






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      (		snake_case__		)							->  List[str]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__=5		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
						_SCREAMING_SNAKE_CASE     				=  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 	__lowerCamelCase      (		snake_case__	,snake_case__=5	,snake_case__=0.05		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""unit tests""", """test file""", """configuration file"""]
						_SCREAMING_SNAKE_CASE     				=  example["""content"""].splitlines()
						_SCREAMING_SNAKE_CASE     				=  0
						_SCREAMING_SNAKE_CASE     				=  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
						_SCREAMING_SNAKE_CASE     				=  example["""content"""].count("""\n"""		)
						_SCREAMING_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      (		snake_case__		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""def """, """class """, """for """, """while """]
						_SCREAMING_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      (		snake_case__	,snake_case__=4		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  example["""content"""].splitlines()
						_SCREAMING_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      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {}
						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 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  int:






						"""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 	__lowerCamelCase      (		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
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
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Optional[Any]									=							OpenAIGPTTokenizer
			__snake_case    :				Dict									=							OpenAIGPTTokenizerFast
			__snake_case    :				Optional[Any]									=							True
			__snake_case    :				Dict									=							False







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""merges_file"""]					)
									with open(self.vocab_file				,     """w"""					) as fp:
															fp.write(json.dumps(UpperCAmelCase_					)					)
									with open(self.merges_file				,     """w"""					) as fp:
															fp.write("""\n""".join(UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return "lower newer", "lower newer"







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OpenAIGPTTokenizer(self.vocab_file				,     self.merges_file					)

									_SCREAMING_SNAKE_CASE     				=  """lower"""
									_SCREAMING_SNAKE_CASE     				=  ["""low""", """er</w>"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + ["""<unk>"""]
									_SCREAMING_SNAKE_CASE     				=  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]=15					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)

																					# Simple input
																					_SCREAMING_SNAKE_CASE     				=  """This is a simple input"""
																					_SCREAMING_SNAKE_CASE     				=  ["""This is a simple input 1""", """This is a simple input 2"""]
																					_SCREAMING_SNAKE_CASE     				=  ("""This is a simple input""", """This is a pair""")
																					_SCREAMING_SNAKE_CASE     				=  [
																					    ("""This is a simple input 1""", """This is a simple input 2"""),
																					    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																					]

																					# Simple input tests
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass



@require_ftfy
@require_spacy
@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			pass

