






"""simple docstring"""






import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


UpperCAmelCase_   : Tuple   			= re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
UpperCAmelCase_   : List[Any]   			= 10
UpperCAmelCase_   : List[str]   			= 256



def 					_A			(__a							) ->       Optional[MinHash]:
    """simple docstring"""






    if len(__a							) < MIN_NUM_TOKENS:
        return None
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		MinHash(num_perm=__a							)
    for token in set(__a							):
        min_hash.update(token.encode()							)
    return min_hash



def 					_A			(__a							) ->       Set[str]:
    """simple docstring"""






    return {t for t in NON_ALPHA.split(__a							) if len(t.strip()							) > 0}




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Dict						,	*,
          lowercase_     :  float = 0.85						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		duplication_jaccard_threshold
          SCREAMING_SNAKE_CASE_      :  Any    						=		NUM_PERM
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		MinHashLSH(threshold=self._duplication_jaccard_threshold						,	num_perm=self._num_perm)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		defaultdict(lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  Tuple						,	lowercase_     :  MinHash):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self._index.query(lowercase_)
          if code_key in self._index.keys:
              print(F'Duplicate key {code_key}')
              return

          self._index.insert(lowercase_						,	lowercase_)
          if len(lowercase_) > 0:
              for base_duplicate in close_duplicates:
                  if base_duplicate in self._duplicate_clusters:
                      self._duplicate_clusters[base_duplicate].add(lowercase_)
                      break
              else:
                  self._duplicate_clusters[close_duplicates[0]].add(lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		[]
          for base, duplicates in self._duplicate_clusters.items():
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		[base] + list(lowercase_)
              # reformat the cluster to be a list of dict
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
              duplicate_clusters.append(lowercase_)
          return duplicate_clusters



      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.get_duplicate_clusters()
          with open(lowercase_						,	'''w''') as f:
              json.dump(lowercase_						,	lowercase_)



def 					_A			(__a							) ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		element
    SCREAMING_SNAKE_CASE_      :  Dict    						=		get_min_hash([t for t in NON_ALPHA.split(data['''content''']							) if len(t.strip()							) > 0]							)
    if min_hash is not None:
        return (index, data["repo_name"], data["path"]), min_hash



def 					_A			(__a							) ->       List[Any]:
    """simple docstring"""






    with mp.Pool() as pool:
        for data in pool.imap_unordered(
            _compute_min_hash    ,				ThreadedIterator(__a    ,				max_queue_size=1_00_00							)    ,				chunksize=1_00    ,				):
            if data is not None:
                yield data



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		DuplicationIndex(duplication_jaccard_threshold=__a							)

    for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__a							)							)    ,				max_queue_size=1_00							)							):
        di.add(__a    ,				__a							)

    # Returns a List[Cluster] where Cluster is List[str] with the filenames.
    return di.get_duplicate_clusters()



def 					_A			(__a    ,				__a							) ->       float:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		get_tokens(__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		get_tokens(__a							)
    return len(tokensa & tokensa							) / len(tokensa | tokensa							)


UpperCAmelCase_   : List[str]   			= None



def 					_A			(__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		[]
    for elementa in cluster:
        SCREAMING_SNAKE_CASE_      :  Any    						=		_shared_dataset[elementa['''base_index''']]['''content''']
        for elementa in extremes:
            SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		_shared_dataset[elementa['''base_index''']]['''content''']
            if jaccard_similarity(__a    ,				__a							) >= jaccard_threshold:
                elementa["copies"] += 1
                break
        else:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		1
            extremes.append(__a							)
    return extremes



def 					_A			(__a    ,				__a    ,				__a							) ->       List[str]:
    """simple docstring"""






    global _shared_dataset
    SCREAMING_SNAKE_CASE_      :  Any    						=		dataset
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  str    						=		partial(_find_cluster_extremes_shared    ,				jaccard_threshold=__a							)
    with mp.Pool() as pool:
        for extremes in tqdm(
            pool.imap_unordered(
                __a    ,				__a    ,				)    ,				total=len(__a							)    ,				):
            extremes_list.append(__a							)
    return extremes_list





def 					_A			(__a    ,				__a = 0.85							) ->       Tuple[Type[Dataset], List[List[Dict]]]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		make_duplicate_clusters(__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{}
    SCREAMING_SNAKE_CASE_      :  int    						=		find_extremes(__a    ,				__a    ,				__a							)
    for extremes in extremes_clusters:
        for element in extremes:
            SCREAMING_SNAKE_CASE_      :  int    						=		element
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		duplicate_indices - set(extreme_dict.keys()							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		dataset.filter(lambda __a    ,				__a							: idx not in remove_indices    ,				with_indices=__a							)

