






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


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict							)     ->  Optional[int]:
   return {"hash": hashlib.mda(re.sub(__lowerCAmelCase					,							""""""					,							example["""content"""]							).encode("""utf-8"""							)							).hexdigest()}


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Optional[Any]:
   __UpperCamelCase	:			Optional[int]					=					[len(__lowerCAmelCase							) for line in example["""content"""].splitlines()]
   return {"line_mean": np.mean(__lowerCAmelCase							), "line_max": max(__lowerCAmelCase							)}


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]							)     ->  Optional[Any]:
   __UpperCamelCase	:			Optional[Any]					=					np.mean([c.isalnum() for c in example["""content"""]]							)
   return {"alpha_frac": alpha_frac}


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Dict							)     ->  int:
   if example["hash"] in uniques:
      uniques.remove(example["""hash"""]							)
      return True
   else:
      return False


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Union[str, Any]=5							)     ->  Any:
   __UpperCamelCase	:			int					=					["""auto-generated""", """autogenerated""", """automatically generated"""]
   __UpperCamelCase	:			Optional[int]					=					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							:					List[str]					,							__lowerCAmelCase							:					Optional[int]=5					,							__lowerCAmelCase							:					Any=0.05							)     ->  Optional[Any]:
   __UpperCamelCase	:			List[str]					=					["""unit tests""", """test file""", """configuration file"""]
   __UpperCamelCase	:			int					=					example["""content"""].splitlines()
   __UpperCamelCase	:			int					=					0
   __UpperCamelCase	:			List[Any]					=					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
   __UpperCamelCase	:			List[Any]					=					example["""content"""].count("""\n"""							)
   __UpperCamelCase	:			List[str]					=					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							:					Union[str, Any]							)     ->  Optional[Any]:
   __UpperCamelCase	:			Dict					=					["""def """, """class """, """for """, """while """]
   __UpperCamelCase	:			Union[str, 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							:					int					,							__lowerCAmelCase							:					Optional[Any]=4							)     ->  int:
   __UpperCamelCase	:			Optional[int]					=					example["""content"""].splitlines()
   __UpperCamelCase	:			Dict					=					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							:					Dict							)     ->  str:
   __UpperCamelCase	:			List[Any]					=					tokenizer(example["""content"""]					,							truncation=__lowerCAmelCase							)["""input_ids"""]
   __UpperCamelCase	:			Optional[int]					=					len(example["""content"""]							) / len(__lowerCAmelCase							)
   return {"ratio": ratio}


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]							)     ->  Dict:
   __UpperCamelCase	:			Tuple					=					{}
   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							:					Union[str, Any]					,							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					List[str]							)     ->  int:
   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
UpperCamelCase    							=      HfArgumentParser(PreprocessingArguments)
UpperCamelCase    							=      parser.parse_args()
if args.num_workers is None:
       UpperCamelCase    							=      multiprocessing.cpu_count()
UpperCamelCase    							=      AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

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

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

  def __init__(    self						:	List[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	float = 0			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					row, column
     __UpperCamelCase	:			Tuple					=					[[default_value for c in range(lowerCamelCase__			)] for r in range(lowerCamelCase__			)]

  def __str__(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					f'Matrix consist of {self.row} rows and {self.column} columns\n'

     # Make string identifier
     __UpperCamelCase	:			Optional[int]					=					0
     for row_vector in self.array:
        for obj in row_vector:
           __UpperCamelCase	:			int					=					max(lowerCamelCase__   ,				len(str(lowerCamelCase__			)			)			)
     __UpperCamelCase	:			Union[str, Any]					=					f'%{max_element_length}s'

     # Make string and return
     def single_line(lowerCamelCase__						:	list[float]			) -> str:
        nonlocal string_format_identifier
        __UpperCamelCase	:			List[Any]					=					"""["""
        line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
        line += "]"
        return line

     s += "\n".join(single_line(lowerCamelCase__			) for row_vector in self.array			)
     return s

  def __repr__(    self						:	Any			):
     """simple docstring"""

     return str(self			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     if not (isinstance(lowerCamelCase__   ,				(list, tuple)			) and len(lowerCamelCase__			) == 2):
        return False
     elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
        return False
     else:
        return True

  def __getitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     return self.array[loc[0]][loc[1]]

  def __setitem__(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	tuple[int, int]   ,				lowerCamelCase__						:	float			):
     """simple docstring"""

     assert self.validate_indicies(lowerCamelCase__			)
     __UpperCamelCase	:			str					=					value

  def __add__(    self						:	int   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == another.row and self.column == another.column

     # Add
     __UpperCamelCase	:			Tuple					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Optional[int]					=					self[r, c] + another[r, c]
     return result

  def __neg__(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					Matrix(self.row   ,				self.column			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			Any					=					-self[r, c]
     return result

  def __sub__(    self						:	Tuple   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     return self + (-another)

  def __mul__(    self						:	Tuple   ,				lowerCamelCase__						:	int | float | Matrix			):
     """simple docstring"""

     if isinstance(lowerCamelCase__   ,				(int, float)			):  # Scalar multiplication
        __UpperCamelCase	:			List[str]					=					Matrix(self.row   ,				self.column			)
        for r in range(self.row			):
           for c in range(self.column			):
              __UpperCamelCase	:			Tuple					=					self[r, c] * another
        return result
     elif isinstance(lowerCamelCase__   ,				lowerCamelCase__			):  # Matrix multiplication
        assert self.column == another.row
        __UpperCamelCase	:			List[Any]					=					Matrix(self.row   ,				another.column			)
        for r in range(self.row			):
           for c in range(another.column			):
              for i in range(self.column			):
                 result[r, c] += self[r, i] * another[i, c]
        return result
     else:
        __UpperCamelCase	:			Any					=					f'Unsupported type given for another ({type(lowerCamelCase__			)})'
        raise TypeError(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					Matrix(self.column   ,				self.row			)
     for r in range(self.row			):
        for c in range(self.column			):
           __UpperCamelCase	:			str					=					self[r, c]
     return result

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Matrix   ,				lowerCamelCase__						:	Matrix			):
     """simple docstring"""

     assert isinstance(lowerCamelCase__   ,				lowerCamelCase__			) and isinstance(lowerCamelCase__   ,				lowerCamelCase__			)
     assert self.row == self.column == u.row == v.row  # u, v should be column vector
     assert u.column == v.column == 1  # u, v should be column vector

     # Calculate
     __UpperCamelCase	:			Optional[int]					=					v.transpose()
     __UpperCamelCase	:			Tuple					=					(v_t * self * u)[0, 0] + 1
     if numerator_factor == 0:
        return None  # It's not invertable
     return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


       def 				__lowerCamelCase     (							)     ->  None:
          # a^(-1)
          __UpperCamelCase	:			str					=					Matrix(3					,							3					,							0							)
          for i in range(3							):
             __UpperCamelCase	:			List[str]					=					1
          print(f'a^(-1) is {ainv}'							)
          # u, v
          __UpperCamelCase	:			Any					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Any					=					1, 2, -3
          __UpperCamelCase	:			int					=					Matrix(3					,							1					,							0							)
          __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					4, -2, 5
          print(f'u is {u}'							)
          print(f'v is {v}'							)
          print(f'uv^T is {u * v.transpose()}'							)
          # Sherman Morrison
          print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase					,							__lowerCAmelCase							)}'							)


       def 				__lowerCamelCase     (							)     ->  None:
          import doctest

          doctest.testmod()



       testa()



