






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     :      int   					=re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
_A     :      Dict   					=10
_A     :      Optional[Any]   					=256





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[MinHash]:
							if len(UpperCamelCase				) < MIN_NUM_TOKENS:
														return None
							lowerCamelCase__ :     Dict	  =	MinHash(num_perm=UpperCamelCase				)
							for token in set(UpperCamelCase				):
														min_hash.update(token.encode()				)
							return min_hash





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Set[str]:
							return {t for t in NON_ALPHA.split(UpperCamelCase				) if len(t.strip()				) > 0}

class _lowercase     :




							def __init__(							self:    Dict      ,							*,
							    UpperCamelCase__:    float = 0.85      ,							):
														lowerCamelCase__ :     Tuple	  =	duplication_jaccard_threshold
														lowerCamelCase__ :     List[str]	  =	NUM_PERM
														lowerCamelCase__ :     Optional[int]	  =	MinHashLSH(threshold=self._duplication_jaccard_threshold      ,							num_perm=self._num_perm					)

														lowerCamelCase__ :     List[Any]	  =	defaultdict(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    MinHash					):
														lowerCamelCase__ :     Any	  =	self._index.query(UpperCamelCase__					)
														if code_key in self._index.keys:
																					print(F'''Duplicate key {code_key}'''					)
																					return

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




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Tuple	  =	[]
														for base, duplicates in self._duplicate_clusters.items():
																					lowerCamelCase__ :     Dict	  =	[base] + list(UpperCamelCase__					)
																					# reformat the cluster to be a list of dict
																					lowerCamelCase__ :     Union[str, Any]	  =	[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
																					duplicate_clusters.append(UpperCamelCase__					)
														return duplicate_clusters




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     List[Any]	  =	self.get_duplicate_clusters()
														with open(UpperCamelCase__      ,							"""w"""					) as f:
																					json.dump(UpperCamelCase__      ,							UpperCamelCase__					)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[Any]:
							lowerCamelCase__ :     Dict	  =	element
							lowerCamelCase__ :     Optional[Any]	  =	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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Union[str, Any]:
							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash    ,      ThreadedIterator(UpperCamelCase    ,      max_queue_size=10000				)    ,      chunksize=100    ,      ):
																					if data is not None:
																												yield data





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[str]:
							lowerCamelCase__ :     Any	  =	DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase				)

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

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	float:
							lowerCamelCase__ :     Any	  =	get_tokens(UpperCamelCase				)
							lowerCamelCase__ :     Tuple	  =	get_tokens(UpperCamelCase				)
							return len(tokensa & tokensa				) / len(tokensa | tokensa				)


_A     :      List[Any]   					=None





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Union[str, Any]:
							lowerCamelCase__ :     str	  =	[]
							for elementa in cluster:
														lowerCamelCase__ :     List[Any]	  =	_shared_dataset[elementa["""base_index"""]]["""content"""]
														for elementa in extremes:
																					lowerCamelCase__ :     Any	  =	_shared_dataset[elementa["""base_index"""]]["""content"""]
																					if jaccard_similarity(UpperCamelCase    ,      UpperCamelCase				) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					lowerCamelCase__ :     Optional[Any]	  =	1
																					extremes.append(UpperCamelCase				)
							return extremes





