





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DonutImageProcessor

class  _UpperCAmelCase  (   unittest.TestCase		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=7					,      snake_case_=3					,      snake_case_=1_8					,      snake_case_=3_0					,      snake_case_=4_0_0					,      snake_case_=True					,      snake_case_=None					,      snake_case_=True					,      snake_case_=False					,      snake_case_=True					,      snake_case_=True					,      snake_case_=[0.5, 0.5, 0.5]					,      snake_case_=[0.5, 0.5, 0.5]					,      ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  parent
        A_  :       Tuple										=  batch_size
        A_  :       List[Any]										=  num_channels
        A_  :       Union[str, Any]										=  image_size
        A_  :       Union[str, Any]										=  min_resolution
        A_  :       Optional[Any]										=  max_resolution
        A_  :       Optional[int]										=  do_resize
        A_  :       Optional[int]										=  size if size is not None else {'height': 1_8, 'width': 2_0}
        A_  :       Optional[Any]										=  do_thumbnail
        A_  :       Optional[Any]										=  do_align_axis
        A_  :       int										=  do_pad
        A_  :       Tuple										=  do_normalize
        A_  :       int										=  image_mean
        A_  :       Optional[int]										=  image_std

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }


@require_torch
@require_vision
class  _UpperCAmelCase  (   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : List[Any]	    =   DonutImageProcessor if is_vision_available() else None

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  DonutImageProcessingTester(self  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       str										=  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(snake_case_					,      'do_resize'  )  )
        self.assertTrue(hasattr(snake_case_					,      'size'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_thumbnail'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_align_long_axis'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_pad'  )  )
        self.assertTrue(hasattr(snake_case_					,      'do_normalize'  )  )
        self.assertTrue(hasattr(snake_case_					,      'image_mean'  )  )
        self.assertTrue(hasattr(snake_case_					,      'image_std'  )  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size					,      {'height': 1_8, 'width': 2_0}  )

        A_  :       Any										=  self.image_processing_class.from_dict(self.image_processor_dict					,      size=4_2  )
        self.assertEqual(image_processor.size					,      {'height': 4_2, 'width': 4_2}  )

        # Previous config had dimensions in (width, height) order
        A_  :       Tuple										=  self.image_processing_class.from_dict(self.image_processor_dict					,      size=(4_2, 8_4)  )
        self.assertEqual(image_processor.size					,      {'height': 8_4, 'width': 4_2}  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @is_flaky()
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        A_  :       Any										=  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_					,      Image.Image  )

        # Test not batched input
        A_  :       Optional[int]										=  image_processing(image_inputs[0]					,      return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape					,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )					,      )

        # Test batched
        A_  :       Optional[Any]										=  image_processing(snake_case_					,      return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )					,      )

      @is_flaky()
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[Any]										=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        A_  :       List[Any]										=  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=snake_case_					,      numpify=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_					,      np.ndarray  )

        # Test not batched input
        A_  :       str										=  image_processing(image_inputs[0]					,      return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape					,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )					,      )

        # Test batched
        A_  :       Union[str, Any]										=  image_processing(snake_case_					,      return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )					,      )

      @is_flaky()
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        A_  :       Union[str, Any]										=  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=snake_case_					,      torchify=snake_case_  )
        for image in image_inputs:
          self.assertIsInstance(snake_case_					,      torch.Tensor  )

        # Test not batched input
        A_  :       Dict										=  image_processing(image_inputs[0]					,      return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape					,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )					,      )

        # Test batched
        A_  :       Union[str, Any]										=  image_processing(snake_case_					,      return_tensors='pt'  ).pixel_values
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )					,      )






"""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_     :						Any						=					re.compile(r'\s+')





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  [len(_UpperCAmelCase					) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_UpperCAmelCase					), "line_max": max(_UpperCAmelCase					)}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5					):






  """simple docstring"""





  A_  :       Optional[int]										=  ['auto-generated', 'autogenerated', 'automatically generated']
  A_  :       List[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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5 ,					_UpperCAmelCase=0.05					):






  """simple docstring"""





  A_  :       Any										=  ['unit tests', 'test file', 'configuration file']
  A_  :       Dict										=  example['content'].splitlines()
  A_  :       List[Any]										=  0
  A_  :       str										=  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
  A_  :       Tuple										=  example['content'].count('\n'					)
  A_  :       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__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  ['def ', 'class ', 'for ', 'while ']
  A_  :       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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=4					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  example['content'].splitlines()
  A_  :       Any										=  0
  for line in lines:
    counter += line.lower().count('='					)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  tokenizer(example['content'] ,					truncation=_UpperCAmelCase					)['input_ids']
  A_  :       Dict										=  len(example['content']					) / len(_UpperCAmelCase					)
  return {"ratio": ratio}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  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 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  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_     :						Optional[int]						=					HfArgumentParser(PreprocessingArguments)
lowerCamelCase_     :						Optional[Any]						=					parser.parse_args()
if args.num_workers is None:
   lowerCamelCase_     :						int						=					multiprocessing.cpu_count()
lowerCamelCase_     :						Tuple						=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
lowerCamelCase_     :						List[str]						=					time.time()
lowerCamelCase_     :						Optional[int]						=					ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

# Deduplicate hashes
lowerCamelCase_     :						int						=					set(ds.unique('hash'))
lowerCamelCase_     :						Union[str, Any]						=					len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

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

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