




"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


A_						=				logging.get_logger(__name__)


A_						=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
A_						=				{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

A_						=				{
    '''RUCAIBox/mvp''': 10_24,
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					["input_ids", "attention_mask"]
 lowercase__ 					=					MvpTokenizer


 def __init__(				self:		Optional[int],      a_:		Dict=None,      a_:		Any=None,      a_:		List[str]=None,      a_:		Any="replace",      a_:		str="<s>",      a_:		int="</s>",      a_:		Optional[int]="</s>",      a_:		int="<s>",      a_:		int="<unk>",      a_:		Dict="<pad>",      a_:		List[Any]="<mask>",      a_:		int=False,      a_:		Tuple=True,      **a_:		Any,      ):




   '''simple docstring'''



   super().__init__(
       a_,      a_,      tokenizer_file=a_,      errors=a_,      bos_token=a_,      eos_token=a_,      sep_token=a_,      cls_token=a_,      unk_token=a_,      pad_token=a_,      mask_token=a_,      add_prefix_space=a_,      trim_offsets=a_,      **a_,      )

   _snake_case							:		Optional[int]								=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
   if pre_tok_state.get("""add_prefix_space""",      a_		) != add_prefix_space:
     _snake_case							:		str								=  getattr(a_,      pre_tok_state.pop("""type"""		)		)
     _snake_case							:		List[Any]								=  add_prefix_space
     _snake_case							:		Dict								=  pre_tok_class(**a_		)

   _snake_case							:		Dict								=  add_prefix_space

   # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
   _snake_case							:		Dict								=  """post_processor"""
   _snake_case							:		Union[str, Any]								=  getattr(self.backend_tokenizer,      a_,      a_		)
   if tokenizer_component_instance:
     _snake_case							:		List[Any]								=  json.loads(tokenizer_component_instance.__getstate__()		)

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
       _snake_case							:		Optional[int]								=  tuple(state["""sep"""]		)
     if "cls" in state:
       _snake_case							:		Tuple								=  tuple(state["""cls"""]		)

     _snake_case							:		List[str]								=  False

     if state.get("""add_prefix_space""",      a_		) != add_prefix_space:
       _snake_case							:		List[Any]								=  add_prefix_space
       _snake_case							:		int								=  True

     if state.get("""trim_offsets""",      a_		) != trim_offsets:
       _snake_case							:		Tuple								=  trim_offsets
       _snake_case							:		List[Any]								=  True

     if changes_to_apply:
       _snake_case							:		str								=  getattr(a_,      state.pop("""type"""		)		)
       _snake_case							:		Optional[Any]								=  component_class(**a_		)
       setattr(self.backend_tokenizer,      a_,      a_		)


 @property
 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   if self._mask_token is None:
     if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""		)
     return None
   return str(self._mask_token		)


 @mask_token.setter
 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  AddedToken(a_,      lstrip=a_,      rstrip=a_		) if isinstance(a_,      a_		) else value
   _snake_case							:		Tuple								=  value


 def  UpperCamelCase_   (				self:		Optional[int],      *a_:		Union[str, Any],      **a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Any								=  kwargs.get("""is_split_into_words""",      a_		)

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
         """to use it with pretokenized inputs."""		)

   return super()._batch_encode_plus(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		Union[str, Any],      *a_:		Optional[Any],      **a_:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  kwargs.get("""is_split_into_words""",      a_		)

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
         """to use it with pretokenized inputs."""		)

   return super()._encode_plus(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[int],      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		str								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


 def  UpperCamelCase_   (				self:		Tuple,      a_:		Optional[Any],      a_:		str=None		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
     return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[int],      a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		int								=  [self.sep_token_id]
   _snake_case							:		List[str]								=  [self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep		) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]








"""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_						=				re.compile(r'''\s+''')


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Dict    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					List[str]=5    ):
  """simple docstring"""

  _snake_case							:		Any								=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
  _snake_case							:		Tuple								=  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 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Union[str, Any]=5		,   snake_case__							:					Any=0.05    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  ["""unit tests""", """test file""", """configuration file"""]
  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		Dict								=  0
  _snake_case							:		str								=  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
  _snake_case							:		Optional[int]								=  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 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  ["""def """, """class """, """for """, """while """]
  _snake_case							:		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__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]=4    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		str								=  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[str]    ):
  """simple docstring"""

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


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  {}
  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 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					List[Any]		,   snake_case__							:					List[str]    ):
  """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 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """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
A_						=				HfArgumentParser(PreprocessingArguments)
A_						=				parser.parse_args()
if args.num_workers is None:
     A_						=				multiprocessing.cpu_count()
A_						=				AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
A_						=				time.time()
A_						=				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_						=				time.time()
     A_	,    A_						=				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_						=				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_						=				output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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