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	List[str]:
							global _shared_dataset
							lowerCamelCase__ :     Dict	  =	dataset
							lowerCamelCase__ :     Dict	  =	[]
							lowerCamelCase__ :     Dict	  =	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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = 0.85				)				->	Tuple[Type[Dataset], List[List[Dict]]]:
							lowerCamelCase__ :     List[str]	  =	make_duplicate_clusters(UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
							lowerCamelCase__ :     Any	  =	{}
							lowerCamelCase__ :     List[str]	  =	find_extremes(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							for extremes in extremes_clusters:
														for element in extremes:
																					lowerCamelCase__ :     int	  =	element
							lowerCamelCase__ :     Any	  =	duplicate_indices - set(extreme_dict.keys()				)
							lowerCamelCase__ :     str	  =	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:
																					lowerCamelCase__ :     List[Any]	  =	element["""base_index"""] in extreme_dict
																					if element["is_extreme"]:
																												lowerCamelCase__ :     Any	  =	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 requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[Any]:
							if "cls_token" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""cls_token"""    ,      """vit.embeddings.cls_token"""				)
							if "mask_token" in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""mask_token"""    ,      """decoder.mask_token"""				)
							if "decoder_pos_embed" in name:
														lowerCamelCase__ :     Tuple	  =	name.replace("""decoder_pos_embed"""    ,      """decoder.decoder_pos_embed"""				)
							if "pos_embed" in name and "decoder" not in name:
														lowerCamelCase__ :     str	  =	name.replace("""pos_embed"""    ,      """vit.embeddings.position_embeddings"""				)
							if "patch_embed.proj" in name:
														lowerCamelCase__ :     Optional[int]	  =	name.replace("""patch_embed.proj"""    ,      """vit.embeddings.patch_embeddings.projection"""				)
							if "patch_embed.norm" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""patch_embed.norm"""    ,      """vit.embeddings.norm"""				)
							if "decoder_blocks" in name:
														lowerCamelCase__ :     Dict	  =	name.replace("""decoder_blocks"""    ,      """decoder.decoder_layers"""				)
							if "blocks" in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""blocks"""    ,      """vit.encoder.layer"""				)
							if "attn.proj" in name:
														lowerCamelCase__ :     List[Any]	  =	name.replace("""attn.proj"""    ,      """attention.output.dense"""				)
							if "attn" in name:
														lowerCamelCase__ :     List[str]	  =	name.replace("""attn"""    ,      """attention.self"""				)
							if "norm1" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""norm1"""    ,      """layernorm_before"""				)
							if "norm2" in name:
														lowerCamelCase__ :     Dict	  =	name.replace("""norm2"""    ,      """layernorm_after"""				)
							if "mlp.fc1" in name:
														lowerCamelCase__ :     str	  =	name.replace("""mlp.fc1"""    ,      """intermediate.dense"""				)
							if "mlp.fc2" in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""mlp.fc2"""    ,      """output.dense"""				)
							if "decoder_embed" in name:
														lowerCamelCase__ :     Tuple	  =	name.replace("""decoder_embed"""    ,      """decoder.decoder_embed"""				)
							if "decoder_norm" in name:
														lowerCamelCase__ :     Optional[int]	  =	name.replace("""decoder_norm"""    ,      """decoder.decoder_norm"""				)
							if "decoder_pred" in name:
														lowerCamelCase__ :     int	  =	name.replace("""decoder_pred"""    ,      """decoder.decoder_pred"""				)
							if "norm.weight" in name and "decoder" not in name:
														lowerCamelCase__ :     Union[str, Any]	  =	name.replace("""norm.weight"""    ,      """vit.layernorm.weight"""				)
							if "norm.bias" in name and "decoder" not in name:
														lowerCamelCase__ :     Dict	  =	name.replace("""norm.bias"""    ,      """vit.layernorm.bias"""				)

							return name





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Tuple:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__ :     List[str]	  =	orig_state_dict.pop(UpperCamelCase				)

