
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			:     List[str]           =   re.compile(R'\s+')

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_,     A_					):






  '''simple docstring'''





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

def a__    (       A_,     A_=5					):






  '''simple docstring'''





  __magic_name__				     =       ['auto-generated', 'autogenerated', 'automatically generated']
  __magic_name__				     =       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 a__    (       A_,     A_=5,     A_=0.05					):






  '''simple docstring'''





  __magic_name__				     =       ['unit tests', 'test file', 'configuration file']
  __magic_name__				     =       example['content'].splitlines()
  __magic_name__				     =       0
  __magic_name__				     =       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
  __magic_name__				     =       example['content'].count("""\n"""					)
  __magic_name__				     =       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__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       ['def ', 'class ', 'for ', 'while ']
  __magic_name__				     =       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__    (       A_,     A_=4					):






  '''simple docstring'''





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

def a__    (       A_					):






  '''simple docstring'''





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

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       {}
  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 a__    (       A_,     A_,     A_					):






  '''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 a__    (       A_					):






  '''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
__lowerCAmelCase			:     List[Any]           =   HfArgumentParser(PreprocessingArguments)
__lowerCAmelCase			:     Dict           =   parser.parse_args()
if args.num_workers is None:
    __lowerCAmelCase			:     Any           =   multiprocessing.cpu_count()
__lowerCAmelCase			:     Tuple           =   AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

from typing import Any



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Any							=	num_of_nodes
          UpperCamelCase    :  list[list[int]]							=	[]
          UpperCamelCase    :  dict[int, int]							=	{}
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          self.m_edges.append([u_node, v_node, weight]       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		int:

          if self.m_component[u_node] == u_node:
             return u_node
          return self.find_component(self.m_component[u_node]       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		None:

          if self.m_component[u_node] != u_node:
             for k in self.m_component:
                UpperCamelCase    :  Dict							=	self.find_component(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:

          if component_size[u_node] <= component_size[v_node]:
             UpperCamelCase    :  Tuple							=	v_node
             component_size[v_node] += component_size[u_node]
             self.set_component(SCREAMING_SNAKE_CASE_       )

          elif component_size[u_node] >= component_size[v_node]:
             UpperCamelCase    :  Union[str, Any]							=	self.find_component(SCREAMING_SNAKE_CASE_       )
             component_size[u_node] += component_size[v_node]
             self.set_component(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		None:
          UpperCamelCase    :  int							=	[]
          UpperCamelCase    :  int							=	0

          UpperCamelCase    :  list[Any]							=	[-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes       ):
             self.m_component.update({node: node}       )
             component_size.append(1       )

          UpperCamelCase    :  List[str]							=	self.m_num_of_nodes

          while num_of_components > 1:
             for edge in self.m_edges:
                UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[Any]							=	edge

                UpperCamelCase    :  str							=	self.m_component[u]
                UpperCamelCase    :  Any							=	self.m_component[v]

                if u_component != v_component:

                   for component in (u_component, v_component):
                      if (
                          minimum_weight_edge[component] == -1
                          or minimum_weight_edge[component][2] > w
                      ):
                         UpperCamelCase    :  Any							=	[u, v, w]

             for edge in minimum_weight_edge:
                if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
                   UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  int							=	edge

                   UpperCamelCase    :  List[Any]							=	self.m_component[u]
                   UpperCamelCase    :  Tuple							=	self.m_component[v]

                   if u_component != v_component:
                      mst_weight += w
                      self.union(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
                      print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""       )
                      num_of_components -= 1

             UpperCamelCase    :  Optional[Any]							=	[-1] * self.m_num_of_nodes
          print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""       )






def 		UpperCamelCase	(   )	->     None:
   pass


if __name__ == "__main__":
    import doctest

    doctest.testmod()



