedding" in name:
          _a           =    name.replace('''text_encoder.positional_embedding'''    ,      '''text_model.embeddings.position_embedding.weight''')
     if "text_encoder.transformer.resblocks." in name:
          _a           =    name.replace('''text_encoder.transformer.resblocks.'''    ,      '''text_model.encoder.layers.''')
     if "ln_1" in name:
          _a           =    name.replace('''ln_1'''    ,      '''layer_norm1''')
     if "ln_2" in name:
          _a           =    name.replace('''ln_2'''    ,      '''layer_norm2''')
     if "c_fc" in name:
          _a           =    name.replace('''c_fc'''    ,      '''fc1''')
     if "c_proj" in name:
          _a           =    name.replace('''c_proj'''    ,      '''fc2''')
     if "text_encoder" in name:
          _a           =    name.replace('''text_encoder'''    ,      '''text_model''')
     if "ln_final" in name:
          _a           =    name.replace('''ln_final'''    ,      '''final_layer_norm''')
     # projection layers
     if "img_projector.linear_hidden." in name:
          _a           =    name.replace('''img_projector.linear_hidden.'''    ,      '''visual_projection.''')
     if "img_projector.linear_out." in name:
          _a           =    name.replace('''img_projector.linear_out.'''    ,      '''visual_projection.3.''')
     if "text_projector.linear_hidden" in name:
          _a           =    name.replace('''text_projector.linear_hidden'''    ,      '''text_projection''')
     if "text_projector.linear_out" in name:
          _a           =    name.replace('''text_projector.linear_out'''    ,      '''text_projection.3''')

     return name




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     for key in orig_state_dict.copy().keys():
          _a           =    orig_state_dict.pop(__A)

          if "qkv" in key:
               # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _a           =    key.split('''.''')
               _a    ,       _a           =    int(key_split[2]), int(key_split[4])
               _a           =    config.vision_config.hidden_size
               if "weight" in key:
                    _a           =    val[:dim, :]
                    _a           =    val[dim : dim * 2, :]
                    _a           =    val[-dim:, :]
               else:
                    _a           =    val[:dim]
                    _a           =    val[dim : dim * 2]
                    _a           =    val[-dim:]
          elif "in_proj" in key:
               # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _a           =    key.split('''.''')
               _a           =    int(key_split[3])
               _a           =    config.text_config.hidden_size
               if "weight" in key:
                    _a           =    val[:dim, :]
                    _a           =    val[
                        dim : dim * 2, :
                    ]
                    _a           =    val[-dim:, :]
               else:
                    _a           =    val[:dim]
                    _a           =    val[dim : dim * 2]
                    _a           =    val[-dim:]
          else:
               _a           =    rename_key(__A)
               # squeeze if necessary
               if (
                   "text_projection.0" in new_name
                   or "text_projection.3" in new_name
                   or "visual_projection.0" in new_name
                   or "visual_projection.3" in new_name
               ):
                    _a           =    val.squeeze_()
               else:
                    _a           =    val

     return orig_state_dict




def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a           =    Image.open(requests.get(__A    ,      stream=__A).raw)
     return im




@torch.no_grad()
def lowerCAmelCase    (__A    ,      __A    ,      __A="groupvit-gcc-yfcc"    ,      __A=False):



     """simple docstring"""



     _a           =    GroupViTConfig()
     _a           =    GroupViTModel(__A).eval()

     _a           =    torch.load(__A    ,      map_location='''cpu''')['''model''']
     _a           =    convert_state_dict(__A    ,      __A)
     _a    ,       _a           =    model.load_state_dict(__A    ,      strict=__A)
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__A) == 0)

     # verify result
     _a           =    CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
     _a           =    prepare_img()
     _a           =    processor(text=['''a photo of a cat''', '''a photo of a dog''']    ,      images=__A    ,      padding=__A    ,      return_tensors='''pt''')

     with torch.no_grad():
          _a           =    model(**__A)

     if model_name == "groupvit-gcc-yfcc":
          _a           =    torch.tensor([[13.35_23, 6.36_29]])
     elif model_name == "groupvit-gcc-redcaps":
          _a           =    torch.tensor([[16.18_73, 8.62_30]])
     else:
          raise ValueError(F'''Model name {model_name} not supported.''')
     assert torch.allclose(outputs.logits_per_image    ,      __A    ,      atol=1e-3)

