


'''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


__A						=re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
__A						=10
__A						=2_56







def      _UpperCamelCase   (		UpperCamelCase__       ):



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







def      _UpperCamelCase   (		UpperCamelCase__       ):
			return {t for t in NON_ALPHA.split(_UpperCamelCase       ) if len(t.strip()       ) > 0}


class 		_snake_case       :




					def __init__(   self				,				*,
					    _lowerCamelCase = 0.85				,				):
								UpperCAmelCase__	:       Union[str, Any]   =  duplication_jaccard_threshold
								UpperCAmelCase__	:       Optional[Any]   =  NUM_PERM
								UpperCAmelCase__	:       int   =  MinHashLSH(threshold=self._duplication_jaccard_threshold				,				num_perm=self._num_perm)

								UpperCAmelCase__	:       Any   =  defaultdict(A_)




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase):
								UpperCAmelCase__	:       List[Any]   =  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 					snake_case__    (   self):
								UpperCAmelCase__	:       Any   =  []
								for base, duplicates in self._duplicate_clusters.items():
											UpperCAmelCase__	:       str   =  [base] + list(A_)
											# reformat the cluster to be a list of dict
											UpperCAmelCase__	:       Tuple   =  [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
											duplicate_clusters.append(A_)
								return duplicate_clusters




					def 					snake_case__    (   self				,				_lowerCamelCase):
								UpperCAmelCase__	:       int   =  self.get_duplicate_clusters()
								with open(A_				,				"""w""") as f:
											json.dump(A_				,				A_)







def      _UpperCamelCase   (		UpperCamelCase__       ):
			UpperCAmelCase__	:       int   =  element
			UpperCAmelCase__	:       List[str]   =  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      _UpperCamelCase   (		UpperCamelCase__       ):



			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash			,   ThreadedIterator(_UpperCamelCase			,   max_queue_size=1_0_0_0_0       )			,   chunksize=1_0_0			,   ):
									if data is not None:
												yield data







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       Tuple   =  DuplicationIndex(duplication_jaccard_threshold=_UpperCamelCase       )

			for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_UpperCamelCase       )       )			,   max_queue_size=1_0_0       )       ):
						di.add(_UpperCamelCase			,   _UpperCamelCase       )

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







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       Tuple   =  get_tokens(_UpperCamelCase       )
			UpperCAmelCase__	:       Optional[Any]   =  get_tokens(_UpperCamelCase       )
			return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


__A						=None







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       int   =  []
			for elementa in cluster:
						UpperCAmelCase__	:       int   =  _shared_dataset[elementa['''base_index''']]['''content''']
						for elementa in extremes:
									UpperCAmelCase__	:       Tuple   =  _shared_dataset[elementa['''base_index''']]['''content''']
									if jaccard_similarity(_UpperCamelCase			,   _UpperCamelCase       ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									UpperCAmelCase__	:       Optional[Any]   =  1
									extremes.append(_UpperCamelCase       )
			return extremes







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__       ):
			global _shared_dataset
			UpperCAmelCase__	:       int   =  dataset
			UpperCAmelCase__	:       int   =  []
			UpperCAmelCase__	:       Optional[Any]   =  partial(_find_cluster_extremes_shared			,   jaccard_threshold=_UpperCamelCase       )
			with mp.Pool() as pool:
						for extremes in tqdm(
						    pool.imap_unordered(
						        _UpperCamelCase			,   _UpperCamelCase			,   )			,   total=len(_UpperCamelCase       )			,   ):
									extremes_list.append(_UpperCamelCase       )
			return extremes_list







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__ = 0.85       ):
			UpperCAmelCase__	:       Union[str, Any]   =  make_duplicate_clusters(_UpperCamelCase			,   _UpperCamelCase       )
			UpperCAmelCase__	:       Optional[int]   =  {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
			UpperCAmelCase__	:       Tuple   =  {}
			UpperCAmelCase__	:       Tuple   =  find_extremes(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase       )
			for extremes in extremes_clusters:
						for element in extremes:
									UpperCAmelCase__	:       Optional[int]   =  element
			UpperCAmelCase__	:       Any   =  duplicate_indices - set(extreme_dict.keys()       )
			UpperCAmelCase__	:       List[Any]   =  dataset.filter(lambda UpperCamelCase__			,   UpperCamelCase__       : idx not in remove_indices			,   with_indices=_UpperCamelCase       )

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

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

			return ds_filter, duplicate_clusters

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__ :       int				= logging.get_logger(__name__)




