
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


SCREAMING_SNAKE_CASE							:Optional[Any]							 =				logging.get_logger(__name__)





@dataclass
class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()  )}  )
	snake_case_					    =     field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}  )
	snake_case_					    =     field(
	    default=128			,			metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Overwrite the cached training and evaluation sets"}  )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.task_name.lower()





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "train"
	snake_case_					    =     "dev"
	snake_case_					    =     "test"





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     42






	def __init__(       self					: Union[str, Any]							,A					: GlueDataTrainingArguments							,A					: PreTrainedTokenizerBase							,A					: Optional[int] = None							,A					: Union[str, Split] = Split.train							,A					: Optional[str] = None							,):
					warnings.warn(
					    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
					    "library. You can have a look at this example script for pointers: "
					    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"							,A							,)
					__A   			= args
					__A   			= glue_processors[args.task_name]()
					__A   			= glue_output_modes[args.task_name]
					if isinstance(A							,A ):
									try:
													__A   			= Split[mode]
									except KeyError:
													raise KeyError("mode is not a valid split name" )
        # Load data features from cache or dataset file
					__A   			= os.path.join(
					    cache_dir if cache_dir is not None else args.data_dir							,f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''							,)
					__A   			= self.processor.get_labels()
					if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
					    "RobertaTokenizer",
					    "RobertaTokenizerFast",
					    "XLMRobertaTokenizer",
					    "BartTokenizer",
					    "BartTokenizerFast",
					):
									# HACK(label indices are swapped in RoBERTa pretrained model)
									__A			,							__A   			= label_list[2], label_list[1]
					__A   			= label_list

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					__A   			= cached_features_file + ".lock"
					with FileLock(A ):
									if os.path.exists(A ) and not args.overwrite_cache:
													__A   			= time.time()
													__A   			= torch.load(A )
													logger.info(
													    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''							,time.time() - start )
									else:
													logger.info(f'''Creating features from dataset file at {args.data_dir}''' )

													if mode == Split.dev:
																	__A   			= self.processor.get_dev_examples(args.data_dir )
													elif mode == Split.test:
																	__A   			= self.processor.get_test_examples(args.data_dir )
													else:
																	__A   			= self.processor.get_train_examples(args.data_dir )
													if limit_length is not None:
																	__A   			= examples[:limit_length]
													__A   			= glue_convert_examples_to_features(
													    A							,A							,max_length=args.max_seq_length							,label_list=A							,output_mode=self.output_mode							,)
													__A   			= time.time()
													torch.save(self.features							,A )
													# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
													logger.info(
													    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )






	def __len__(       self					: int ):
					return len(self.features )






	def __getitem__(       self					: Tuple							,A					: List[Any] ):
					return self.features[i]







	def 				UpperCamelCase_  (       self					: List[Any] ):
					return self.label_list

"""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


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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      Optional[MinHash]:
  if len(_lowerCamelCase	) < MIN_NUM_TOKENS:
    return None
  _lowerCAmelCase						:       Optional[Any]  			=     MinHash(num_perm=_lowerCamelCase	)
  for token in set(_lowerCamelCase	):
    min_hash.update(token.encode()	)
  return min_hash





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Set[str]:
  return {t for t in NON_ALPHA.split(_lowerCamelCase	) if len(t.strip()	) > 0}
class 						__A							:






     def __init__(		self				,     *,
         a__ = 0.8_5				,     ):
       _lowerCAmelCase						:       List[Any]  			=     duplication_jaccard_threshold
       _lowerCAmelCase						:       Union[str, Any]  			=     NUM_PERM
       _lowerCAmelCase						:       Optional[int]  			=     MinHashLSH(threshold=self._duplication_jaccard_threshold				,     num_perm=self._num_perm  )

       _lowerCAmelCase						:       Optional[int]  			=     defaultdict(a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     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  ):
       _lowerCAmelCase						:       int  			=     []
       for base, duplicates in self._duplicate_clusters.items():
         _lowerCAmelCase						:       List[str]  			=     [base] + list(a__  )
         # reformat the cluster to be a list of dict
         _lowerCAmelCase						:       List[Any]  			=     [{"""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__  ):
       _lowerCAmelCase						:       Dict  			=     self.get_duplicate_clusters()
       with open(a__				,     """w"""  ) as f:
         json.dump(a__				,     a__  )







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      Tuple:
  _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       Tuple  			=     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     (			_lowerCamelCase						:       Type[Dataset]	)       ->      Optional[Any]:
  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash			,ThreadedIterator(_lowerCamelCase			,max_queue_size=10000	)			,chunksize=100			,):
      if data is not None:
        yield data





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float	)       ->      List[str]:
  _lowerCAmelCase						:       Optional[Any]  			=     DuplicationIndex(duplication_jaccard_threshold=_lowerCamelCase	)

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

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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      float:
  _lowerCAmelCase						:       Any  			=     get_tokens(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     get_tokens(_lowerCamelCase	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


_a      :		str           =   None





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]	)       ->      Dict:
  _lowerCAmelCase						:       int  			=     []
  for elementa in cluster:
    _lowerCAmelCase						:       Dict  			=     _shared_dataset[elementa["""base_index"""]]["""content"""]
    for elementa in extremes:
      _lowerCAmelCase						:       Any  			=     _shared_dataset[elementa["""base_index"""]]["""content"""]
      if jaccard_similarity(_lowerCamelCase			,_lowerCamelCase	) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      _lowerCAmelCase						:       Any  			=     1
      extremes.append(_lowerCamelCase	)
  return extremes





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Any	)       ->      str:
  global _shared_dataset
  _lowerCAmelCase						:       Tuple  			=     dataset
  _lowerCAmelCase						:       Optional[Any]  			=     []
  _lowerCAmelCase						:       Optional[Any]  			=     partial(_find_cluster_extremes_shared			,jaccard_threshold=_lowerCamelCase	)
  with mp.Pool() as pool:
    for extremes in tqdm(
        pool.imap_unordered(
            _lowerCamelCase			,_lowerCamelCase			,)			,total=len(_lowerCamelCase	)			,):
      extremes_list.append(_lowerCamelCase	)
  return extremes_list






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float = 0.85	)       ->      Tuple[Type[Dataset], List[List[Dict]]]:
  _lowerCAmelCase						:       Tuple  			=     make_duplicate_clusters(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
  _lowerCAmelCase						:       Optional[int]  			=     {}
  _lowerCAmelCase						:       Tuple  			=     find_extremes(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
  for extremes in extremes_clusters:
    for element in extremes:
      _lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       List[Any]  			=     duplicate_indices - set(extreme_dict.keys()	)
  _lowerCAmelCase						:       List[Any]  			=     dataset.filter(lambda _lowerCamelCase			,_lowerCamelCase	: idx not in remove_indices			,with_indices=_lowerCamelCase	)

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      _lowerCAmelCase						:       Tuple  			=     element["""base_index"""] in extreme_dict
      if element["is_extreme"]:
        _lowerCAmelCase						:       Dict  			=     extreme_dict[element["""base_index"""]]["""copies"""]

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

  return ds_filter, duplicate_clusters




