


import qiskit
def 				__lowerCamelCase						( UpperCAmelCase_						:		int = 2						):




      """simple docstring"""





      a						:Tuple 							=       qubits

      # Using Aer's simulator
      a						:Union[str, Any] 							=       qiskit.Aer.get_backend('''aer_simulator'''						)

      # Creating a Quantum Circuit acting on the q register
      a						:str 							=       qiskit.QuantumCircuit(UpperCAmelCase_ ,  UpperCAmelCase_						)

      # Adding a H gate on qubit 0 (now q0 in superposition)
      circuit.h(0						)

      for i in range(1 ,  UpperCAmelCase_						):
            # Adding CX (CNOT) gate
            circuit.cx(i - 1 ,  UpperCAmelCase_						)

      # Mapping the quantum measurement to the classical bits
      circuit.measure(list(range(UpperCAmelCase_						)						) ,  list(range(UpperCAmelCase_						)						)						)

      # Now measuring any one qubit would affect other qubits to collapse
      # their super position and have same state as the measured one.

      # Executing the circuit on the simulator
      a						:Union[str, Any] 							=       qiskit.execute(UpperCAmelCase_ ,  UpperCAmelCase_ ,  shots=1000						)

      return job.result().get_counts(UpperCAmelCase_						)


if __name__ == "__main__":
     print(F"""Total count for various states are: {quantum_entanglement(3)}""")







'''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


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





def 					UpperCAmelCase_		(__a      :    Any ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   np.mean([c.isalnum() for c in example['content']] )
   return {"alpha_frac": alpha_frac}





def 					UpperCAmelCase_		(__a      :    Optional[int] ,	__a      :    Any ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Union[str, Any]=5 ):






   """simple docstring"""

   _a				:	Optional[int]			=   ['auto-generated', 'autogenerated', 'automatically generated']
   _a				:	List[str]			=   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 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    Dict=5 ,	__a      :    Tuple=0.05 ):






   """simple docstring"""

   _a				:	Optional[int]			=   ['unit tests', 'test file', 'configuration file']
   _a				:	int			=   example['content'].splitlines()
   _a				:	int			=   0
   _a				:	Dict			=   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
   _a				:	int			=   example['content'].count('\n' )
   _a				:	int			=   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_		(__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	List[str]			=   ['def ', 'class ', 'for ', 'while ']
   _a				:	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_		(__a      :    int ,	__a      :    Any=4 ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Optional[Any]			=   tokenizer(example['content'] ,	truncation=__a )['input_ids']
   _a				:	Optional[int]			=   len(example['content'] ) / len(__a )
   return {"ratio": ratio}





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Dict			=   {}
   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 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    str ):






   """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 					UpperCAmelCase_		(__a      :    Union[str, 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
__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}''')



