


"""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


__snake_case       =       re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__snake_case       =       10
__snake_case       =       256







def     __lowerCAmelCase		(		lowercase    :    List[str]					)      ->		Optional[MinHash]:


    """simple docstring"""





    if len(lowercase					) < MIN_NUM_TOKENS:
        return None
    snake_case       : Optional[int]    =       MinHash(num_perm=lowercase					)
    for token in set(lowercase					):
        min_hash.update(token.encode()					)
    return min_hash







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		Set[str]:


    """simple docstring"""





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







class _lowerCAmelCase							:







   def __init__(      self		,      *,
       UpperCamelCase__ = 0.85		,      )      ->						Dict:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       duplication_jaccard_threshold
       snake_case       : Tuple    =       NUM_PERM
       snake_case       : Dict    =       MinHashLSH(threshold=self._duplication_jaccard_threshold		,      num_perm=self._num_perm		)

       snake_case       : Tuple    =       defaultdict(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       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 					lowerCamelCase						(      self		)      ->						List[List[Dict]]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       []
       for base, duplicates in self._duplicate_clusters.items():
           snake_case       : str    =       [base] + list(UpperCamelCase__		)
           # reformat the cluster to be a list of dict
           snake_case       : List[Any]    =       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
           duplicate_clusters.append(UpperCamelCase__		)
       return duplicate_clusters







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       snake_case       : Dict    =       self.get_duplicate_clusters()
       with open(UpperCamelCase__		,      "w"		) as f:
           json.dump(UpperCamelCase__		,      UpperCamelCase__		)







def     __lowerCAmelCase		(		lowercase    :    List[Any]					)      ->		Optional[Any]:


    """simple docstring"""





    snake_case   ,snake_case       : Union[str, Any]    =       element
    snake_case       : List[Any]    =       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		(		lowercase    :    Type[Dataset]					)      ->		Union[str, Any]:


    """simple docstring"""





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







def     __lowerCAmelCase		(		lowercase    :    Type[Dataset]		,				lowercase    :    float					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Any    =       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     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str					)      ->		float:


    """simple docstring"""





    snake_case       : Dict    =       get_tokens(lowercase					)
    snake_case       : List[str]    =       get_tokens(lowercase					)
    return len(tokensa & tokensa					) / len(tokensa | tokensa					)


__snake_case       =       None







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]		,				lowercase    :    Optional[int]					)      ->		int:


    """simple docstring"""





    snake_case       : Union[str, Any]    =       []
    for elementa in cluster:
        snake_case       : str    =       _shared_dataset[elementa["base_index"]]["content"]
        for elementa in extremes:
            snake_case       : Dict    =       _shared_dataset[elementa["base_index"]]["content"]
            if jaccard_similarity(lowercase		,				lowercase					) >= jaccard_threshold:
                elementa["copies"] += 1
                break
        else:
            snake_case       : List[str]    =       1
            extremes.append(lowercase					)
    return extremes







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    Any		,				lowercase    :    int					)      ->		List[Any]:


    """simple docstring"""





    global _shared_dataset
    snake_case       : int    =       dataset
    snake_case       : int    =       []
    snake_case       : Union[str, Any]    =       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     __lowerCAmelCase		(		lowercase    :    Type[Dataset]		,				lowercase    :    float = 0.85					)      ->		Tuple[Type[Dataset], List[List[Dict]]]:


    """simple docstring"""





    snake_case       : Any    =       make_duplicate_clusters(lowercase		,				lowercase					)
    snake_case       : Union[str, Any]    =       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
    snake_case       : List[Any]    =       {}
    snake_case       : Optional[int]    =       find_extremes(lowercase		,				lowercase		,				lowercase					)
    for extremes in extremes_clusters:
        for element in extremes:
            snake_case       : List[Any]    =       element
    snake_case       : List[str]    =       duplicate_indices - set(extreme_dict.keys()					)
    snake_case       : Optional[Any]    =       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:
            snake_case       : Tuple    =       element["base_index"] in extreme_dict
            if element["is_extreme"]:
                snake_case       : Tuple    =       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"""



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       AlbertForPreTraining(lowercase					)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(lowercase		,				lowercase		,				lowercase					)

    # Save pytorch-model
    print(F'Save PyTorch model to {pytorch_dump_path}'					)
    torch.save(model.state_dict()		,				lowercase					)


if __name__ == "__main__":
  __snake_case       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--albert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained ALBERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __snake_case       =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





