





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


lowerCAmelCase_						   =    re.compile(r"""\s+""")


def     lowerCamelCase_	(      lowerCAmelCase:	List[str]				)->		Dict:
  return {"hash": hashlib.mda(re.sub(lowerCAmelCase    ,							''    ,							example['content']				).encode('utf-8'				)				).hexdigest()}


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]				)->		Optional[int]:
  _snake_case   :				Optional[int]														=	[len(lowerCAmelCase				) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(lowerCAmelCase				), "line_max": max(lowerCAmelCase				)}


def     lowerCamelCase_	(      lowerCAmelCase:	List[str]				)->		int:
  _snake_case   :				str														=	np.mean([c.isalnum() for c in example['content']]				)
  return {"alpha_frac": alpha_frac}


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	Union[str, Any]				)->		int:
  if example["hash"] in uniques:
    uniques.remove(example['hash']				)
    return True
  else:
    return False


def     lowerCamelCase_	(      lowerCAmelCase:	str    ,							lowerCAmelCase:	str=5				)->		List[str]:
  _snake_case   :				Dict														=	['auto-generated', 'autogenerated', 'automatically generated']
  _snake_case   :				Dict														=	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     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	Any=5    ,							lowerCAmelCase:	List[Any]=0.0_5				)->		List[str]:
  _snake_case   :				Tuple														=	['unit tests', 'test file', 'configuration file']
  _snake_case   :				Any														=	example['content'].splitlines()
  _snake_case   :				List[str]														=	0
  _snake_case   :				Tuple														=	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
  _snake_case   :				List[str]														=	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     lowerCamelCase_	(      lowerCAmelCase:	int				)->		Dict:
  _snake_case   :				str														=	['def ', 'class ', 'for ', 'while ']
  _snake_case   :				List[Any]														=	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     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict=4				)->		int:
  _snake_case   :				Tuple														=	example['content'].splitlines()
  _snake_case   :				Optional[int]														=	0
  for line in lines:
    counter += line.lower().count('='				)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}


def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		List[str]:
  _snake_case   :				int														=	tokenizer(example['content']    ,							truncation=lowerCAmelCase				)['input_ids']
  _snake_case   :				int														=	len(example['content']				) / len(lowerCAmelCase				)
  return {"ratio": ratio}


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		Dict:
  _snake_case   :				Optional[int]														=	{}
  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     lowerCamelCase_	(      lowerCAmelCase:	Dict    ,							lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Optional[Any]				)->		Dict:
  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     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		Any:
  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
lowerCAmelCase_						   =    HfArgumentParser(PreprocessingArguments)
lowerCAmelCase_						   =    parser.parse_args()
if args.num_workers is None:
      lowerCAmelCase_						   =    multiprocessing.cpu_count()
lowerCAmelCase_						   =    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

lowerCAmelCase_						   =    output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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











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


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Tuple      					="""audio-spectrogram-transformer"""







  def __init__(      self      :					List[Any]      ,				UpperCamelCase      :					Union[str, Any]=7_68      ,				UpperCamelCase      :					int=12      ,				UpperCamelCase      :					str=12      ,				UpperCamelCase      :					Tuple=30_72      ,				UpperCamelCase      :					Optional[Any]="gelu"      ,				UpperCamelCase      :					Any=0.0      ,				UpperCamelCase      :					Dict=0.0      ,				UpperCamelCase      :					List[Any]=0.02      ,				UpperCamelCase      :					Dict=1e-1_2      ,				UpperCamelCase      :					str=16      ,				UpperCamelCase      :					List[Any]=True      ,				UpperCamelCase      :					Any=10      ,				UpperCamelCase      :					Optional[int]=10      ,				UpperCamelCase      :					int=10_24      ,				UpperCamelCase      :					Optional[Any]=1_28      ,				**UpperCamelCase      :					Optional[Any]      ,				):


    '''simple docstring'''





    super().__init__(**UpperCamelCase							)

    _snake_case   :				Tuple														=	hidden_size
    _snake_case   :				str														=	num_hidden_layers
    _snake_case   :				Optional[Any]														=	num_attention_heads
    _snake_case   :				Optional[Any]														=	intermediate_size
    _snake_case   :				Optional[Any]														=	hidden_act
    _snake_case   :				List[str]														=	hidden_dropout_prob
    _snake_case   :				Union[str, Any]														=	attention_probs_dropout_prob
    _snake_case   :				Any														=	initializer_range
    _snake_case   :				List[str]														=	layer_norm_eps
    _snake_case   :				int														=	patch_size
    _snake_case   :				List[str]														=	qkv_bias
    _snake_case   :				int														=	frequency_stride
    _snake_case   :				List[Any]														=	time_stride
    _snake_case   :				List[Any]														=	max_length
    _snake_case   :				List[str]														=	num_mel_bins




