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







def      UpperCamelCase_						(					snake_case_       :       List[Any]      )					->     Dict:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Optional[Any]:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Any:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       int      )					->     int:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]						,		snake_case_       :       int=5      )					->     Any:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       str=5						,		snake_case_       :       int=0.0_5      )					->     str:






 '''simple docstring'''

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






 '''simple docstring'''

 __lowerCAmelCase											=						['def ', 'class ', 'for ', 'while ']
 __lowerCAmelCase											=						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_       :       Any						,		snake_case_       :       List[Any]=4      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						example['content'].splitlines()
 __lowerCAmelCase											=						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[Any]      )					->     int:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     str:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       Any						,		snake_case_       :       Dict						,		snake_case_       :       Union[str, Any]      )					->     Union[str, Any]:






 '''simple docstring'''

 if not check_uniques(_UpperCAmelCase						,		_UpperCAmelCase      ):
  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_       :       Tuple      )					->     Dict:






 '''simple docstring'''

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


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

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

# Run preprocessing
_A			:      List[str]					      =				time.time()
_A			:      Optional[int]					      =				ds.map(preprocess, num_proc=args.num_workers)
print(f'Time to preprocess dataset: {time.time()-t_start:.2f}')

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

# Deduplicate data and apply heuristics
_A			:      Optional[int]					      =				time.time()
_A			:      Tuple					      =				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			:      Union[str, Any]					      =				time.time()
 _A			:      str					      =				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			:      Tuple					      =				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			:      Optional[Any]					      =				output_dir / 'data'
data_dir.mkdir(exist_ok=True)

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













"""simple docstring"""




import qiskit





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  qiskit.Aer.get_backend('aer_simulator'					)

  A_  :       str										=  qiskit.QuantumCircuit(4 ,					2					)
  # encode inputs in qubits 0 and 1
  if bita == 1:
    qc_ha.x(0					)
  if bita == 1:
    qc_ha.x(1					)
  qc_ha.barrier()

  # use cnots to write XOR of the inputs on qubit2
  qc_ha.cx(0 ,					2					)
  qc_ha.cx(1 ,					2					)

  # use ccx / toffoli gate to write AND of the inputs on qubit3
  qc_ha.ccx(0 ,					1 ,					3					)
  qc_ha.barrier()

  # extract outputs
  qc_ha.measure(2 ,					0					)  # extract XOR value
  qc_ha.measure(3 ,					1					)  # extract AND value

  # Execute the circuit on the qasm simulator
  A_  :       Optional[Any]										=  qiskit.execute(_UpperCAmelCase ,					_UpperCAmelCase ,					shots=1000					)

  # Return the histogram data of the results of the experiment
  return job.result().get_counts(_UpperCAmelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						List[str]						=					half_adder(1, 1)
   print(F"Half Adder Output Qubit Counts: {counts}")