


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_ :					Any       =			logging.get_logger(__name__)

UpperCAmelCase_ :					List[str]       =			{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class 					SCREAMING_SNAKE_CASE__    (							lowercase__     ):
      snake_case__							:	Dict	  =		'instructblip_vision_model'
      def __init__(			self							:			Dict	,				SCREAMING_SNAKE_CASE__							:			Tuple=1_4_0_8	,				SCREAMING_SNAKE_CASE__							:			List[str]=6_1_4_4	,				SCREAMING_SNAKE_CASE__							:			str=3_9	,				SCREAMING_SNAKE_CASE__							:			Tuple=1_6	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=2_2_4	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]=1_4	,				SCREAMING_SNAKE_CASE__							:			Any="gelu"	,				SCREAMING_SNAKE_CASE__							:			List[Any]=1E-6	,				SCREAMING_SNAKE_CASE__							:			List[str]=0.0	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=1E-10	,				SCREAMING_SNAKE_CASE__							:			Optional[int]=True	,				**SCREAMING_SNAKE_CASE__							:			Optional[int]	,				)						->     Union[str, Any]:
             super().__init__(**SCREAMING_SNAKE_CASE__	)

             a_   :						List[str]				=    hidden_size
             a_   :						str				=    intermediate_size
             a_   :						str				=    num_hidden_layers
             a_   :						Dict				=    num_attention_heads
             a_   :						Union[str, Any]				=    patch_size
             a_   :						Union[str, Any]				=    image_size
             a_   :						List[Any]				=    initializer_range
             a_   :						int				=    attention_dropout
             a_   :						Union[str, Any]				=    layer_norm_eps
             a_   :						Tuple				=    hidden_act
             a_   :						List[str]				=    qkv_bias

      @classmethod
      def 			SCREAMING_SNAKE_CASE				(			cls							:			Optional[Any]	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]	,				**SCREAMING_SNAKE_CASE__							:			List[str]	)						->     "PretrainedConfig":
             cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__	)

             a_				,						a_   :						Optional[Any]				=    cls.get_config_dict(SCREAMING_SNAKE_CASE__	,				**SCREAMING_SNAKE_CASE__	)

             # get the vision config dict if we are loading from InstructBlipConfig
             if config_dict.get('model_type'	) == "instructblip":
                    a_   :						Union[str, Any]				=    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(SCREAMING_SNAKE_CASE__	,				**SCREAMING_SNAKE_CASE__	)
class 					SCREAMING_SNAKE_CASE__    (							lowercase__     ):
      snake_case__							:	Dict	  =		'instructblip_qformer'
      def __init__(			self							:			List[Any]	,				SCREAMING_SNAKE_CASE__							:			Dict=3_0_5_2_2	,				SCREAMING_SNAKE_CASE__							:			Any=7_6_8	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=1_2	,				SCREAMING_SNAKE_CASE__							:			Dict=1_2	,				SCREAMING_SNAKE_CASE__							:			str=3_0_7_2	,				SCREAMING_SNAKE_CASE__							:			List[str]="gelu"	,				SCREAMING_SNAKE_CASE__							:			str=0.1	,				SCREAMING_SNAKE_CASE__							:			Any=0.1	,				SCREAMING_SNAKE_CASE__							:			Any=5_1_2	,				SCREAMING_SNAKE_CASE__							:			str=0.02	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]=1E-12	,				SCREAMING_SNAKE_CASE__							:			List[str]=0	,				SCREAMING_SNAKE_CASE__							:			str="absolute"	,				SCREAMING_SNAKE_CASE__							:			str=2	,				SCREAMING_SNAKE_CASE__							:			List[Any]=1_4_0_8	,				**SCREAMING_SNAKE_CASE__							:			List[Any]	,				)						->     Any:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__	,				**SCREAMING_SNAKE_CASE__	)

             a_   :						Optional[int]				=    vocab_size
             a_   :						str				=    hidden_size
             a_   :						Optional[int]				=    num_hidden_layers
             a_   :						Optional[Any]				=    num_attention_heads
             a_   :						int				=    hidden_act
             a_   :						List[Any]				=    intermediate_size
             a_   :						str				=    hidden_dropout_prob
             a_   :						Any				=    attention_probs_dropout_prob
             a_   :						List[str]				=    max_position_embeddings
             a_   :						Optional[int]				=    initializer_range
             a_   :						Optional[Any]				=    layer_norm_eps
             a_   :						int				=    position_embedding_type
             a_   :						Dict				=    cross_attention_frequency
             a_   :						Optional[Any]				=    encoder_hidden_size

      @classmethod
      def 			SCREAMING_SNAKE_CASE				(			cls							:			List[str]	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]	,				**SCREAMING_SNAKE_CASE__							:			Tuple	)						->     "PretrainedConfig":
             cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__	)

             a_				,						a_   :						Tuple				=    cls.get_config_dict(SCREAMING_SNAKE_CASE__	,				**SCREAMING_SNAKE_CASE__	)

