


"""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


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


def 					lowercase      (    A_	)->				Optional[MinHash]:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				Set[str]:
  '''simple docstring'''







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






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			*,
        __UpperCAmelCase    :   float = 0.85    ,			):
      a						:		str											=      duplication_jaccard_threshold
      a						:		List[Any]											=      NUM_PERM
      a						:		Tuple											=      MinHashLSH(threshold=self._duplication_jaccard_threshold    ,			num_perm=self._num_perm)

      a						:		Dict											=      defaultdict(__UpperCAmelCase)






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






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   List[str]):
      a						:		Optional[Any]											=      self.get_duplicate_clusters()
      with open(__UpperCAmelCase    ,			"w") as f:
        json.dump(__UpperCAmelCase    ,			__UpperCAmelCase)






def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  a							,		a						:		int											=      element
  a						:		str											=      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 					lowercase      (    A_	)->				List[str]:
  '''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 					lowercase      (    A_  ,       A_	)->				Any:
  '''simple docstring'''







  a						:		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 					lowercase      (    A_  ,       A_	)->				float:
  '''simple docstring'''







  a						:		str											=      get_tokens(A_	)
  a						:		Optional[Any]											=      get_tokens(A_	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


__lowercase    =				None


def 					lowercase      (    A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Any											=      []
  for elementa in cluster:
    a						:		Tuple											=      _shared_dataset[elementa["base_index"]]["content"]
    for elementa in extremes:
      a						:		int											=      _shared_dataset[elementa["base_index"]]["content"]
      if jaccard_similarity(A_  ,       A_	) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      a						:		Any											=      1
      extremes.append(A_	)
  return extremes


def 					lowercase      (    A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  global _shared_dataset
  a						:		Optional[Any]											=      dataset
  a						:		Any											=      []
  a						:		int											=      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 					lowercase      (    A_  ,       A_ = 0.8_5	)->				Tuple[Type[Dataset], List[List[Dict]]]:
  '''simple docstring'''







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










"""simple docstring"""







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      TapasModel(config=A_	)
  else:
    raise ValueError(F'''Task {task} not supported.'''	)

  print(F'''Building PyTorch model from configuration: {config}'''	)
  # Load weights from tf checkpoint
  load_tf_weights_in_tapas(A_  ,       A_  ,       A_	)

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
  model.save_pretrained(A_	)

  # Save tokenizer files
  print(F'''Save tokenizer files to {pytorch_dump_path}'''	)
  a						:		Optional[int]											=      TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"  ,       model_max_length=512	)
  tokenizer.save_pretrained(A_	)

  print("Used relative position embeddings:"  ,       model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
 )
 parser.add_argument(
     """--reset_position_index_per_cell""",
     default=False,
     action="""store_true""",
     help="""Whether to use relative position embeddings or not. Defaults to True.""",
 )
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--tapas_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained TAPAS 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."""
 )
 __lowercase    =				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )






