



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 		UpperCamelCase	(   snake_case__       :    List[Any]	)	->     List[str]:
   return {"hash": hashlib.mda(re.sub(snake_case__       ,     ''       ,     example['content']	).encode('utf-8'	)	).hexdigest()}
def 		UpperCamelCase	(   snake_case__       :    Optional[int]	)	->     Dict:
   UpperCamelCase    :  int							=	[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]	)	->     Optional[int]:
   UpperCamelCase    :  Any							=	np.mean([c.isalnum() for c in example['content']]	)
   return {"alpha_frac": alpha_frac}
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    str	)	->     Dict:
   if example["hash"] in uniques:
      uniques.remove(example['hash']	)
      return True
   else:
      return False
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Optional[int]=5	)	->     int:
   UpperCamelCase    :  List[Any]							=	['auto-generated', 'autogenerated', 'automatically generated']
   UpperCamelCase    :  Any							=	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__       :    List[str]=5       ,     snake_case__       :    Any=0.05	)	->     Any:
   UpperCamelCase    :  Optional[Any]							=	['unit tests', 'test file', 'configuration file']
   UpperCamelCase    :  Union[str, Any]							=	example['content'].splitlines()
   UpperCamelCase    :  Dict							=	0
   UpperCamelCase    :  List[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
   UpperCamelCase    :  str							=	example['content'].count('\n'	)
   UpperCamelCase    :  Union[str, Any]							=	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__       :    int	)	->     str:
   UpperCamelCase    :  Union[str, Any]							=	['def ', 'class ', 'for ', 'while ']
   UpperCamelCase    :  List[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[Any]       ,     snake_case__       :    Union[str, Any]=4	)	->     List[str]:
   UpperCamelCase    :  str							=	example['content'].splitlines()
   UpperCamelCase    :  Optional[int]							=	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__       :    Dict	)	->     Optional[Any]:
   UpperCamelCase    :  Tuple							=	tokenizer(example['content']       ,     truncation=snake_case__	)['input_ids']
   UpperCamelCase    :  Any							=	len(example['content']	) / len(snake_case__	)
   return {"ratio": ratio}
def 		UpperCamelCase	(   snake_case__       :    List[Any]	)	->     List[Any]:
   UpperCamelCase    :  List[str]							=	{}
   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__       :    Union[str, Any]       ,     snake_case__       :    Optional[Any]       ,     snake_case__       :    Optional[Any]	)	->     List[str]:
   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__       :    List[str]	)	->     Dict:
   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
__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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	max_position_embeddings
          UpperCamelCase    :  Tuple							=	hidden_size
          UpperCamelCase    :  Union[str, Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Tuple							=	intermediate_size
          UpperCamelCase    :  List[str]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  Tuple							=	type_vocab_size
          UpperCamelCase    :  Any							=	layer_norm_eps

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



