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







def 						A__  (					__A	):







    '''simple docstring'''
    if len(__A	) < MIN_NUM_TOKENS:
        return None
    _lowerCamelCase     :		Union[str, Any] 						=						MinHash(num_perm=__A	)
    for token in set(__A	):
        min_hash.update(token.encode()	)
    return min_hash







def 						A__  (					__A	):







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




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Tuple    ,  *,
          _UpperCamelCase     :				float = 0.8_5    ,  )				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						duplication_jaccard_threshold
          _lowerCamelCase     :		List[str] 						=						NUM_PERM
          _lowerCamelCase     :		Any 						=						MinHashLSH(threshold=self._duplication_jaccard_threshold    ,  num_perm=self._num_perm)

          _lowerCamelCase     :		Tuple 						=						defaultdict(_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				MinHash)				->None:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						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     :				int)				->List[List[Dict]]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for base, duplicates in self._duplicate_clusters.items():
              _lowerCamelCase     :		Optional[int] 						=						[base] + list(_UpperCamelCase)
              # reformat the cluster to be a list of dict
              _lowerCamelCase     :		Dict 						=						[{"""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     :				List[str]    ,  _UpperCamelCase     :				Union[str, Any])				->None:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.get_duplicate_clusters()
          with open(_UpperCamelCase    ,  """w""") as f:
              json.dump(_UpperCamelCase    ,  _UpperCamelCase)







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						element
    _lowerCamelCase     :		Optional[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 						A__  (					__A	):







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







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						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 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						get_tokens(__A	)
    _lowerCamelCase     :		Any 						=						get_tokens(__A	)
    return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowerCAmelCase			:							Optional[int]     =None







def 						A__  (					__A   , __A	):







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







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    global _shared_dataset
    _lowerCamelCase     :		int 						=						dataset
    _lowerCamelCase     :		str 						=						[]
    _lowerCamelCase     :		Optional[Any] 						=						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 						A__  (					__A   , __A = 0.85	):







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





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCAmelCase			:							int     ={
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()







def 						A__  (					__A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		List[Any] 						=						finetuning_task.lower() if finetuning_task is not None else """"""
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
        _lowerCamelCase     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(__A   , __A   , __A	)

    # Save pytorch-model
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(__A   , __A	)
    _lowerCamelCase     :		Any 						=						os.path.join(__A   , __A	)
    print(F"""Save PyTorch model to {os.path.abspath(__A	)}"""	)
    torch.save(model.state_dict()   , __A	)
    print(F"""Save configuration file to {os.path.abspath(__A	)}"""	)
    with open(__A   , """w"""   , encoding="""utf-8"""	) as f:
        f.write(config.to_json_string()	)


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =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(
        "--xlnet_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained XLNet model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--finetuning_task",
        default=None,
        type=str,
        help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
    )
    lowerCAmelCase			:							Union[str, Any]     =parser.parse_args()
    print(args)

    convert_xlnet_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
    )




