

'''simple docstring'''




import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


_lowerCAmelCase         =						"<<<<<<< This should probably be modified because it mentions: "

_lowerCAmelCase         =						"=======\n>>>>>>>\n"

_lowerCAmelCase         =						[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

_lowerCAmelCase         =						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r"tfds\.core", r"datasets"),
    (r"tf\.io\.gfile\.GFile", r"open"),
    (r"tf\.([\w\d]+)", r"datasets.Value('\1')"),
    (r"tfds\.features\.Text\(\)", r"datasets.Value('string')"),
    (r"tfds\.features\.Text\(", r"datasets.Value('string'),"),
    (r"features\s*=\s*tfds.features.FeaturesDict\(", r"features=datasets.Features("),
    (r"tfds\.features\.FeaturesDict\(", r"dict("),
    (r"The TensorFlow Datasets Authors", r"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
    (r"tfds\.", r"datasets."),
    (r"dl_manager\.manual_dir", r"self.config.data_dir"),
    (r"self\.builder_config", r"self.config"),
]




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     return ConvertCommand(args.tfds_path		,						args.datasets_directory			)






class   _SCREAMING_SNAKE_CASE   (   __a     ):



 @staticmethod
 def 			snake_case__  (   a__		:   ArgumentParser      ):
      __magic_name__						       =    parser.add_parser(
          '''convert'''			,				help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''			,				)
      train_parser.add_argument(
          '''--tfds_path'''			,				type=a__			,				required=a__			,				help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''			,				)
      train_parser.add_argument(
          '''--datasets_directory'''			,				type=a__			,				required=a__			,				help='''Path to the HuggingFace Datasets folder.'''      )
      train_parser.set_defaults(func=a__      )



 def __init__(   self		:   str			,				a__		:   str			,				a__		:   str			,				*a__		:   Tuple      ):
      __magic_name__						       =    get_logger('''datasets-cli/converting'''      )

      __magic_name__						       =    tfds_path
      __magic_name__						       =    datasets_directory



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      if os.path.isdir(self._tfds_path      ):
           __magic_name__						       =    os.path.abspath(self._tfds_path      )
      elif os.path.isfile(self._tfds_path      ):
           __magic_name__						       =    os.path.dirname(self._tfds_path      )
      else:
           raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''      )

      __magic_name__						       =    os.path.abspath(self._datasets_directory      )

      self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''      )

      __magic_name__						       =    []
      __magic_name__						       =    []
      __magic_name__						       =    {}

      if os.path.isdir(self._tfds_path      ):
           __magic_name__						       =    os.listdir(a__      )
      else:
           __magic_name__						       =    [os.path.basename(self._tfds_path      )]

      for f_name in file_names:
           self._logger.info(F'''Looking at file {f_name}'''      )
           __magic_name__						       =    os.path.join(a__			,				a__      )
           __magic_name__						       =    os.path.join(a__			,				a__      )

           if not os.path.isfile(a__      ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
                self._logger.info('''Skipping file'''      )
                continue

           with open(a__			,				encoding='''utf-8'''      ) as f:
                __magic_name__						       =    f.readlines()

           __magic_name__						       =    []
           __magic_name__						       =    False
           __magic_name__						       =    False
           __magic_name__						       =    []
           for line in lines:
                __magic_name__						       =    line

                # Convert imports
                if "import tensorflow.compat.v2 as tf" in out_line:
                     continue
                elif "@tfds.core" in out_line:
                     continue
                elif "builder=self" in out_line:
                     continue
                elif "import tensorflow_datasets.public_api as tfds" in out_line:
                     __magic_name__						       =    '''import datasets\n'''
                elif "import tensorflow" in out_line:
                     # order is important here
                     __magic_name__						       =    ''''''
                     continue
                elif "from absl import logging" in out_line:
                     __magic_name__						       =    '''from datasets import logging\n'''
                elif "getLogger" in out_line:
                     __magic_name__						       =    out_line.replace('''getLogger'''			,				'''get_logger'''      )
                elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
                     __magic_name__						       =    True
                     __magic_name__						       =    list(filter(lambda a__      : e in out_line			,				a__      )      )
                     out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(a__      ) + '''\n'''      )
                     out_lines.append(a__      )
                     out_lines.append(a__      )
                     continue
                else:
                     for pattern, replacement in TO_CONVERT:
                          __magic_name__						       =    re.sub(a__			,				a__			,				a__      )

