


'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

a__  :					Optional[int]        =       {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	'deta'
    __UpperCAmelCase   			=	{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }


    def __init__(     self  ,					a=None  ,					a=9_00  ,					a=20_48  ,					a=6  ,					a=20_48  ,					a=8  ,					a=6  ,					a=10_24  ,					a=8  ,					a=0.0  ,					a=True  ,					a="relu"  ,					a=2_56  ,					a=0.1  ,					a=0.0  ,					a=0.0  ,					a=0.02  ,					a=1.0  ,					a=True  ,					a=False  ,					a="sine"  ,					a=5  ,					a=4  ,					a=4  ,					a=True  ,					a=3_00  ,					a=True  ,					a=True  ,					a=1  ,					a=5  ,					a=2  ,					a=1  ,					a=1  ,					a=5  ,					a=2  ,					a=0.1  ,					a=0.25  ,					**a  ,					):
          if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."    )
                UpperCamelCase__										=				CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]    )
          else:
                if isinstance(a  ,					a    ):
                      UpperCamelCase__										=				backbone_config.pop("model_type"    )
                      UpperCamelCase__										=				CONFIG_MAPPING[backbone_model_type]
                      UpperCamelCase__										=				config_class.from_dict(a    )

          UpperCamelCase__										=				backbone_config
          UpperCamelCase__										=				num_queries
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				d_model
          UpperCamelCase__										=				encoder_ffn_dim
          UpperCamelCase__										=				encoder_layers
          UpperCamelCase__										=				encoder_attention_heads
          UpperCamelCase__										=				decoder_ffn_dim
          UpperCamelCase__										=				decoder_layers
          UpperCamelCase__										=				decoder_attention_heads
          UpperCamelCase__										=				dropout
          UpperCamelCase__										=				attention_dropout
          UpperCamelCase__										=				activation_dropout
          UpperCamelCase__										=				activation_function
          UpperCamelCase__										=				init_std
          UpperCamelCase__										=				init_xavier_std
          UpperCamelCase__										=				encoder_layerdrop
          UpperCamelCase__										=				auxiliary_loss
          UpperCamelCase__										=				position_embedding_type
          # deformable attributes
          UpperCamelCase__										=				num_feature_levels
          UpperCamelCase__										=				encoder_n_points
          UpperCamelCase__										=				decoder_n_points
          UpperCamelCase__										=				two_stage
          UpperCamelCase__										=				two_stage_num_proposals
          UpperCamelCase__										=				with_box_refine
          UpperCamelCase__										=				assign_first_stage
          if two_stage is True and with_box_refine is False:
                raise ValueError("If two_stage is True, with_box_refine must be True."    )
          # Hungarian matcher
          UpperCamelCase__										=				class_cost
          UpperCamelCase__										=				bbox_cost
          UpperCamelCase__										=				giou_cost
          # Loss coefficients
          UpperCamelCase__										=				mask_loss_coefficient
          UpperCamelCase__										=				dice_loss_coefficient
          UpperCamelCase__										=				bbox_loss_coefficient
          UpperCamelCase__										=				giou_loss_coefficient
          UpperCamelCase__										=				eos_coefficient
          UpperCamelCase__										=				focal_alpha
          super().__init__(is_encoder_decoder=a  ,					**a    )


    @property
    def    __a							(     self    ):
          return self.encoder_attention_heads


    @property
    def    __a							(     self    ):
          return self.d_model


    def    __a							(     self    ):
          UpperCamelCase__										=				copy.deepcopy(self.__dict__    )
          UpperCamelCase__										=				self.backbone_config.to_dict()
          UpperCamelCase__										=				self.__class__.model_type
          return output









"""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_			     =  1_0
lowercase_			     =  2_5_6

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Optional[MinHash]:
		if len(lowerCAmelCase__			) < MIN_NUM_TOKENS:
				return None
		__a           =			MinHash(num_perm=lowerCAmelCase__			)
		for token in set(lowerCAmelCase__			):
				min_hash.update(token.encode()			)
		return min_hash

def        lowercase							(		lowerCAmelCase__ :     str			) ->      Set[str]:
		return {t for t in NON_ALPHA.split(lowerCAmelCase__			) if len(t.strip()			) > 0}
class 					__lowerCAmelCase					:


		'''simple docstring'''



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

				__a           =			defaultdict(_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__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 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.get_duplicate_clusters()
				with open(_a							, '''w'''      ) as f:
						json.dump(_a							, _a      )







def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      int:
		__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        lowercase							(		lowerCAmelCase__ :     Type[Dataset]			) ->      str:
		with mp.Pool() as pool:
				for data in pool.imap_unordered(
				    _compute_min_hash    ,					ThreadedIterator(lowerCAmelCase__    ,					max_queue_size=10000			)    ,					chunksize=100    ,					):
						if data is not None:
								yield data

def        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float			) ->      Dict:
		__a           =			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        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      float:
		__a           =			get_tokens(lowerCAmelCase__			)
		__a           =			get_tokens(lowerCAmelCase__			)
		return len(tokensa & tokensa			) / len(tokensa | tokensa			)


lowercase_			     =  None

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Union[str, Any]			) ->      Any:
		__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(lowerCAmelCase__    ,					lowerCAmelCase__			) >= jaccard_threshold:
								elementa["copies"] += 1
								break
				else:
						__a           =			1
						extremes.append(lowerCAmelCase__			)
		return extremes

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[int]			) ->      Optional[int]:
		global _shared_dataset
		__a           =			dataset
		__a           =			[]
		__a           =			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        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float = 0.85			) ->      Tuple[Type[Dataset], List[List[Dict]]]:
		__a           =			make_duplicate_clusters(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
		__a           =			{}
		__a           =			find_extremes(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		for extremes in extremes_clusters:
				for element in extremes:
						__a           =			element
		__a           =			duplicate_indices - set(extreme_dict.keys()			)
		__a           =			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:
						__a           =			element['''base_index'''] in extreme_dict
						if element["is_extreme"]:
								__a           =			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