     processor.save_pretrained(__A)
     model.save_pretrained(__A)
     print('''Successfully saved processor and model to'''    ,      __A)

     if push_to_hub:
          print('''Pushing to the hub...''')
          processor.push_to_hub(__A    ,      organization='''nielsr''')
          model.push_to_hub(__A    ,      organization='''nielsr''')


if __name__ == "__main__":
  lowercase_									=    argparse.ArgumentParser()
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
  )
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
  parser.add_argument(
      "--model_name",
      default="groupvit-gccy-fcc",
      type=str,
      help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
      help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
  )
  lowercase_									=    parser.parse_args()

  convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''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


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




def lowerCAmelCase    (__A):



     """simple docstring"""



     if len(__A) < MIN_NUM_TOKENS:
          return None
     _a           =    MinHash(num_perm=__A)
     for token in set(__A):
          min_hash.update(token.encode())
     return min_hash




def lowerCAmelCase    (__A):



     """simple docstring"""



     return {t for t in NON_ALPHA.split(__A) if len(t.strip()) > 0}






class __A     :



 '''simple docstring'''







 def __init__(self					,  *,
     A = 0.85					,  )       ->	Optional[int]:


      """simple docstring"""



      _a           =    duplication_jaccard_threshold
      _a           =    NUM_PERM
      _a           =    MinHashLSH(threshold=self._duplication_jaccard_threshold					,  num_perm=self._num_perm			)

      _a           =    defaultdict(A			)



 def    a__    (self					,  A					,  A			)       ->	None:


      """simple docstring"""



      _a           =    self._index.query(A			)
      if code_key in self._index.keys:
           print(f'''Duplicate key {code_key}'''			)
           return

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



 def    a__    (self			)       ->	List[List[Dict]]:


      """simple docstring"""



      _a           =    []
      for base, duplicates in self._duplicate_clusters.items():
           _a           =    [base] + list(A			)
           # reformat the cluster to be a list of dict
           _a           =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(A			)
      return duplicate_clusters



 def    a__    (self					,  A			)       ->	None:


      """simple docstring"""



      _a           =    self.get_duplicate_clusters()
      with open(A					,  '''w'''			) as f:
           json.dump(A					,  A			)





def lowerCAmelCase    (__A):



     """simple docstring"""



     _a    ,       _a           =    element
     _a           =    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    (__A):



     """simple docstring"""



     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash    ,      ThreadedIterator(__A    ,      max_queue_size=10_000)    ,      chunksize=100    ,      ):
               if data is not None:
                    yield data




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    DuplicationIndex(duplication_jaccard_threshold=__A)

     for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__A))    ,      max_queue_size=100)):
          di.add(__A    ,      __A)

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




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    get_tokens(__A)
     _a           =    get_tokens(__A)
     return len(tokensa & tokensa) / len(tokensa | tokensa)


lowercase_									=    None




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    []
     for elementa in cluster:
          _a           =    _shared_dataset[elementa['''base_index''']]['''content''']
          for elementa in extremes:
               _a           =    _shared_dataset[elementa['''base_index''']]['''content''']
               if jaccard_similarity(__A    ,      __A) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               _a           =    1
               extremes.append(__A)
     return extremes




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     global _shared_dataset
     _a           =    dataset
     _a           =    []
     _a           =    partial(_find_cluster_extremes_shared    ,      jaccard_threshold=__A)
     with mp.Pool() as pool:
          for extremes in tqdm(
              pool.imap_unordered(
                  __A    ,      __A    ,      )    ,      total=len(__A)    ,      ):
               extremes_list.append(__A)
     return extremes_list




def lowerCAmelCase    (__A    ,      __A = 0.85):



     """simple docstring"""



     _a           =    make_duplicate_clusters(__A    ,      __A)
     _a           =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     _a           =    {}
     _a           =    find_extremes(__A    ,      __A    ,      __A)
     for extremes in extremes_clusters:
          for element in extremes:
               _a           =    element
     _a           =    duplicate_indices - set(extreme_dict.keys())
     _a           =    dataset.filter(lambda __A    ,      __A: idx not in remove_indices    ,      with_indices=__A)

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

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

     return ds_filter, duplicate_clusters



