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


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




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




def      _UpperCamelCase      (	snake_case__							)  ->      List[str]:
							__UpperCAmelCase  :	str   			=      [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__							)  ->      Tuple:
							__UpperCAmelCase  :	List[Any]   			=      np.mean([c.isalnum() for c in example["content"]]							)
							return {"alpha_frac": alpha_frac}




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


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




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




def      _UpperCamelCase      (	snake_case__							)  ->      str:
							__UpperCAmelCase  :	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__,			snake_case__,			snake_case__							)  ->      Union[str, Any]:


							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[Any]:


							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
_snake_case								=  HfArgumentParser(PreprocessingArguments)
_snake_case								=  parser.parse_args()
if args.num_workers is None:
	_snake_case								=  multiprocessing.cpu_count()
_snake_case								=  AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

_snake_case								=  output_dir / "data"
data_dir.mkdir(exist_ok=True)

_snake_case								=  time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
	_snake_case								=  str(data_dir / F'file-{file_number+1:012}.json')
	_snake_case								=  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 os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case :		int							      =  logging.get_logger(__name__)
snake_case :		List[str]							      =  {"vocab_file": "spiece.model"}

snake_case :		List[str]							      =  {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

snake_case :		Tuple							      =  {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

snake_case :		List[str]							      =  "▁"





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







			def __init__(				self						,   _a						,   _a=True						,   _a=True						,   _a=False						,   _a="[CLS]"						,   _a="[SEP]"						,   _a="<unk>"						,   _a="[SEP]"						,   _a="<pad>"						,   _a="[CLS]"						,   _a="[MASK]"						,   _a = None						,   **_a						,   ):
				# Mask token behave like a normal word, i.e. include the space before it and
				# is included in the raw text, there should be a match in a non-normalized sentence.
				__magic_name__		:		str	   =     (
				    AddedToken(_a						,   lstrip=_a						,   rstrip=_a						,   normalized=_a		)
				    if isinstance(_a						,   _a		)
				    else mask_token
				)

				__magic_name__		:		Union[str, Any]	   =     {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=_a						,   remove_space=_a						,   keep_accents=_a						,   bos_token=_a						,   eos_token=_a						,   unk_token=_a						,   sep_token=_a						,   pad_token=_a						,   cls_token=_a						,   mask_token=_a						,   sp_model_kwargs=self.sp_model_kwargs						,   **_a						,   )

				__magic_name__		:		Dict	   =     do_lower_case
				__magic_name__		:		Tuple	   =     remove_space
				__magic_name__		:		Union[str, Any]	   =     keep_accents
				__magic_name__		:		Tuple	   =     vocab_file

				__magic_name__		:		int	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(_a		)







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.sp_model		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     {self.convert_ids_to_tokens(_a		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab







			def __getstate__(				self		):
				__magic_name__		:		List[str]	   =     self.__dict__.copy()
				__magic_name__		:		Any	   =     None
				return state







			def __setstate__(				self						,   _a		):
				__magic_name__		:		Union[str, Any]	   =     d

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

				__magic_name__		:		Optional[int]	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(self.vocab_file		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if self.remove_space:
					__magic_name__		:		List[Any]	   =     " ".join(inputs.strip().split()		)
				else:
					__magic_name__		:		str	   =     inputs
				__magic_name__		:		int	   =     outputs.replace("``"						,   "\""		).replace("''"						,   "\""		)

				if not self.keep_accents:
					__magic_name__		:		str	   =     unicodedata.normalize("NFKD"						,   _a		)
					__magic_name__		:		Tuple	   =     "".join([c for c in outputs if not unicodedata.combining(_a		)]		)
				if self.do_lower_case:
					__magic_name__		:		int	   =     outputs.lower()

				return outputs







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     self.preprocess_text(_a		)
				__magic_name__		:		Dict	   =     self.sp_model.encode(_a						,   out_type=_a		)
				__magic_name__		:		Any	   =     []
				for piece in pieces:
					if len(_a		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
						__magic_name__		:		Optional[Any]	   =     self.sp_model.EncodeAsPieces(piece[:-1].replace(_a						,   ""		)		)
						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
							if len(cur_pieces[0]		) == 1:
								__magic_name__		:		List[str]	   =     cur_pieces[1:]
							else:
								__magic_name__		:		Optional[int]	   =     cur_pieces[0][1:]
						cur_pieces.append(piece[-1]		)
						new_pieces.extend(_a		)
					else:
						new_pieces.append(_a		)

				return new_pieces







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.sp_model.PieceToId(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.sp_model.IdToPiece(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Any	   =     []
				__magic_name__		:		Union[str, Any]	   =     ""
				__magic_name__		:		int	   =     False
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						if not prev_is_special:
							out_string += " "
						out_string += self.sp_model.decode(_a		) + token
						__magic_name__		:		List[Any]	   =     True
						__magic_name__		:		Optional[int]	   =     []
					else:
						current_sub_tokens.append(_a		)
						__magic_name__		:		Optional[Any]	   =     False
				out_string += self.sp_model.decode(_a		)
				return out_string.strip()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		List[str]	   =     [self.sep_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.cls_token_id]
				if token_ids_a is None:
					return cls + token_ids_a + sep
				return cls + token_ids_a + sep + token_ids_a + sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = False		):

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

				if token_ids_a is not None:
					return [1] + ([0] * len(_a		)) + [1] + ([0] * len(_a		)) + [1]
				return [1] + ([0] * len(_a		)) + [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Optional[int]	   =     [self.sep_token_id]
				__magic_name__		:		Union[str, Any]	   =     [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		) * [0] + len(token_ids_a + sep		) * [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				if not os.path.isdir(_a		):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
					return
				__magic_name__		:		List[str]	   =     os.path.join(
				    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(_a		) and os.path.isfile(self.vocab_file		):
					copyfile(self.vocab_file						,   _a		)
				elif not os.path.isfile(self.vocab_file		):
					with open(_a						,   "wb"		) as fi:
						__magic_name__		:		Union[str, Any]	   =     self.sp_model.serialized_model_proto()
						fi.write(_a		)

				return (out_vocab_file,)
