





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


snake_case__					:							int													=     re.compile(r'\s+')



def _a				(	lowerCamelCase:   Tuple )	->						List[str]:







      '''simple docstring'''






      return {"hash": hashlib.mda(re.sub(lowerCamelCase					,				''''''					,				example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}



def _a				(	lowerCamelCase:   int )	->						Tuple:







      '''simple docstring'''






      __A          =				[len(lowerCamelCase ) for line in example['''content'''].splitlines()]
      return {"line_mean": np.mean(lowerCamelCase ), "line_max": max(lowerCamelCase )}



def _a				(	lowerCamelCase:   Optional[int] )	->						Optional[Any]:







      '''simple docstring'''






      __A          =				np.mean([c.isalnum() for c in example['''content''']] )
      return {"alpha_frac": alpha_frac}



def _a				(	lowerCamelCase:   Dict					,				lowerCamelCase:   Optional[Any] )	->						Any:







      '''simple docstring'''






      if example["hash"] in uniques:
            uniques.remove(example['''hash'''] )
            return True
      else:
            return False



def _a				(	lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   List[str]=5 )	->						Any:







      '''simple docstring'''






      __A          =				['''auto-generated''', '''autogenerated''', '''automatically generated''']
      __A          =				example['''content'''].splitlines()
      for _, line in zip(range(lowerCamelCase )					,				lowerCamelCase ):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"autogenerated": True}
      else:
            return {"autogenerated": False}



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   List[str]=5					,				lowerCamelCase:   str=0.05 )	->						int:







      '''simple docstring'''






      __A          =				['''unit tests''', '''test file''', '''configuration file''']
      __A          =				example['''content'''].splitlines()
      __A          =				0
      __A          =				0
      # first test
      for _, line in zip(range(lowerCamelCase )					,				lowerCamelCase ):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"config_or_test": True}
    # second test
      __A          =				example['''content'''].count('''\n''' )
      __A          =				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				(	lowerCamelCase:   List[str] )	->						int:







      '''simple docstring'''






      __A          =				['''def ''', '''class ''', '''for ''', '''while ''']
      __A          =				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				(	lowerCamelCase:   Any					,				lowerCamelCase:   Tuple=4 )	->						Optional[int]:







      '''simple docstring'''






      __A          =				example['''content'''].splitlines()
      __A          =				0
      for line in lines:
            counter += line.lower().count('''=''' )
            if counter > minimum:
                  return {"has_few_assignments": False}
      return {"has_few_assignments": True}



def _a				(	lowerCamelCase:   Any )	->						int:







      '''simple docstring'''






      __A          =				tokenizer(example['''content''']					,				truncation=lowerCamelCase )['''input_ids''']
      __A          =				len(example['''content'''] ) / len(lowerCamelCase )
      return {"ratio": ratio}



def _a				(	lowerCamelCase:   Optional[Any] )	->						str:







      '''simple docstring'''






      __A          =				{}
      results.update(get_hash(lowerCamelCase ) )
      results.update(line_stats(lowerCamelCase ) )
      results.update(alpha_stats(lowerCamelCase ) )
      results.update(char_token_ratio(lowerCamelCase ) )
      results.update(is_autogenerated(lowerCamelCase ) )
      results.update(is_config_or_test(lowerCamelCase ) )
      results.update(has_no_keywords(lowerCamelCase ) )
      results.update(has_few_assignments(lowerCamelCase ) )
      return results



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      if not check_uniques(lowerCamelCase					,				lowerCamelCase ):
            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				(	lowerCamelCase:   int )	->						int:







      '''simple docstring'''






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


# Settings
snake_case__					:							Any													=     HfArgumentParser(PreprocessingArguments)
snake_case__					:							Dict													=     parser.parse_args()
if args.num_workers is None:
       snake_case__					:							Optional[int]													=     multiprocessing.cpu_count()
snake_case__					:							int													=     AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
snake_case__					:							Tuple													=     time.time()
snake_case__					:							Optional[int]													=     load_dataset(args.dataset_name, split='train')
print(f'Time to load dataset: {time.time()-t_start:.2f}')

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

# Deduplicate hashes
snake_case__					:							int													=     set(ds.unique('hash'))
snake_case__					:							Union[str, Any]													=     len(uniques) / len(ds)
print(f'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
snake_case__					:							Optional[Any]													=     time.time()
snake_case__					:							Optional[Any]													=     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:
       snake_case__					:							Union[str, Any]													=     time.time()
       snake_case__						,    snake_case__					:							Tuple													=     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
snake_case__					:							int													=     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)

snake_case__					:							str													=     output_dir / 'data'
data_dir.mkdir(exist_ok=True)

snake_case__					:							int													=     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
       snake_case__					:							str													=     str(data_dir / f'file-{file_number+1:012}.json')
       snake_case__					:							Union[str, Any]													=     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

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     argparse.ArgumentParser(
           description=(
               'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
               ' Distillation'
           )
       )
       parser.add_argument('--model_type', default='bert', choices=['bert'])
       parser.add_argument('--model_name', default='bert-base-uncased', type=str)
       parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
       parser.add_argument('--vocab_transform', action='store_true')
       snake_case__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

       for w in ["word_embeddings", "position_embeddings"]:
              snake_case__					:							Tuple													=     state_dict[f'{prefix}.embeddings.{w}.weight']
       for w in ["weight", "bias"]:
              snake_case__					:							Optional[Any]													=     state_dict[f'{prefix}.embeddings.LayerNorm.{w}']

       snake_case__					:							int													=     0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     snake_case__					:							Union[str, Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
                     ]
                     snake_case__					:							Dict													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
                     ]

                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
                     ]
                     snake_case__					:							Optional[int]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
                     ]

                     snake_case__					:							Optional[Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
                     ]
                     snake_case__					:							List[str]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
                     ]
              std_idx += 1

       snake_case__					:							Optional[int]													=     state_dict['cls.predictions.decoder.weight']
       snake_case__					:							str													=     state_dict['cls.predictions.bias']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     snake_case__					:							int													=     state_dict[f'cls.predictions.transform.dense.{w}']
                     snake_case__					:							Optional[int]													=     state_dict[f'cls.predictions.transform.LayerNorm.{w}']

       print(f'N layers selected for distillation: {std_idx}')
       print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')

       print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
       torch.save(compressed_sd, args.dump_checkpoint)





