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






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [len(lowerCAmelCase__						) for line in example['''content'''].splitlines()]
			return {"line_mean": np.mean(lowerCAmelCase__						), "line_max": max(lowerCAmelCase__						)}






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  np.mean([c.isalnum() for c in example['''content''']]						)
			return {"alpha_frac": alpha_frac}






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if example["hash"] in uniques:
						uniques.remove(example['''hash''']						)
						return True
			else:
						return False






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=5						):
			lowerCamelCase_					=  ['''auto-generated''', '''autogenerated''', '''automatically generated''']
			lowerCamelCase_					=  example['''content'''].splitlines()
			for _, line in zip(range(lowerCAmelCase__						)	,lowerCAmelCase__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=5	,lowerCAmelCase__=0.05						):
			lowerCamelCase_					=  ['''unit tests''', '''test file''', '''configuration file''']
			lowerCamelCase_					=  example['''content'''].splitlines()
			lowerCamelCase_					=  0
			lowerCamelCase_					=  0
			# first test
			for _, line in zip(range(lowerCAmelCase__						)	,lowerCAmelCase__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			lowerCamelCase_					=  example['''content'''].count('''\n'''						)
			lowerCamelCase_					=  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  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  ['''def ''', '''class ''', '''for ''', '''while ''']
			lowerCamelCase_					=  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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=4						):
			lowerCamelCase_					=  example['''content'''].splitlines()
			lowerCamelCase_					=  0
			for line in lines:
						counter += line.lower().count('''='''						)
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  tokenizer(example['''content''']	,truncation=lowerCAmelCase__						)['''input_ids''']
			lowerCamelCase_					=  len(example['''content''']						) / len(lowerCAmelCase__						)
			return {"ratio": ratio}






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  {}
			results.update(get_hash(lowerCAmelCase__						)						)
			results.update(line_stats(lowerCAmelCase__						)						)
			results.update(alpha_stats(lowerCAmelCase__						)						)
			results.update(char_token_ratio(lowerCAmelCase__						)						)
			results.update(is_autogenerated(lowerCAmelCase__						)						)
			results.update(is_config_or_test(lowerCAmelCase__						)						)
			results.update(has_no_keywords(lowerCAmelCase__						)						)
			results.update(has_few_assignments(lowerCAmelCase__						)						)
			return results






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			if not check_uniques(lowerCAmelCase__	,lowerCAmelCase__						):
						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  lowercase						(	lowerCAmelCase__						):
			with open(lowerCAmelCase__	,'''rb'''						) as f_in:
						with gzip.open(str(lowerCAmelCase__						) + '''.gz'''	,'''wb'''	,compresslevel=6						) as f_out:
									shutil.copyfileobj(lowerCAmelCase__	,lowerCAmelCase__						)
			os.unlink(lowerCAmelCase__						)


# 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}")





"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  tempfile.mkdtemp()

							lowerCamelCase_					=  [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file''']   )
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8'''   ) as vocab_writer:
										vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )

							lowerCamelCase_					=  {
							    '''do_resize''': True,
							    '''size''': 20,
							    '''do_center_crop''': True,
							    '''crop_size''': 18,
							    '''do_normalize''': True,
							    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							}
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						UpperCAmelCase   )
							with open(self.image_processor_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										json.dump(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizer.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							shutil.rmtree(self.tmpdirname   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta   )]
							lowerCamelCase_					=  [Image.fromarray(np.moveaxis(UpperCAmelCase							,						0							,						-1   )   ) for x in image_inputs]
							return image_inputs




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()
							lowerCamelCase_					=  self.get_rust_tokenizer()
							lowerCamelCase_					=  self.get_image_processor()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_slow.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname							,						use_fast=UpperCAmelCase   )

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_fast.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname   )

							self.assertEqual(processor_slow.tokenizer.get_vocab()							,						tokenizer_slow.get_vocab()   )
							self.assertEqual(processor_fast.tokenizer.get_vocab()							,						tokenizer_fast.get_vocab()   )
							self.assertEqual(tokenizer_slow.get_vocab()							,						tokenizer_fast.get_vocab()   )
							self.assertIsInstance(processor_slow.tokenizer							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.tokenizer							,						UpperCAmelCase   )

							self.assertEqual(processor_slow.image_processor.to_json_string()							,						image_processor.to_json_string()   )
							self.assertEqual(processor_fast.image_processor.to_json_string()							,						image_processor.to_json_string()   )
							self.assertIsInstance(processor_slow.image_processor							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AlignProcessor(tokenizer=self.get_tokenizer()							,						image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

							lowerCamelCase_					=  self.get_tokenizer(bos_token='''(BOS)'''							,						eos_token='''(EOS)'''   )
							lowerCamelCase_					=  self.get_image_processor(do_normalize=UpperCAmelCase							,						padding_value=1.0   )

							lowerCamelCase_					=  AlignProcessor.from_pretrained(
							    self.tmpdirname							,						bos_token='''(BOS)'''							,						eos_token='''(EOS)'''							,						do_normalize=UpperCAmelCase							,						padding_value=1.0   )

							self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()   )
							self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase   )

							self.assertEqual(processor.image_processor.to_json_string()							,						image_processor_add_kwargs.to_json_string()   )
							self.assertIsInstance(processor.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  image_processor(UpperCAmelCase							,						return_tensors='''np'''   )
							lowerCamelCase_					=  processor(images=UpperCAmelCase							,						return_tensors='''np'''   )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()							,						input_processor[key].sum()							,						delta=1e-2   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''

							lowerCamelCase_					=  processor(text=UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding='''max_length'''							,						max_length=64   )

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]							,						encoded_processor[key]   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

							self.assertListEqual(list(inputs.keys()   )							,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']   )

							# test if it raises when no input is passed
							with pytest.raises(UpperCAmelCase   ):
										processor()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase_					=  processor.batch_decode(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.batch_decode(UpperCAmelCase   )

							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

							self.assertListEqual(list(inputs.keys()   )							,						processor.model_input_names   )