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase=False			,					_UpperCamelCase=False			,					_UpperCamelCase=False    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  []
  for i in range(config.num_hidden_layers    ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''')    )
    rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''')    )
    rename_keys.append(
        (f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''')    )
    rename_keys.append(
        (f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''')    )
    rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''')    )
    rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''')    )
    rename_keys.append(
        (f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''')    )
    rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''')    )
    rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''')    )
    rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''')    )

  # embeddings
  rename_keys.extend(
      [
          # text embeddings
          ('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
          (
              '''text_embeddings.position_embeddings.weight''',
              '''vilt.embeddings.text_embeddings.position_embeddings.weight''',
          ),
          ('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
          (
              '''text_embeddings.token_type_embeddings.weight''',
              '''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
          ),
          ('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
          ('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
          # patch embeddings
          ('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
          ('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
          ('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
          ('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
          # token type embeddings
          ('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
      ]    )

  # final layernorm + pooler
  rename_keys.extend(
      [
          ('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
          ('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
          ('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
          ('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
      ]    )

  # classifier head(s)
  if vqa_model:
    # classification head
    rename_keys.extend(
        [
            ('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
            ('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
            ('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
            ('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
            ('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
            ('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
        ]    )
  elif nlvr_model:
    # classification head
    rename_keys.extend(
        [
            ('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
            ('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
            ('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
            ('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
            ('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
            ('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
        ]    )
  else:
    pass

  return rename_keys




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  for i in range(config.num_hidden_layers    ):
    _lowercase:      List[str]   			=  '''vilt.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _lowercase:      Optional[int]   			=  state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight'''    )
    _lowercase:      int   			=  state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    _lowercase:      List[str]   			=  in_proj_weight[
        : config.hidden_size, :
    ]
    _lowercase:      List[Any]   			=  in_proj_bias[: config.hidden_size]
    _lowercase:      Dict   			=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _lowercase:      List[Any]   			=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _lowercase:      Optional[Any]   			=  in_proj_weight[
        -config.hidden_size :, :
    ]
    _lowercase:      Optional[Any]   			=  in_proj_bias[-config.hidden_size :]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(_UpperCamelCase			,					_UpperCamelCase    )




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  dct.pop(_UpperCamelCase    )
  _lowercase:      Dict   			=  val




@torch.no_grad()
def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[Any]   			=  ViltConfig(image_size=384			,					patch_size=32			,					tie_word_embeddings=_UpperCamelCase    )
  _lowercase:      Optional[Any]   			=  False
  _lowercase:      Optional[Any]   			=  False
  _lowercase:      Union[str, Any]   			=  False
  _lowercase:      Union[str, Any]   			=  False
  if "vqa" in checkpoint_url:
    _lowercase:      Union[str, Any]   			=  True
    _lowercase:      str   			=  3_129
    _lowercase:      List[str]   			=  '''huggingface/label-files'''
    _lowercase:      Tuple   			=  '''vqa2-id2label.json'''
    _lowercase:      Dict   			=  json.load(open(hf_hub_download(_UpperCamelCase			,					_UpperCamelCase			,					repo_type='''dataset'''    )			,					'''r'''    )    )
    _lowercase:      str   			=  {int(_UpperCamelCase    ): v for k, v in idalabel.items()}
    _lowercase:      Tuple   			=  idalabel
    _lowercase:      Tuple   			=  {v: k for k, v in idalabel.items()}
    _lowercase:      Any   			=  ViltForQuestionAnswering(_UpperCamelCase    )
  elif "nlvr" in checkpoint_url:
    _lowercase:      Any   			=  True
    _lowercase:      List[str]   			=  2
    _lowercase:      Optional[int]   			=  {0: '''False''', 1: '''True'''}
    _lowercase:      List[Any]   			=  {v: k for k, v in config.idalabel.items()}
    _lowercase:      Optional[Any]   			=  3
    _lowercase:      List[Any]   			=  ViltForImagesAndTextClassification(_UpperCamelCase    )
  elif "irtr" in checkpoint_url:
    _lowercase:      List[str]   			=  True
    _lowercase:      Optional[int]   			=  ViltForImageAndTextRetrieval(_UpperCamelCase    )
  elif "mlm_itm" in checkpoint_url:
    _lowercase:      Dict   			=  True
    _lowercase:      int   			=  ViltForMaskedLM(_UpperCamelCase    )
  else:
    raise ValueError('''Unknown model type'''    )

