

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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







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



def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Any:

     """simple docstring"""







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



def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







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



def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







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



def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=5				)       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    ["auto-generated", "autogenerated", "automatically generated"]
     _lowerCAmelCase   :					Any        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=5		,							SCREAMING_SNAKE_CASE=0.05				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    ["unit tests", "test file", "configuration file"]
     _lowerCAmelCase   :					Union[str, Any]        =    example["content"].splitlines()
     _lowerCAmelCase   :					Any        =    0
     _lowerCAmelCase   :					List[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
     _lowerCAmelCase   :					List[Any]        =    example["content"].count("\n"				)
     _lowerCAmelCase   :					int        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    ["def ", "class ", "for ", "while "]
     _lowerCAmelCase   :					Union[str, Any]        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=4				)       ->      List[str]:

     """simple docstring"""







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



def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					List[Any]        =    tokenizer(example["content"]		,							truncation=UpperCamelCase__				)["input_ids"]
     _lowerCAmelCase   :					Optional[int]        =    len(example["content"]				) / len(UpperCamelCase__				)
     return {"ratio": ratio}



def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    {}
     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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      str:

     """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
__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    				=     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}''')









from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
  import PIL

# soft dependency
if is_pytesseract_available():
  import pytesseract

__UpperCAmelCase    				=     logging.get_logger(__name__)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     return [
         int(1000 * (box[0] / width)				),
         int(1000 * (box[1] / height)				),
         int(1000 * (box[2] / width)				),
         int(1000 * (box[3] / height)				),
     ]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

     # apply OCR
     _lowerCAmelCase   :					List[str]        =    to_pil_image(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    pil_image.size
     _lowerCAmelCase   :					Union[str, Any]        =    pytesseract.image_to_data(SCREAMING_SNAKE_CASE		,							lang=SCREAMING_SNAKE_CASE		,							output_type="dict"		,							config=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     _lowerCAmelCase   :					Dict        =    [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if not word.strip()]
     _lowerCAmelCase   :					Any        =    [word for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Dict        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[int]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     _lowerCAmelCase   :					Union[str, Any]        =    []
     for x, y, w, h in zip(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [x, y, x + w, y + h]
          actual_boxes.append(SCREAMING_SNAKE_CASE				)

     # finally, normalize the bounding boxes
     _lowerCAmelCase   :					Dict        =    []
     for box in actual_boxes:
          normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)				)

     assert len(SCREAMING_SNAKE_CASE				) == len(SCREAMING_SNAKE_CASE				), "Not as many words as there are bounding boxes"

     return words, normalized_boxes





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = ""     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, Any]        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Dict        =    [to_numpy_array(A_       ) for image in images]

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      _lowerCAmelCase   :					Optional[int]        =    [flip_channel_order(A_       ) for image in images]
      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    BatchFeature(data={"pixel_values": images}     ,						tensor_type=A_       )

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






