




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


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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Optional[MinHash]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Set[str]:



       """simple docstring"""

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


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      Tuple   ,					*,
        UpperCAmelCase__ :      float = 0.85   ,					)		->int:





           '''simple docstring'''

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

           A__ 					=       defaultdict(UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      MinHash)		->None:





           '''simple docstring'''

           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->List[List[Dict]]:





           '''simple docstring'''

           A__ 					=       []
           for base, duplicates in self._duplicate_clusters.items():
                  A__ 					=       [base] + list(UpperCAmelCase__)
                  # 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(UpperCAmelCase__)
           return duplicate_clusters





    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      Tuple)		->None:





           '''simple docstring'''

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Optional[Any]:



       """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 SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Dict:



       """simple docstring"""

       A__ 					=       DuplicationIndex(duplication_jaccard_threshold=lowercase_		)

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

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			float:



       """simple docstring"""

       A__ 					=       get_tokens(lowercase_		)
       A__ 					=       get_tokens(lowercase_		)
       return len(tokensa & tokensa		) / len(tokensa | tokensa		)


_lowerCamelCase      :						Any							=  None







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Optional[int]:



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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			List[str]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = 0.85		)  ->			Tuple[Type[Dataset], List[List[Dict]]]:



       """simple docstring"""

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

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

       return ds_filter, duplicate_clusters









"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		"""philschmid/bart-large-cnn-samsum"""
				__snake_case :							Optional[int]									=		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				__snake_case :							Dict									=		"""summarizer"""
				__snake_case :							str									=		AutoTokenizer
				__snake_case :							List[str]									=		AutoModelForSeqaSeqLM

				__snake_case :							List[Any]									=		["""text"""]
				__snake_case :							Dict									=		["""text"""]




				def        __lowercase	(  self	:int    ,     __lowercase	:List[Any]      ):
											return self.pre_processor(__lowercase    ,     return_tensors='''pt'''    ,     truncation=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											return self.model.generate(**__lowercase      )[0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											return self.pre_processor.decode(__lowercase    ,     skip_special_tokens=__lowercase    ,     clean_up_tokenization_spaces=__lowercase      )