  # load state_dict of original model, remove and rename some keys
  _lowercase:      List[Any]   			=  torch.hub.load_state_dict_from_url(_UpperCamelCase			,					map_location='''cpu'''    )['''state_dict''']
  _lowercase:      Dict   			=  create_rename_keys(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )
  for src, dest in rename_keys:
    rename_key(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )
  read_in_q_k_v(_UpperCamelCase			,					_UpperCamelCase    )
  if mlm_model or irtr_model:
    _lowercase:      int   			=  ['''itm_score.fc.weight''', '''itm_score.fc.bias''']
    for k in ignore_keys:
      state_dict.pop(_UpperCamelCase			,					_UpperCamelCase    )

    # load state dict into HuggingFace model
  model.eval()
  if mlm_model:
    _lowercase			,					_lowercase:      Optional[int]   			=  model.load_state_dict(_UpperCamelCase			,					strict=_UpperCamelCase    )
    assert missing_keys == ["mlm_score.decoder.bias"]
  else:
    model.load_state_dict(_UpperCamelCase    )

  # Define processor
  _lowercase:      int   			=  ViltImageProcessor(size=384    )
  _lowercase:      List[str]   			=  BertTokenizer.from_pretrained('''bert-base-uncased'''    )
  _lowercase:      List[Any]   			=  ViltProcessor(_UpperCamelCase			,					_UpperCamelCase    )

  # Forward pass on example inputs (image + text)
  if nlvr_model:
    _lowercase:      int   			=  Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''			,					stream=_UpperCamelCase    ).raw    )
    _lowercase:      List[Any]   			=  Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''			,					stream=_UpperCamelCase    ).raw    )
    _lowercase:      Dict   			=  (
        '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
        ''' standing.'''
    )
    _lowercase:      Any   			=  processor(_UpperCamelCase			,					_UpperCamelCase			,					return_tensors='''pt'''    )
    _lowercase:      Union[str, Any]   			=  processor(_UpperCamelCase			,					_UpperCamelCase			,					return_tensors='''pt'''    )
    _lowercase:      List[str]   			=  model(
        input_ids=encoding_a.input_ids			,					pixel_values=encoding_a.pixel_values			,					pixel_values_a=encoding_a.pixel_values			,					)
  else:
    _lowercase:      Dict   			=  Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg'''			,					stream=_UpperCamelCase    ).raw    )
    if mlm_model:
      _lowercase:      int   			=  '''a bunch of [MASK] laying on a [MASK].'''
    else:
      _lowercase:      Optional[int]   			=  '''How many cats are there?'''
    _lowercase:      Optional[Any]   			=  processor(_UpperCamelCase			,					_UpperCamelCase			,					return_tensors='''pt'''    )
    _lowercase:      Optional[Any]   			=  model(**_UpperCamelCase    )

  # Verify outputs
  if mlm_model:
    _lowercase:      List[str]   			=  torch.Size([1, 11, 30_522]    )
    _lowercase:      Tuple   			=  torch.tensor([-12.5_061, -12.5_123, -12.5_174]    )
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]			,					_UpperCamelCase			,					atol=1e-4    )

    # verify masked token prediction equals "cats"
    _lowercase:      Optional[int]   			=  outputs.logits[0, 4, :].argmax(-1    ).item()
    assert tokenizer.decode([predicted_id]    ) == "cats"
  elif vqa_model:
    _lowercase:      Optional[int]   			=  torch.Size([1, 3_129]    )
    _lowercase:      List[str]   			=  torch.tensor([-15.9_495, -18.1_472, -10.3_041]    )
    assert torch.allclose(outputs.logits[0, :3]			,					_UpperCamelCase			,					atol=1e-4    )
    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, 0, :3]			,					_UpperCamelCase			,					atol=1e-4    )

    # verify vqa prediction equals "2"
    _lowercase:      List[Any]   			=  outputs.logits.argmax(-1    ).item()
    assert model.config.idalabel[predicted_idx] == "2"
  elif nlvr_model:
    _lowercase:      Tuple   			=  torch.Size([1, 2]    )
    _lowercase:      Any   			=  torch.tensor([-2.8_721, 2.1_291]    )
    assert torch.allclose(outputs.logits[0, :3]			,					_UpperCamelCase			,					atol=1e-4    )
    assert outputs.logits.shape == expected_shape

  Path(_UpperCamelCase    ).mkdir(exist_ok=_UpperCamelCase    )
  print(f'''Saving model and processor to {pytorch_dump_folder_path}'''    )
  model.save_pretrained(_UpperCamelCase    )
  processor.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
  A__ :       str				= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
      type=str,
      help='URL of the checkpoint you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  A__ :       str				= parser.parse_args()
  convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
