




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    :		Tuple			   =       re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowerCamelCase    :		Any			   =       10
lowerCamelCase    :		Union[str, Any]			   =       256

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]					):


						'''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


						'''simple docstring'''



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







class   A:



	'''simple docstring'''


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

							"""simple docstring"""






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

							lowerCamelCase_							=   defaultdict(A_  )

	def     a__			(    self :  Optional[int]						,							A_ :  Tuple						,							A_ :  MinHash  )       ->     None:

							"""simple docstring"""






							lowerCamelCase_							=   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 :  Union[str, Any]  )       ->     List[List[Dict]]:

							"""simple docstring"""






							lowerCamelCase_							=   []
							for base, duplicates in self._duplicate_clusters.items():
													lowerCamelCase_							=   [base] + list(A_  )
													# reformat the cluster to be a list of dict
													lowerCamelCase_							=   [{'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 :  Optional[Any]						,							A_ :  Optional[Any]  )       ->     None:

							"""simple docstring"""






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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict					):


						'''simple docstring'''



						lowerCamelCase_					,    lowerCamelCase_							=   element
						lowerCamelCase_							=   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     :	Type[Dataset]					):


						'''simple docstring'''



						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash     ,							ThreadedIterator(lowercase     ,							max_queue_size=1_00_00					)     ,							chunksize=1_00     ,							):
																		if data is not None:
																								yield data

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Type[Dataset]     ,							lowercase     :	float					):


						'''simple docstring'''



						lowerCamelCase_							=   DuplicationIndex(duplication_jaccard_threshold=lowercase					)

						for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowercase					)					)     ,							max_queue_size=1_00					)					):
												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     :	str     ,							lowercase     :	str					):


						'''simple docstring'''



						lowerCamelCase_							=   get_tokens(lowercase					)
						lowerCamelCase_							=   get_tokens(lowercase					)
						return len(tokensa & tokensa					) / len(tokensa | tokensa					)


lowerCamelCase    :		Optional[int]			   =       None

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[int]					):


						'''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	int     ,							lowercase     :	List[str]					):


						'''simple docstring'''



						global _shared_dataset
						lowerCamelCase_							=   dataset
						lowerCamelCase_							=   []
						lowerCamelCase_							=   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     :	Type[Dataset]     ,							lowercase     :	float = 0.85					):


						'''simple docstring'''



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









import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase    :		Union[str, Any]			   =       logging.get_logger(__name__)


lowerCamelCase    :		str			   =       {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	str     ,							lowercase     :	int     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



      if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
            raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()					)}."""					)

      if tokenizer_name is None:
            lowerCamelCase_							=   TOKENIZER_CLASSES
      else:
            lowerCamelCase_							=   {tokenizer_name: getattr(lowercase     ,							tokenizer_name + 'Fast'					)}

      logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""					)

      for tokenizer_name in tokenizer_names:
            lowerCamelCase_							=   TOKENIZER_CLASSES[tokenizer_name]

            lowerCamelCase_							=   True
            if checkpoint_name is None:
                  lowerCamelCase_							=   list(tokenizer_class.max_model_input_sizes.keys()					)
            else:
                  lowerCamelCase_							=   [checkpoint_name]

            logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""					)

            for checkpoint in checkpoint_names:
                  logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""					)

                  # Load tokenizer
                  lowerCamelCase_							=   tokenizer_class.from_pretrained(lowercase     ,							force_download=lowercase					)

                  # Save fast tokenizer
                  logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""					)

                  # For organization names we create sub-directories
                  if "/" in checkpoint:
                        lowerCamelCase_					,    lowerCamelCase_							=   checkpoint.split('/'					)
                        lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                  elif add_prefix:
                        lowerCamelCase_							=   checkpoint
                        lowerCamelCase_							=   dump_path
                  else:
                        lowerCamelCase_							=   None
                        lowerCamelCase_							=   dump_path

                  logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""					)

                  if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()					)[0]:
                        lowerCamelCase_							=   list(tokenizer.pretrained_vocab_files_map.values()					)[0][checkpoint]
                        lowerCamelCase_							=   file_path.split(lowercase					)[-1][0]
                        if next_char == "/":
                              lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                              lowerCamelCase_							=   None

                        logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""					)

                  lowerCamelCase_							=   tokenizer.save_pretrained(
                      lowercase     ,							legacy_format=lowercase     ,							filename_prefix=lowercase					)
                  logger.info(f"""=> File names {file_names}"""					)

                  for file_name in file_names:
                        if not file_name.endswith('tokenizer.json'					):
                              os.remove(lowercase					)
                              logger.info(f"""=> removing {file_name}"""					)


if __name__ == "__main__":
 lowerCamelCase    :		Tuple			   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
 )
 parser.add_argument(
     "--tokenizer_name",
     default=None,
     type=str,
     help=(
         F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
         "download and convert all the checkpoints from AWS."
     ),
 )
 parser.add_argument(
     "--checkpoint_name",
     default=None,
     type=str,
     help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
 )
 parser.add_argument(
     "--force_download",
     action="store_true",
     help="Re-download checkpoints.",
 )
 lowerCamelCase    :		Union[str, Any]			   =       parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)



