



'''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  (				_snake_case  :						List[str]       ):





				'''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						str       ):





				'''simple docstring'''


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


class 				snake_case :



							def __init__(				self     ,*,
							    UpperCAmelCase_ = 0.85     ,)  ->							Optional[int]:
											lowercase__		       =						duplication_jaccard_threshold
											lowercase__		       =						NUM_PERM
											lowercase__		       =						MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm      )

											lowercase__		       =						defaultdict(UpperCAmelCase_      )



							def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							None:
											lowercase__		       =						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 							_a      (				self      )  ->							List[List[Dict]]:
											lowercase__		       =						[]
											for base, duplicates in self._duplicate_clusters.items():
															lowercase__		       =						[base] + list(UpperCAmelCase_      )
															# 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(UpperCAmelCase_      )
											return duplicate_clusters







							def 							_a      (				self     ,UpperCAmelCase_      )  ->							None:
											lowercase__		       =						self.get_duplicate_clusters()
											with open(UpperCAmelCase_     ,"w"      ) as f:
															json.dump(UpperCAmelCase_     ,UpperCAmelCase_      )





def        lowerCamelCase  (				_snake_case  :						Optional[int]       ):





				'''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        lowerCamelCase  (				_snake_case  :						Type[Dataset]       ):





				'''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						Type[Dataset]     ,_snake_case  :						float       ):





				'''simple docstring'''


				lowercase__		       =						DuplicationIndex(duplication_jaccard_threshold=_snake_case       )

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

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




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						str       ):





				'''simple docstring'''


				lowercase__		       =						get_tokens(_snake_case       )
				lowercase__		       =						get_tokens(_snake_case       )
				return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


SCREAMING_SNAKE_CASE__      					=     None




def        lowerCamelCase  (				_snake_case  :						Optional[int]     ,_snake_case  :						Tuple       ):





				'''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(_snake_case     ,_snake_case       ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												lowercase__		       =						1
												extremes.append(_snake_case       )
				return extremes




def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						int     ,_snake_case  :						Tuple       ):





				'''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						Type[Dataset]     ,_snake_case  :						float = 0.85       ):





				'''simple docstring'''


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

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

				return ds_filter, duplicate_clusters









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               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(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["vision_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(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