														if "qkv" in key:
																					lowerCamelCase__ :     List[Any]	  =	key.split("""."""				)
																					lowerCamelCase__ :     Optional[int]	  =	int(key_split[1]				)
																					if "decoder_blocks" in key:
																												lowerCamelCase__ :     str	  =	config.decoder_hidden_size
																												lowerCamelCase__ :     List[Any]	  =	"""decoder.decoder_layers."""
																												if "weight" in key:
																																			lowerCamelCase__ :     int	  =	val[:dim, :]
																																			lowerCamelCase__ :     int	  =	val[dim : dim * 2, :]
																																			lowerCamelCase__ :     Tuple	  =	val[-dim:, :]
																												elif "bias" in key:
																																			lowerCamelCase__ :     Tuple	  =	val[:dim]
																																			lowerCamelCase__ :     Optional[int]	  =	val[dim : dim * 2]
																																			lowerCamelCase__ :     List[Any]	  =	val[-dim:]
																					else:
																												lowerCamelCase__ :     List[Any]	  =	config.hidden_size
																												lowerCamelCase__ :     Optional[int]	  =	"""vit.encoder.layer."""
																												if "weight" in key:
																																			lowerCamelCase__ :     str	  =	val[:dim, :]
																																			lowerCamelCase__ :     List[Any]	  =	val[dim : dim * 2, :]
																																			lowerCamelCase__ :     Optional[int]	  =	val[-dim:, :]
																												elif "bias" in key:
																																			lowerCamelCase__ :     int	  =	val[:dim]
																																			lowerCamelCase__ :     List[Any]	  =	val[dim : dim * 2]
																																			lowerCamelCase__ :     Optional[int]	  =	val[-dim:]

														else:
																					lowerCamelCase__ :     int	  =	val

							return orig_state_dict





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	int:
							lowerCamelCase__ :     Any	  =	ViTMAEConfig()
							if "large" in checkpoint_url:
														lowerCamelCase__ :     Any	  =	1024
														lowerCamelCase__ :     Optional[Any]	  =	4096
														lowerCamelCase__ :     List[str]	  =	24
														lowerCamelCase__ :     Union[str, Any]	  =	16
							elif "huge" in checkpoint_url:
														lowerCamelCase__ :     List[str]	  =	14
														lowerCamelCase__ :     Dict	  =	1280
														lowerCamelCase__ :     Tuple	  =	5120
														lowerCamelCase__ :     List[str]	  =	32
														lowerCamelCase__ :     Union[str, Any]	  =	16

							lowerCamelCase__ :     List[Any]	  =	ViTMAEForPreTraining(UpperCamelCase				)

							lowerCamelCase__ :     str	  =	torch.hub.load_state_dict_from_url(UpperCamelCase    ,      map_location="""cpu"""				)["""model"""]

							lowerCamelCase__ :     Union[str, Any]	  =	ViTMAEImageProcessor(size=config.image_size				)

							lowerCamelCase__ :     List[str]	  =	convert_state_dict(UpperCamelCase    ,      UpperCamelCase				)

							model.load_state_dict(UpperCamelCase				)
							model.eval()

							lowerCamelCase__ :     Union[str, Any]	  =	"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

							lowerCamelCase__ :     List[Any]	  =	Image.open(requests.get(UpperCamelCase    ,      stream=UpperCamelCase				).raw				)
							lowerCamelCase__ :     str	  =	ViTMAEImageProcessor(size=config.image_size				)
							lowerCamelCase__ :     Any	  =	image_processor(images=UpperCamelCase    ,      return_tensors="""pt"""				)

							# forward pass
							torch.manual_seed(2				)
							lowerCamelCase__ :     Optional[Any]	  =	model(**UpperCamelCase				)
							lowerCamelCase__ :     Optional[Any]	  =	outputs.logits

							if "large" in checkpoint_url:
														lowerCamelCase__ :     List[Any]	  =	torch.tensor(
														    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]				)
							elif "huge" in checkpoint_url:
														lowerCamelCase__ :     Optional[Any]	  =	torch.tensor(
														    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
							else:
														lowerCamelCase__ :     int	  =	torch.tensor(
														    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]				)

							# verify logits
							assert torch.allclose(logits[0, :3, :3]    ,      UpperCamelCase    ,      atol=1E-4				)

							print(f'''Saving model to {pytorch_dump_folder_path}'''				)
							model.save_pretrained(UpperCamelCase				)

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
							image_processor.save_pretrained(UpperCamelCase				)


if __name__ == "__main__":
			_A     :      str   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--checkpoint_url''',
			    default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
			    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     :      Tuple   					=parser.parse_args()
			convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

