






"""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


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



def   a    (     __UpperCAmelCase      :    List[Any]  )    ->							List[Any]:
						return {"hash": hashlib.mda(re.sub(__UpperCAmelCase      ,   """"""      ,   example["""content"""]  ).encode("""utf-8"""  )  ).hexdigest()}



def   a    (     __UpperCAmelCase      :    Optional[int]  )    ->							int:
						__magic_name__:	Tuple							=       [len(__UpperCAmelCase  ) for line in example["""content"""].splitlines()]
						return {"line_mean": np.mean(__UpperCAmelCase  ), "line_max": max(__UpperCAmelCase  )}



def   a    (     __UpperCAmelCase      :    List[str]  )    ->							Union[str, Any]:
						__magic_name__:	Optional[int]							=       np.mean([c.isalnum() for c in example["""content"""]]  )
						return {"alpha_frac": alpha_frac}



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    str  )    ->							str:
						if example["hash"] in uniques:
												uniques.remove(example["""hash"""]  )
												return True
						else:
												return False



def   a    (     __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Union[str, Any]=5  )    ->							Optional[Any]:
						__magic_name__:	List[Any]							=       ["""auto-generated""", """autogenerated""", """automatically generated"""]
						__magic_name__:	str							=       example["""content"""].splitlines()
						for _, line in zip(range(__UpperCAmelCase  )      ,   __UpperCAmelCase  ):
												for keyword in keywords:
																		if keyword in line.lower():
																								return {"autogenerated": True}
						else:
												return {"autogenerated": False}



def   a    (     __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    List[str]=5      ,   __UpperCAmelCase      :    Optional[Any]=0.05  )    ->							List[str]:
						__magic_name__:	str							=       ["""unit tests""", """test file""", """configuration file"""]
						__magic_name__:	Tuple							=       example["""content"""].splitlines()
						__magic_name__:	int							=       0
						__magic_name__:	Any							=       0
						# first test
						for _, line in zip(range(__UpperCAmelCase  )      ,   __UpperCAmelCase  ):
												for keyword in keywords:
																		if keyword in line.lower():
																								return {"config_or_test": True}
    # second test
						__magic_name__:	Dict							=       example["""content"""].count("""\n"""  )
						__magic_name__:	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   a    (     __UpperCAmelCase      :    List[str]  )    ->							Dict:
						__magic_name__:	Dict							=       ["""def """, """class """, """for """, """while """]
						__magic_name__:	Tuple							=       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   a    (     __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Optional[int]=4  )    ->							Optional[int]:
						__magic_name__:	Optional[Any]							=       example["""content"""].splitlines()
						__magic_name__:	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   a    (     __UpperCAmelCase      :    Tuple  )    ->							List[Any]:
						__magic_name__:	Optional[int]							=       tokenizer(example["""content"""]      ,   truncation=__UpperCAmelCase  )["""input_ids"""]
						__magic_name__:	List[str]							=       len(example["""content"""]  ) / len(__UpperCAmelCase  )
						return {"ratio": ratio}



def   a    (     __UpperCAmelCase      :    Union[str, Any]  )    ->							List[str]:
						__magic_name__:	Any							=       {}
						results.update(get_hash(__UpperCAmelCase  )  )
						results.update(line_stats(__UpperCAmelCase  )  )
						results.update(alpha_stats(__UpperCAmelCase  )  )
						results.update(char_token_ratio(__UpperCAmelCase  )  )
						results.update(is_autogenerated(__UpperCAmelCase  )  )
						results.update(is_config_or_test(__UpperCAmelCase  )  )
						results.update(has_no_keywords(__UpperCAmelCase  )  )
						results.update(has_few_assignments(__UpperCAmelCase  )  )
						return results