             # get the qformer config dict if we are loading from InstructBlipConfig
             if config_dict.get('model_type'	) == "instructblip":
                    a_   :						int				=    config_dict['qformer_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(SCREAMING_SNAKE_CASE__	,				**SCREAMING_SNAKE_CASE__	)
class 					SCREAMING_SNAKE_CASE__    (							lowercase__     ):
      snake_case__							:	Union[str, Any]	  =		'instructblip'
      snake_case__							:	str	  =		True
      def __init__(			self							:			Any	,				SCREAMING_SNAKE_CASE__							:			Any=None	,				SCREAMING_SNAKE_CASE__							:			Tuple=None	,				SCREAMING_SNAKE_CASE__							:			int=None	,				SCREAMING_SNAKE_CASE__							:			Dict=3_2	,				**SCREAMING_SNAKE_CASE__							:			Tuple	)						->     Dict:
             super().__init__(**SCREAMING_SNAKE_CASE__	)

             if vision_config is None:
                    a_   :						List[Any]				=    {}
                    logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'	)

             if qformer_config is None:
                    a_   :						Optional[int]				=    {}
                    logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'	)

             if text_config is None:
                    a_   :						Tuple				=    {}
                    logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'	)

             a_   :						List[Any]				=    InstructBlipVisionConfig(**SCREAMING_SNAKE_CASE__	)
             a_   :						Optional[int]				=    InstructBlipQFormerConfig(**SCREAMING_SNAKE_CASE__	)
             a_   :						Dict				=    text_config['model_type'] if 'model_type' in text_config else 'opt'
             a_   :						List[Any]				=    CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE__	)

             a_   :						List[Any]				=    self.text_config.tie_word_embeddings
             a_   :						List[Any]				=    self.text_config.is_encoder_decoder

             a_   :						str				=    num_query_tokens
             a_   :						List[Any]				=    self.vision_config.hidden_size
             a_   :						Any				=    self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
             a_   :						Any				=    1.0
             a_   :						Union[str, Any]				=    0.02
      @classmethod
      def 			SCREAMING_SNAKE_CASE				(			cls							:			Union[str, Any]	,				SCREAMING_SNAKE_CASE__							:			Union[str, Any]	,				SCREAMING_SNAKE_CASE__							:			int	,				SCREAMING_SNAKE_CASE__							:			Tuple	,				**SCREAMING_SNAKE_CASE__							:			Optional[Any]	,				)						->     List[str]:
             return cls(
                 vision_config=vision_config.to_dict()	,				qformer_config=qformer_config.to_dict()	,				text_config=text_config.to_dict()	,				**SCREAMING_SNAKE_CASE__	,				)

      def 			SCREAMING_SNAKE_CASE				(			self							:			Any	)						->     Union[str, Any]:
             a_   :						List[Any]				=    copy.deepcopy(self.__dict__	)
             a_   :						Optional[Any]				=    self.vision_config.to_dict()
             a_   :						Union[str, Any]				=    self.qformer_config.to_dict()
             a_   :						str				=    self.text_config.to_dict()
             a_   :						Any				=    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_									=    10
lowercase_									=    256




def lowerCAmelCase    (__A):



     """simple docstring"""



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




def lowerCAmelCase    (__A):



     """simple docstring"""



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






class __A     :



 '''simple docstring'''







 def __init__(self					,  *,
     A = 0.85					,  )       ->	Optional[int]:


      """simple docstring"""



      _a           =    duplication_jaccard_threshold
      _a           =    NUM_PERM
      _a           =    MinHashLSH(threshold=self._duplication_jaccard_threshold					,  num_perm=self._num_perm			)

      _a           =    defaultdict(A			)



 def    a__    (self					,  A					,  A			)       ->	None:


      """simple docstring"""



      _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    a__    (self			)       ->	List[List[Dict]]:


      """simple docstring"""



      _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    a__    (self					,  A			)       ->	None:


      """simple docstring"""



      _a           =    self.get_duplicate_clusters()
      with open(A					,  '''w'''			) as f:
           json.dump(A					,  A			)





def lowerCAmelCase    (__A):



     """simple docstring"""



     _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 lowerCAmelCase    (__A):



     """simple docstring"""



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




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    DuplicationIndex(duplication_jaccard_threshold=__A)

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

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




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    get_tokens(__A)
     _a           =    get_tokens(__A)
     return len(tokensa & tokensa) / len(tokensa | tokensa)


lowercase_									=    None




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _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(__A    ,      __A) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               _a           =    1
               extremes.append(__A)
     return extremes




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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




def lowerCAmelCase    (__A    ,      __A = 0.85):



     """simple docstring"""



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

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

     return ds_filter, duplicate_clusters



