






"""simple docstring"""



import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


A__       :				Optional[Any] 					=					logging.get_logger(__name__)

A__       :				Optional[Any] 					=					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A__       :				Union[str, Any] 					=					{
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

A__       :				Tuple 					=					{
    'Salesforce/codegen-350M-mono': 2_048,
}







class    lowercase__			(							__a   ):
			_UpperCAmelCase		:int   			= VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple   			= PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:List[str]   			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Dict   			= ["input_ids", "attention_mask"]
			_UpperCAmelCase		:Union[str, Any]   			= CodeGenTokenizer


			def __init__(      self     :  Union[str, Any]   ,       snake_case__     :  List[Any]=None   ,       snake_case__     :  str=None   ,       snake_case__     :  str=None   ,       snake_case__     :  Dict="<|endoftext|>"   ,       snake_case__     :  Tuple="<|endoftext|>"   ,       snake_case__     :  str="<|endoftext|>"   ,       snake_case__     :  List[Any]=False   ,       **snake_case__     :  List[str]   ,       ):
										super().__init__(
										    a_   ,       a_   ,       tokenizer_file=a_   ,       unk_token=a_   ,       bos_token=a_   ,       eos_token=a_   ,       add_prefix_space=a_   ,       **a_   ,       )

										if kwargs.pop("add_bos_token"   ,       a_					):
																	lowerCamelCase_       :							str									=kwargs.pop("name_or_path"   ,       ""					)
																	raise ValueError(
																	    "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
																	    "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
																	    F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
																	    F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
																	    "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
																	    " so that the fast tokenizer works correctly."					)

										lowerCamelCase_       :							Tuple									=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
										if pre_tok_state.get("add_prefix_space"   ,       a_					) != add_prefix_space:
																	lowerCamelCase_       :							Dict									=getattr(a_   ,       pre_tok_state.pop("type"					)					)
																	lowerCamelCase_       :							Dict									=add_prefix_space
																	lowerCamelCase_       :							str									=pre_tok_class(**a_					)

										lowerCamelCase_       :							List[Any]									=add_prefix_space


			def 	UpperCAmelCase__						(      self     :  Any   ,       *snake_case__     :  Any   ,       **snake_case__     :  int					):
										lowerCamelCase_       :							Optional[int]									=kwargs.get("is_split_into_words"   ,       a_					)
										assert self.add_prefix_space or not is_split_into_words, (
										    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     :  Optional[Any]   ,       *snake_case__     :  Any   ,       **snake_case__     :  List[str]					):
										lowerCamelCase_       :							Dict									=kwargs.get("is_split_into_words"   ,       a_					)

										assert self.add_prefix_space or not is_split_into_words, (
										    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]   ,       snake_case__     :  str   ,       snake_case__     :  Optional[str] = None					):
										lowerCamelCase_       :							List[Any]									=self._tokenizer.model.save(a_   ,       name=a_					)
										return tuple(a_					)


			def 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]   ,       snake_case__     :  bool = False   ,       snake_case__     :  bool = None   ,       snake_case__     :  Optional[List[str]] = None   ,       **snake_case__     :  List[str]   ,       ):
										lowerCamelCase_       :							Any									=super().decode(
										    token_ids=a_   ,       skip_special_tokens=a_   ,       clean_up_tokenization_spaces=a_   ,       **a_   ,       )

										if truncate_before_pattern is not None and len(a_					) > 0:
																	lowerCamelCase_       :							List[str]									=self.truncate(a_   ,       a_					)

										return decoded_text


			def 	UpperCAmelCase__						(      self     :  Dict   ,       snake_case__     :  Tuple   ,       snake_case__     :  Optional[Any]					):



										def find_re(snake_case__     :  Dict   ,       snake_case__     :  str   ,       snake_case__     :  Union[str, Any]					):
																	lowerCamelCase_       :							Any									=pattern.search(a_   ,       a_					)
																	return m.start() if m else -1

										lowerCamelCase_       :							Tuple									=[re.compile(a_   ,       re.MULTILINE					) for pattern in truncate_before_pattern]

										lowerCamelCase_       :							List[Any]									=list(re.finditer("^print"   ,       a_   ,       re.MULTILINE					)					)

										if len(a_					) > 1:
																	lowerCamelCase_       :							int									=completion[: prints[1].start()]

										lowerCamelCase_       :							List[str]									=list(re.finditer("^def"   ,       a_   ,       re.MULTILINE					)					)

										if len(a_					) > 1:
																	lowerCamelCase_       :							List[Any]									=completion[: defs[1].start()]

										lowerCamelCase_       :							int									=0

										lowerCamelCase_       :							List[Any]									=[
										    pos for pos in [find_re(a_   ,       a_   ,       a_					) for terminal in terminals] if pos != -1
										]

										if len(a_					) > 0:
																	return completion[: min(a_					)]
										else:
																	return completion









"""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}''')