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Any  )    ->							Dict:
						if not check_uniques(__UpperCAmelCase      ,   __UpperCAmelCase  ):
												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   a    (     __UpperCAmelCase      :    str  )    ->							str:
						with open(__UpperCAmelCase      ,   """rb"""  ) as f_in:
												with gzip.open(str(__UpperCAmelCase  ) + """.gz"""      ,   """wb"""      ,   compresslevel=6  ) as f_out:
																		shutil.copyfileobj(__UpperCAmelCase      ,   __UpperCAmelCase  )
						os.unlink(__UpperCAmelCase  )


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

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

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

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

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

__lowerCamelCase							=  output_dir / 'data'
data_dir.mkdir(exist_ok=True)

__lowerCamelCase							=  time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
							__lowerCamelCase							=  str(data_dir / f'''file-{file_number+1:012}.json''')
							__lowerCamelCase							=  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 CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor






@require_vision
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Tuple														=       tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase     :      List[str]														=       ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase     :      Tuple														=       dict(zip(A	,      range(len(A		)		)		)		)
           _UpperCAmelCase     :      Any														=       ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase     :      Optional[Any]														=       {"unk_token": "<unk>"}

           _UpperCAmelCase     :      List[Any]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      int														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["merges_file"]		)
           with open(self.vocab_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write(json.dumps(A		) + "\n"		)
           with open(self.merges_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write("\n".join(A		)		)

           _UpperCAmelCase     :      List[Any]														=       {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase     :      Tuple														=       os.path.join(self.tmpdirname	,      A		)
           with open(self.image_processor_file	,      "w"	,      encoding="utf-8"		) as fp:
                json.dump(A	,      A		)



      def 		snake_case_   (	self	:      int	,      **A	:      Any		):
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      List[str]	,      **A	:      Tuple		):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Any	,      **A	:      Optional[Any]		):
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Dict		):
           shutil.rmtree(self.tmpdirname		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       [np.random.randint(2_5_5	,      size=(3, 3_0, 4_0_0)	,      dtype=np.uinta		)]

           _UpperCAmelCase     :      str														=       [Image.fromarray(np.moveaxis(A	,      0	,      -1		)		) for x in image_inputs]

           return image_inputs



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       self.get_tokenizer()
           _UpperCAmelCase     :      int														=       self.get_rust_tokenizer()
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()

           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_slow.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A		)

           _UpperCAmelCase     :      Any														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_fast.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      int														=       CLIPSegProcessor.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	,      A		)
           self.assertIsInstance(processor_fast.tokenizer	,      A		)

           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	,      A		)
           self.assertIsInstance(processor_fast.image_processor	,      A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()		)
           processor.save_pretrained(self.tmpdirname		)

           _UpperCAmelCase     :      List[str]														=       self.get_tokenizer(bos_token="(BOS)"	,      eos_token="(EOS)"		)
           _UpperCAmelCase     :      int														=       self.get_image_processor(do_normalize=A	,      padding_value=1.0		)

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor.from_pretrained(
               self.tmpdirname	,      bos_token="(BOS)"	,      eos_token="(EOS)"	,      do_normalize=A	,      padding_value=1.0		)

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

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



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Dict														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      int														=       image_processor(A	,      return_tensors="np"		)
           _UpperCAmelCase     :      int														=       processor(images=A	,      return_tensors="np"		)

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



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.get_image_processor()
           _UpperCAmelCase     :      Dict														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Tuple														=       "lower newer"

           _UpperCAmelCase     :      Any														=       processor(text=A		)

           _UpperCAmelCase     :      int														=       tokenizer(A		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()
           _UpperCAmelCase     :      Union[str, Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Tuple														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       "lower newer"
           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      str														=       processor(text=A	,      images=A		)

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

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      str														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()
           _UpperCAmelCase     :      int														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      Any														=       processor(images=A	,      visual_prompt=A		)

           self.assertListEqual(list(inputs.keys()		)	,      ["pixel_values", "conditional_pixel_values"]		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      str														=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase     :      Union[str, Any]														=       processor.batch_decode(A		)
           _UpperCAmelCase     :      List[Any]														=       tokenizer.batch_decode(A		)

           self.assertListEqual(A	,      A		)
