

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__      ( __A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str	,__A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :int=5		):


       """simple docstring"""




       __snake_case       	=							["""auto-generated""", """autogenerated""", """automatically generated"""]
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :int	,__A    :int=5	,__A    :str=0.05		):


       """simple docstring"""




       __snake_case       	=							["""unit tests""", """test file""", """configuration file"""]
       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       __snake_case       	=							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
       __snake_case       	=							example["""content"""].count("""\n"""		)
       __snake_case       	=							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__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							["""def """, """class """, """for """, """while """]
       __snake_case       	=							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__      ( __A    :Dict	,__A    :List[str]=4		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       __snake_case       	=							tokenizer(example["""content"""]	,truncation=__A		)["""input_ids"""]
       __snake_case       	=							len(example["""content"""]		) / len(__A		)
       return {"ratio": ratio}







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							{}
       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 						lowerCamelCase__      ( __A    :int	,__A    :str	,__A    :Dict		):


       """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 						lowerCamelCase__      ( __A    :str		):


       """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
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}')




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     '''▁'''

UpperCamelCase__		 =     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

UpperCamelCase__		 =     {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

UpperCamelCase__		 =     {
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

UpperCamelCase__		 =     ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

UpperCamelCase__		 =     {'''mustc''': MUSTC_LANGS}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							MAX_MODEL_INPUT_SIZES
       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]

       __SCREAMING_SNAKE_CASE				    =							[]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase="<s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=None      , _UpperCamelCase=None      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              __snake_case       	=							{} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  bos_token=_UpperCamelCase      , eos_token=_UpperCamelCase      , unk_token=_UpperCamelCase      , pad_token=_UpperCamelCase      , do_upper_case=_UpperCamelCase      , do_lower_case=_UpperCamelCase      , tgt_lang=_UpperCamelCase      , lang_codes=_UpperCamelCase      , sp_model_kwargs=self.sp_model_kwargs      , **_UpperCamelCase      , )
              __snake_case       	=							do_upper_case
              __snake_case       	=							do_lower_case

              __snake_case       	=							load_json(_UpperCamelCase   )
              __snake_case       	=							{v: k for k, v in self.encoder.items()}
              __snake_case       	=							spm_file
              __snake_case       	=							load_spm(_UpperCamelCase      , self.sp_model_kwargs   )

              if lang_codes is not None:
                     __snake_case       	=							lang_codes
                     __snake_case       	=							LANGUAGES[lang_codes]
                     __snake_case       	=							[F'<lang:{lang}>' for lang in self.langs]
                     __snake_case       	=							{lang: self.sp_model.PieceToId(F'<lang:{lang}>'   ) for lang in self.langs}

                     __snake_case       	=							self.lang_tokens
                     __snake_case       	=							tgt_lang if tgt_lang is not None else self.langs[0]

                     self.set_tgt_lang_special_tokens(self._tgt_lang   )
              else:
                     __snake_case       	=							{}


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return len(self.encoder   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self._tgt_lang


       @tgt_lang.setter
       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							new_tgt_lang
              self.set_tgt_lang_special_tokens(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							self.lang_code_to_id[tgt_lang]
              __snake_case       	=							[lang_code_id]


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              return self.sp_model.encode(_UpperCamelCase      , out_type=_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              return self.encoder.get(_UpperCamelCase      , self.encoder[self.unk_token]   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              return self.decoder.get(_UpperCamelCase      , self.unk_token   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							[]
              __snake_case       	=							""""""
              for token in tokens:
                     # make sure that special tokens are not decoded using sentencepiece model
                     if token in self.all_special_tokens:
                            __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
                            out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                            __snake_case       	=							[]
                     else:
                            current_sub_tokens.append(_UpperCamelCase   )
              __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
              out_string += decoded.upper() if self.do_upper_case else decoded
              return out_string.strip()


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=None   )      ->							List[int]:






              """simple docstring"""


              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + [self.eos_token_id]
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_UpperCamelCase      , token_ids_a=_UpperCamelCase      , already_has_special_tokens=_UpperCamelCase   )

              __snake_case       	=							[1] * len(self.prefix_tokens   )
              __snake_case       	=							[1]
              if token_ids_a is None:
                     return prefix_ones + ([0] * len(_UpperCamelCase   )) + suffix_ones
              return prefix_ones + ([0] * len(_UpperCamelCase   )) + ([0] * len(_UpperCamelCase   )) + suffix_ones


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.encoder.copy()
              vocab.update(self.added_tokens_encoder   )
              return vocab


       def __getstate__(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.__dict__.copy()
              __snake_case       	=							None
              return state


       def __setstate__(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							d

              # for backward compatibility
              if not hasattr(self      , """sp_model_kwargs"""   ):
                     __snake_case       	=							{}

              __snake_case       	=							load_spm(self.spm_file      , self.sp_model_kwargs   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


              __snake_case       	=							Path(_UpperCamelCase   )
              assert save_dir.is_dir(), F'{save_directory} should be a directory'
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
              )
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
              )

              save_json(self.encoder      , _UpperCamelCase   )

              if os.path.abspath(self.spm_file   ) != os.path.abspath(_UpperCamelCase   ) and os.path.isfile(self.spm_file   ):
                     copyfile(self.spm_file      , _UpperCamelCase   )
              elif not os.path.isfile(self.spm_file   ):
                     with open(_UpperCamelCase      , """wb"""   ) as fi:
                            __snake_case       	=							self.sp_model.serialized_model_proto()
                            fi.write(_UpperCamelCase   )

              return (str(_UpperCamelCase   ), str(_UpperCamelCase   ))







def 						lowerCamelCase__      ( __A    :str	,__A    :Dict[str, Any]		):


       """simple docstring"""




       __snake_case       	=							sentencepiece.SentencePieceProcessor(**__A		)
       spm.Load(str(__A		)		)
       return spm







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       with open(__A	,"""r"""		) as f:
              return json.load(__A		)







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :str		):


       """simple docstring"""




       with open(__A	,"""w"""		) as f:
              json.dump(__A	,__A	,indent=2		)

