

import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


lowercase_             =     re.compile(R'\s+')
def   a   (    A__			:     str  )					->   Optional[int]:





      """simple docstring"""



      return {"hash": hashlib.mda(re.sub(A__   ,     ''   ,     example['content']  ).encode('utf-8'  )  ).hexdigest()}
def   a   (    A__			:     int  )					->   Any:





      """simple docstring"""



      _lowercase			 =[len(A__  ) for line in example['content'].splitlines()]
      return {"line_mean": np.mean(A__  ), "line_max": max(A__  )}
def   a   (    A__			:     Dict  )					->   int:





      """simple docstring"""



      _lowercase			 =np.mean([c.isalnum() for c in example['content']]  )
      return {"alpha_frac": alpha_frac}
def   a   (    A__			:     Optional[int]   ,     A__			:     Dict  )					->   Union[str, Any]:





      """simple docstring"""



      if example["hash"] in uniques:
            uniques.remove(example['hash']  )
            return True
      else:
            return False
def   a   (    A__			:     List[Any]   ,     A__			:     List[str]=5  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =['auto-generated', 'autogenerated', 'automatically generated']
      _lowercase			 =example['content'].splitlines()
      for _, line in zip(range(A__  )   ,     A__  ):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"autogenerated": True}
      else:
            return {"autogenerated": False}
def   a   (    A__			:     str   ,     A__			:     Any=5   ,     A__			:     Dict=0.05  )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =['unit tests', 'test file', 'configuration file']
      _lowercase			 =example['content'].splitlines()
      _lowercase			 =0
      _lowercase			 =0
      # first test
      for _, line in zip(range(A__  )   ,     A__  ):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"config_or_test": True}
    # second test
      _lowercase			 =example['content'].count('\n'  )
      _lowercase			 =int(coeff * nlines  )
      for line in lines:
            count_config += line.lower().count('config'  )
            count_test += line.lower().count('test'  )
            if count_config > threshold or count_test > threshold:
                  return {"config_or_test": True}
      return {"config_or_test": False}
def   a   (    A__			:     List[Any]  )					->   Dict:





      """simple docstring"""



      _lowercase			 =['def ', 'class ', 'for ', 'while ']
      _lowercase			 =example['content'].splitlines()
      for line in lines:
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"has_no_keywords": False}
      return {"has_no_keywords": True}
def   a   (    A__			:     Tuple   ,     A__			:     str=4  )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =example['content'].splitlines()
      _lowercase			 =0
      for line in lines:
            counter += line.lower().count('='  )
            if counter > minimum:
                  return {"has_few_assignments": False}
      return {"has_few_assignments": True}
def   a   (    A__			:     Optional[int]  )					->   Union[str, Any]:





      """simple docstring"""



      _lowercase			 =tokenizer(example['content']   ,     truncation=A__  )['input_ids']
      _lowercase			 =len(example['content']  ) / len(A__  )
      return {"ratio": ratio}
def   a   (    A__			:     List[Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 ={}
      results.update(get_hash(A__  )  )
      results.update(line_stats(A__  )  )
      results.update(alpha_stats(A__  )  )
      results.update(char_token_ratio(A__  )  )
      results.update(is_autogenerated(A__  )  )
      results.update(is_config_or_test(A__  )  )
      results.update(has_no_keywords(A__  )  )
      results.update(has_few_assignments(A__  )  )
      return results
def   a   (    A__			:     Tuple   ,     A__			:     Union[str, Any]   ,     A__			:     List[Any]  )					->   List[Any]:





      """simple docstring"""



      if not check_uniques(A__   ,     A__  ):
            return False
      elif example["autogenerated"]:
            return False
      elif example["line_max"] > args.line_max:
            return False
      elif example["line_mean"] > args.line_mean:
            return False
      elif example["alpha_frac"] < args.alpha_frac:
            return False
      elif example["ratio"] < args.min_token_ratio:
            return False
      elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
            return False
      elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
            return False
      elif example["has_few_assignments"]:
            return False
      else:
            return True
def   a   (    A__			:     int  )					->   Any:





      """simple docstring"""



      with open(A__   ,     'rb'  ) as f_in:
            with gzip.open(str(A__  ) + '.gz'   ,     'wb'   ,     compresslevel=6  ) as f_out:
                  shutil.copyfileobj(A__   ,     A__  )
      os.unlink(A__  )


# Settings
lowercase_             =     HfArgumentParser(PreprocessingArguments)
lowercase_             =     parser.parse_args()
if args.num_workers is None:
     lowercase_             =     multiprocessing.cpu_count()
lowercase_             =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
lowercase_             =     time.time()
lowercase_             =     load_dataset(args.dataset_name, split='train')
print(f"Time to load dataset: {time.time()-t_start:.2f}")

# Run preprocessing
lowercase_             =     time.time()
lowercase_             =     ds.map(preprocess, num_proc=args.num_workers)
print(f"Time to preprocess dataset: {time.time()-t_start:.2f}")

# Deduplicate hashes
lowercase_             =     set(ds.unique('hash'))
lowercase_             =     len(uniques) / len(ds)
print(f"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
lowercase_             =     time.time()
lowercase_             =     ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(f"Time to filter dataset: {time.time()-t_start:.2f}")
print(f"Size of filtered dataset: {len(ds_filter)}")

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
     lowercase_             =     time.time()
     lowercase_       ,		lowercase_             =     deduplicate_dataset(ds_filter, args.jaccard_threshold)
     print(f"Time to deduplicate dataset: {time.time()-t_start:.2f}")
     print(f"Size of deduplicate dataset: {len(ds_filter)}")

# Save data in batches of samples_per_file
lowercase_             =     Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
     with open(output_dir / 'duplicate_clusters.json', 'w') as f:
          json.dump(duplicate_clusters, f)

lowercase_             =     output_dir / 'data'
data_dir.mkdir(exist_ok=True)

lowercase_             =     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     lowercase_             =     str(data_dir / f"file-{file_number+1:012}.json")
     lowercase_             =     min(len(ds_filter), index + args.samples_per_file)
     ds_filter.select(list(range(index, end_index))).to_json(file_path)
     compress_file(file_path)







print(f"Time to save dataset: {time.time()-t_start:.2f}")





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   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =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}'''  )
      _lowercase			 =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,
  )


