






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:     Optional[Any]   					=					re.compile(R'''\s+''')




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						str:
       return {"hash": hashlib.mda(re.sub(__snake_case   ,      """"""   ,      example["""content"""] ).encode("""utf-8""" ) ).hexdigest()}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       _UpperCAmelCase							=    [len(__snake_case ) for line in example["""content"""].splitlines()]
       return {"line_mean": np.mean(__snake_case ), "line_max": max(__snake_case )}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       _UpperCAmelCase							=    np.mean([c.isalnum() for c in example["""content"""]] )
       return {"alpha_frac": alpha_frac}




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Dict:
       if example["hash"] in uniques:
              uniques.remove(example["""hash"""] )
              return True
       else:
              return False




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=5 )				->						Optional[int]:
       _UpperCAmelCase							=    ["""auto-generated""", """autogenerated""", """automatically generated"""]
       _UpperCAmelCase							=    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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=5   ,      __snake_case=0.05 )				->						Union[str, Any]:
       _UpperCAmelCase							=    ["""unit tests""", """test file""", """configuration file"""]
       _UpperCAmelCase							=    example["""content"""].splitlines()
       _UpperCAmelCase							=    0
       _UpperCAmelCase							=    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							=    example["""content"""].count("""\n""" )
       _UpperCAmelCase							=    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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    ["""def """, """class """, """for """, """while """]
       _UpperCAmelCase							=    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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=4 )				->						Any:
       _UpperCAmelCase							=    example["""content"""].splitlines()
       _UpperCAmelCase							=    0
       for line in lines:
              counter += line.lower().count("""=""" )
              if counter > minimum:
                     return {"has_few_assignments": False}
       return {"has_few_assignments": True}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						List[str]:
       _UpperCAmelCase							=    tokenizer(example["""content"""]   ,      truncation=__snake_case )["""input_ids"""]
       _UpperCAmelCase							=    len(example["""content"""] ) / len(__snake_case )
       return {"ratio": ratio}




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[Any]:
       _UpperCAmelCase							=    {}
       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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						int:
       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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[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
__a:     Optional[int]   					=					HfArgumentParser(PreprocessingArguments)
__a:     List[Any]   					=					parser.parse_args()
if args.num_workers is None:
  __a:     Tuple   					=					multiprocessing.cpu_count()
__a:     Tuple   					=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__a:     Optional[Any]   					=					time.time()
__a:     Dict   					=					load_dataset(args.dataset_name, split='''train''')
print(F"Time to load dataset: {time.time()-t_start:.2f}")

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

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

# Deduplicate data and apply heuristics
__a:     Optional[Any]   					=					time.time()
__a:     Optional[Any]   					=					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:     Optional[int]   					=					time.time()
  __a					,		__a:     str   					=					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:     int   					=					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:     Optional[Any]   					=					output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

__a:     List[Any]   					=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  __a:     Optional[int]   					=					str(data_dir / F"file-{file_number+1:012}.json")
  __a:     List[str]   					=					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 shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
       import torch

if is_tf_available():
       import tensorflow as tf




@require_vision
@require_torchvision
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



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



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

        A__		   =		[Image.fromarray(np.moveaxis(__lowerCamelCase,0,-1					)					) for x in image_inputs]

        return image_inputs



 def 							UpperCamelCase     (				self					):
        A__		   =		SamProcessor(image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        A__		   =		self.get_image_processor(do_normalize=__lowerCamelCase,padding_value=1.0					)

        A__		   =		SamProcessor.from_pretrained(self.tmpdirname,do_normalize=__lowerCamelCase,padding_value=1.0					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''np'''					)

        input_feat_extract.pop('''original_sizes'''					)  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('''reshaped_input_sizes'''					)  # pop original_sizes as it is popped in the processor

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




 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		[torch.ones((1, 3, 5, 5)					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		processor.post_process_masks(
            __lowerCamelCase,torch.tensor(__lowerCamelCase					),torch.tensor(__lowerCamelCase					)					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        # should also work with np
        A__		   =		[np.ones((1, 3, 5, 5)					)]
        A__		   =		processor.post_process_masks(__lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					)					)

        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		[[1, 0], [0, 1]]
        with self.assertRaises(__lowerCamelCase					):
               A__		   =		processor.post_process_masks(__lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					)					)




@require_vision
@require_tf
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



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



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

        A__		   =		[Image.fromarray(np.moveaxis(__lowerCamelCase,0,-1					)					) for x in image_inputs]

        return image_inputs



 def 							UpperCamelCase     (				self					):
        A__		   =		SamProcessor(image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        A__		   =		self.get_image_processor(do_normalize=__lowerCamelCase,padding_value=1.0					)

        A__		   =		SamProcessor.from_pretrained(self.tmpdirname,do_normalize=__lowerCamelCase,padding_value=1.0					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''np'''					)

        input_feat_extract.pop('''original_sizes'''					)  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('''reshaped_input_sizes'''					)  # pop reshaped_input_sizes as it is popped in the processor

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




 @require_tf
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		[tf.ones((1, 3, 5, 5)					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''tf'''					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		processor.post_process_masks(
            __lowerCamelCase,tf.convert_to_tensor(__lowerCamelCase					),tf.convert_to_tensor(__lowerCamelCase					),return_tensors='''tf''',)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        # should also work with np
        A__		   =		[np.ones((1, 3, 5, 5)					)]
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					),return_tensors='''tf'''					)

        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		[[1, 0], [0, 1]]
        with self.assertRaises(tf.errors.InvalidArgumentError					):
               A__		   =		processor.post_process_masks(
                   __lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					),return_tensors='''tf'''					)




@require_vision
@require_torchvision
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



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



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

        A__		   =		[Image.fromarray(np.moveaxis(__lowerCamelCase,0,-1					)					) for x in image_inputs]

        return image_inputs



 @is_pt_tf_cross_test
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		np.random.randint(0,2,size=(1, 3, 5, 5)					).astype(np.floataa					)
        A__		   =		[tf.convert_to_tensor(__lowerCamelCase					)]
        A__		   =		[torch.tensor(__lowerCamelCase					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''tf'''					)
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''pt'''					)

        self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()					)					)




 @is_pt_tf_cross_test
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''pt'''					)['''pixel_values'''].numpy()
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''pt'''					)['''pixel_values'''].numpy()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''tf'''					)['''pixel_values'''].numpy()
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''tf'''					)['''pixel_values'''].numpy()

        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase					)					)
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase					)					)
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase					)					)





