




"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_						=				logging.get_logger(__name__)

A_						=				{
    '''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
    '''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"markuplm"


 def __init__(				self:		Optional[Any],      a_:		List[str]=30_522,      a_:		List[str]=768,      a_:		Optional[int]=12,      a_:		List[str]=12,      a_:		Optional[int]=3_072,      a_:		str="gelu",      a_:		str=0.1,      a_:		int=0.1,      a_:		Dict=512,      a_:		List[Any]=2,      a_:		List[Any]=0.02,      a_:		Optional[Any]=1E-12,      a_:		Any=0,      a_:		Union[str, Any]=0,      a_:		int=2,      a_:		Union[str, Any]=256,      a_:		Tuple=1_024,      a_:		str=216,      a_:		str=1_001,      a_:		str=32,      a_:		Optional[Any]=50,      a_:		List[str]="absolute",      a_:		Dict=True,      a_:		int=None,      **a_:		Any,      ):




   '''simple docstring'''



   super().__init__(
       pad_token_id=a_,      bos_token_id=a_,      eos_token_id=a_,      **a_,      )
   _snake_case							:		Optional[Any]								=  vocab_size
   _snake_case							:		Optional[Any]								=  hidden_size
   _snake_case							:		Union[str, Any]								=  num_hidden_layers
   _snake_case							:		Tuple								=  num_attention_heads
   _snake_case							:		Optional[Any]								=  hidden_act
   _snake_case							:		List[Any]								=  intermediate_size
   _snake_case							:		List[str]								=  hidden_dropout_prob
   _snake_case							:		int								=  attention_probs_dropout_prob
   _snake_case							:		Tuple								=  max_position_embeddings
   _snake_case							:		str								=  type_vocab_size
   _snake_case							:		Any								=  initializer_range
   _snake_case							:		Any								=  layer_norm_eps
   _snake_case							:		str								=  position_embedding_type
   _snake_case							:		List[Any]								=  use_cache
   _snake_case							:		Any								=  classifier_dropout
   # additional properties
   _snake_case							:		Dict								=  max_depth
   _snake_case							:		str								=  max_xpath_tag_unit_embeddings
   _snake_case							:		str								=  max_xpath_subs_unit_embeddings
   _snake_case							:		Union[str, Any]								=  tag_pad_id
   _snake_case							:		int								=  subs_pad_id
   _snake_case							:		List[Any]								=  xpath_unit_hidden_size








"""simple docstring"""


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


A_						=				re.compile(r'''\s+''')


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Dict    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					List[str]=5    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Union[str, Any]=5		,   snake_case__							:					Any=0.05    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  ["""unit tests""", """test file""", """configuration file"""]
  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		Dict								=  0
  _snake_case							:		str								=  0
  # first test
  for _, line in zip(range(snake_case__    )		,   snake_case__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  _snake_case							:		Optional[int]								=  example["""content"""].count("""\n"""    )
  _snake_case							:		Tuple								=  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 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  ["""def """, """class """, """for """, """while """]
  _snake_case							:		str								=  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 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]=4    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  tokenizer(example["""content"""]		,   truncation=snake_case__    )["""input_ids"""]
  _snake_case							:		Optional[Any]								=  len(example["""content"""]    ) / len(snake_case__    )
  return {"ratio": ratio}


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  {}
  results.update(get_hash(snake_case__    )    )
  results.update(line_stats(snake_case__    )    )
  results.update(alpha_stats(snake_case__    )    )
  results.update(char_token_ratio(snake_case__    )    )
  results.update(is_autogenerated(snake_case__    )    )
  results.update(is_config_or_test(snake_case__    )    )
  results.update(has_no_keywords(snake_case__    )    )
  results.update(has_few_assignments(snake_case__    )    )
  return results


def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					List[Any]		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  if not check_uniques(snake_case__		,   snake_case__    ):
    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 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

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


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

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

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

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

# Deduplicate data and apply heuristics
A_						=				time.time()
A_						=				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:
     A_						=				time.time()
     A_	,    A_						=				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
A_						=				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)

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

A_						=				time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     A_						=				str(data_dir / F'''file-{file_number+1:012}.json''')
     A_						=				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}''')