                # Take care of saving utilities (to later move them together with main script)
                if "tensorflow_datasets" in out_line:
                     __magic_name__						       =    re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''			,				a__      )
                     tfds_imports.extend(imp.strip() for imp in match.group(1      ).split(''','''      )      )
                     __magic_name__						       =    '''from . import ''' + match.group(1      )

                # Check we have not forget anything
                if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
                     raise ValueError(F'''Error converting {out_line.strip()}'''      )

                if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
                     __magic_name__						       =    True
                out_lines.append(a__      )

           if is_builder or "wmt" in f_name:
                # We create a new directory for each dataset
                __magic_name__						       =    f_name.replace('''.py'''			,				''''''      )
                __magic_name__						       =    os.path.join(a__			,				a__      )
                __magic_name__						       =    os.path.join(a__			,				a__      )
                os.makedirs(a__			,				exist_ok=a__      )
                self._logger.info(F'''Adding directory {output_dir}'''      )
                imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}      )
           else:
                # Utilities will be moved at the end
                utils_files.append(a__      )

           if needs_manual_update:
                with_manual_update.append(a__      )

           with open(a__			,				'''w'''			,				encoding='''utf-8'''      ) as f:
                f.writelines(a__      )
           self._logger.info(F'''Converted in {output_file}'''      )

      for utils_file in utils_files:
           try:
                __magic_name__						       =    os.path.basename(a__      )
                __magic_name__						       =    imports_to_builder_map[f_name.replace('''.py'''			,				''''''      )]
                self._logger.info(F'''Moving {dest_folder} to {utils_file}'''      )
                shutil.copy(a__			,				a__      )
           except KeyError:
                self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.'''      )

      if with_manual_update:
           for file_path in with_manual_update:
                self._logger.warning(
                    F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''      )









'''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


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




def 						UpperCamelCase	(			a			)				->     Optional[MinHash]:






     '''simple docstring'''
     if len(a			) < MIN_NUM_TOKENS:
          return None
     __magic_name__						       =    MinHash(num_perm=a			)
     for token in set(a			):
          min_hash.update(token.encode()			)
     return min_hash




def 						UpperCamelCase	(			a			)				->     Set[str]:






     '''simple docstring'''
     return {t for t in NON_ALPHA.split(a			) if len(t.strip()			) > 0}






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Any			,				*,
     a__		:   float = 0.85			,				):
      __magic_name__						       =    duplication_jaccard_threshold
      __magic_name__						       =    NUM_PERM
      __magic_name__						       =    MinHashLSH(threshold=self._duplication_jaccard_threshold			,				num_perm=self._num_perm      )

      __magic_name__						       =    defaultdict(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple			,				a__		:   MinHash      ):
      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    []
      for base, duplicates in self._duplicate_clusters.items():
           __magic_name__						       =    [base] + list(a__      )
           # reformat the cluster to be a list of dict
           __magic_name__						       =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(a__      )
      return duplicate_clusters



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple      ):
      __magic_name__						       =    self.get_duplicate_clusters()
      with open(a__			,				'''w'''      ) as f:
           json.dump(a__			,				a__      )




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    element
     __magic_name__						       =    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 						UpperCamelCase	(			a			)				->     List[Any]:






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




def 						UpperCamelCase	(			a		,						a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     float:






     '''simple docstring'''
     __magic_name__						       =    get_tokens(a			)
     __magic_name__						       =    get_tokens(a			)
     return len(tokensa & tokensa			) / len(tokensa | tokensa			)


_lowerCAmelCase         =						None




def 						UpperCamelCase	(			a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    []
     for elementa in cluster:
          __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
          for elementa in extremes:
               __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
               if jaccard_similarity(a		,						a			) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               __magic_name__						       =    1
               extremes.append(a			)
     return extremes




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     global _shared_dataset
     __magic_name__						       =    dataset
     __magic_name__						       =    []
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a = 0.85			)				->     Tuple[Type[Dataset], List[List[Dict]]]:






     '''simple docstring'''
     __magic_name__						       =    make_duplicate_clusters(a		,						a			)
     __magic_name__						       =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     __magic_name__						       =    {}
     __magic_name__						       =    find_extremes(a		,						a		,						a			)
     for extremes in extremes_clusters:
          for element in extremes:
               __magic_name__						       =    element
     __magic_name__						       =    duplicate_indices - set(extreme_dict.keys()			)
     __magic_name__						       =    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:
               __magic_name__						       =    element['''base_index'''] in extreme_dict
               if element["is_extreme"]:
                    __magic_name__						       =    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






