
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__	:List[str]										=					logging.get_logger(__name__)

lowercase__	:List[str]										=					{
    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
    "BridgeTower/bridgetower-base-itm-mlm": (
        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
    ),
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       ='''bridgetower_vision_model'''




							def __init__(       self     ,A__=7_6_8     ,A__=1_2     ,A__=3     ,A__=1_6     ,A__=2_8_8     ,A__=1     ,A__=1E-05     ,A__=False     ,A__=True     ,A__=False     ,**A__     ,):
												super().__init__(**A__)
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_channels
												lowercase					=		patch_size
												lowercase					=		image_size
												lowercase					=		initializer_factor
												lowercase					=		layer_norm_eps
												lowercase					=		stop_gradient
												lowercase					=		share_layernorm
												lowercase					=		remove_last_layer





							@classmethod
							def 				A__   (       cls     ,A__     ,**A__):
												lowercase			,		lowercase					=		cls.get_config_dict(A__     ,**A__)

												if config_dict.get('''model_type''') == "bridgetower":
																	lowercase					=		config_dict['''text_config''']

												if "model_type" in config_dict and hasattr(cls     ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__     ,**A__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[int]       ='''bridgetower_text_model'''




							def __init__(       self     ,A__=5_0_2_6_5     ,A__=7_6_8     ,A__=1_2     ,A__=1_2     ,A__=1     ,A__=3_0_7_2     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=5_1_4     ,A__=1     ,A__=1E-05     ,A__=1     ,A__=0     ,A__=2     ,A__="absolute"     ,A__=True     ,**A__     ,):
												super().__init__(**A__)

												lowercase					=		vocab_size
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_attention_heads
												lowercase					=		hidden_act
												lowercase					=		initializer_factor
												lowercase					=		intermediate_size
												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		max_position_embeddings
												lowercase					=		type_vocab_size
												lowercase					=		layer_norm_eps
												lowercase					=		position_embedding_type
												lowercase					=		use_cache
												lowercase					=		pad_token_id
												lowercase					=		bos_token_id
												lowercase					=		eos_token_id





							@classmethod
							def 				A__   (       cls     ,A__     ,**A__):
												lowercase			,		lowercase					=		cls.get_config_dict(A__     ,**A__)

												if config_dict.get('''model_type''') == "bridgetower":
																	lowercase					=		config_dict['''text_config''']

												if "model_type" in config_dict and hasattr(cls     ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__     ,**A__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Any       ='''bridgetower'''




							def __init__(       self     ,A__=True     ,A__="gelu"     ,A__=7_6_8     ,A__=1     ,A__=1E-05     ,A__=False     ,A__="add"     ,A__=1_2     ,A__=6     ,A__=False     ,A__=False     ,A__=None     ,A__=None     ,**A__     ,):
												# TODO: remove this once the Hub files are updated.
												lowercase					=		kwargs.pop('''text_config_dict'''     ,A__)
												lowercase					=		kwargs.pop('''vision_config_dict'''     ,A__)

												super().__init__(**A__)
												lowercase					=		share_cross_modal_transformer_layers
												lowercase					=		hidden_act
												lowercase					=		hidden_size
												lowercase					=		initializer_factor
												lowercase					=		layer_norm_eps
												lowercase					=		share_link_tower_layers
												lowercase					=		link_tower_type
												lowercase					=		num_attention_heads
												lowercase					=		num_hidden_layers
												lowercase					=		tie_word_embeddings
												lowercase					=		init_layernorm_from_vision_encoder

												if text_config is None:
																	lowercase					=		{}
																	logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.''')

												if vision_config is None:
																	lowercase					=		{}
																	logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.''')

												lowercase					=		BridgeTowerTextConfig(**A__)
												lowercase					=		BridgeTowerVisionConfig(**A__)




							@classmethod
							def 				A__   (       cls     ,A__     ,A__     ,**A__):
												return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**A__)





							def 				A__   (       self):
												lowercase					=		copy.deepcopy(self.__dict__)
												lowercase					=		self.text_config.to_dict()
												lowercase					=		self.vision_config.to_dict()
												lowercase					=		self.__class__.model_type
												return output






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__	:Optional[Any]										=					re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase__	:List[str]										=					10
lowercase__	:Tuple										=					256



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < MIN_NUM_TOKENS:
										return None
					lowercase					=		MinHash(num_perm=lowerCAmelCase__	)
					for token in set(lowerCAmelCase__	):
										min_hash.update(token.encode()	)
					return min_hash



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return {t for t in NON_ALPHA.split(lowerCAmelCase__	) if len(t.strip()	) > 0}







class        lowercase	:




							def __init__(       self     ,*,
							    A__ = 0.85     ,):
												lowercase					=		duplication_jaccard_threshold
												lowercase					=		NUM_PERM
												lowercase					=		MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm)

												lowercase					=		defaultdict(A__)




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		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):
												lowercase					=		[]
												for base, duplicates in self._duplicate_clusters.items():
																	lowercase					=		[base] + list(A__)
																	# reformat the cluster to be a list of dict
																	lowercase					=		[{'''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__):
												lowercase					=		self.get_duplicate_clusters()
												with open(A__     ,'''w''') as f:
																	json.dump(A__     ,A__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		element
					lowercase					=		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					(							lowerCAmelCase__	):
					'''simple docstring'''
					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(lowerCAmelCase__				,    max_queue_size=1_0000	)				,    chunksize=100				,    ):
															if data is not None:
																				yield data



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase__	)

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

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



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		get_tokens(lowerCAmelCase__	)
					lowercase					=		get_tokens(lowerCAmelCase__	)
					return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowercase__	:List[Any]										=					None



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[]
					for elementa in cluster:
										lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
										for elementa in extremes:
															lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
															if jaccard_similarity(lowerCAmelCase__				,    lowerCAmelCase__	) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															lowercase					=		1
															extremes.append(lowerCAmelCase__	)
					return extremes



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					global _shared_dataset
					lowercase					=		dataset
					lowercase					=		[]
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.85	):
					'''simple docstring'''
					lowercase					=		make_duplicate_clusters(lowerCAmelCase__				,    lowerCAmelCase__	)
					lowercase					=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
					lowercase					=		{}
					lowercase					=		find_extremes(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
					for extremes in extremes_clusters:
										for element in extremes:
															lowercase					=		element
					lowercase					=		duplicate_indices - set(extreme_dict.keys()	)
					lowercase					=		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:
															lowercase					=		element['''base_index'''] in extreme_dict
															if element["is_extreme"]:
																				lowercase					=		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