    # update duplicate_clusters
    for cluster in duplicate_clusters:
        for element in cluster:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		element['''base_index'''] in extreme_dict
            if element["is_extreme"]:
                SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		extreme_dict[element['''base_index''']]['''copies''']

    print(f'Original dataset size: {len(__a							)}'							)
    print(f'Number of duplicate clusters: {len(__a							)}'							)
    print(f'Files in duplicate cluster: {len(__a							)}'							)
    print(f'Unique files in duplicate cluster: {len(__a							)}'							)
    print(f'Filtered dataset size: {len(__a							)}'							)

    return ds_filter, duplicate_clusters










"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

UpperCAmelCase_   : int   			= logging.get_logger(__name__)



def 					_A			(__a							) ->       List[List[ImageInput]]:
    """simple docstring"""






    if isinstance(__a    ,				(list, tuple)							) and isinstance(videos[0]    ,				(list, tuple)							) and is_valid_image(videos[0][0]							):
        return videos

    elif isinstance(__a    ,				(list, tuple)							) and is_valid_image(videos[0]							):
        return [videos]

    elif is_valid_image(__a							):
        return [[videos]]

    raise ValueError(f'Could not make batched video from {videos}'							)





class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["pixel_values"]
      def __init__(						self     :  Dict						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[int, float] = 1 / 255						,	lowercase_     :  bool = True						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


          super().__init__(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		size if size is not None else {'''shortest_edge''': 256}
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          SCREAMING_SNAKE_CASE_      :  Dict    						=		get_size_dict(lowercase_						,	param_name='''crop_size''')

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_resize
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		do_center_crop
          SCREAMING_SNAKE_CASE_      :  Dict    						=		crop_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		resample
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		do_rescale
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		rescale_factor
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		offset
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		do_normalize
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		image_std if image_std is not None else IMAGENET_STANDARD_STD
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Any						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          if "shortest_edge" in size:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		get_resize_output_image_size(lowercase_						,	size['''shortest_edge''']						,	default_to_square=lowercase_)
          elif "height" in size and "width" in size:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(size['''height'''], size['''width'''])
          else:
              raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
          return resize(lowercase_						,	size=lowercase_						,	resample=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		get_size_dict(lowercase_)
          if "height" not in size or "width" not in size:
              raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
          return center_crop(lowercase_						,	size=(size['''height'''], size['''width'''])						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[int, float]						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Optional[int]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image.astype(np.floataa)
          if offset:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		image - (scale / 2)
          return rescale(lowercase_						,	scale=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[str]						,	):




          '''simple docstring'''


          return normalize(lowercase_						,	mean=lowercase_						,	std=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  ImageInput						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = None						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = None						,	lowercase_     :  float = None						,	lowercase_     :  bool = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[ChannelDimension] = ChannelDimension.FIRST						,	):




          '''simple docstring'''


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

          if do_center_crop and crop_size is None:
              raise ValueError('''Crop size must be specified if do_center_crop is True.''')

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

          if offset and not do_rescale:
              raise ValueError('''For offset, do_rescale must also be set to True.''')

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		to_numpy_array(lowercase_)

          if do_resize:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.resize(image=lowercase_						,	size=lowercase_						,	resample=lowercase_)

          if do_center_crop:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.center_crop(lowercase_						,	size=lowercase_)

          if do_rescale:
              SCREAMING_SNAKE_CASE_      :  int    						=		self.rescale(image=lowercase_						,	scale=lowercase_						,	offset=lowercase_)

          if do_normalize:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.normalize(image=lowercase_						,	mean=lowercase_						,	std=lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		to_channel_dimension_format(lowercase_						,	lowercase_)
          return image



      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  ImageInput						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = None						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = None						,	lowercase_     :  float = None						,	lowercase_     :  bool = None						,	lowercase_     :  bool = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[str, TensorType]] = None						,	lowercase_     :  ChannelDimension = ChannelDimension.FIRST						,	**lowercase_     :  Optional[Any]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		do_resize if do_resize is not None else self.do_resize
          SCREAMING_SNAKE_CASE_      :  int    						=		resample if resample is not None else self.resample
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		do_center_crop if do_center_crop is not None else self.do_center_crop
          SCREAMING_SNAKE_CASE_      :  Dict    						=		do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_      :  Dict    						=		rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_      :  Dict    						=		offset if offset is not None else self.offset
          SCREAMING_SNAKE_CASE_      :  str    						=		do_normalize if do_normalize is not None else self.do_normalize
          SCREAMING_SNAKE_CASE_      :  Dict    						=		image_mean if image_mean is not None else self.image_mean
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		image_std if image_std is not None else self.image_std

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		size if size is not None else self.size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		crop_size if crop_size is not None else self.crop_size
          SCREAMING_SNAKE_CASE_      :  Dict    						=		get_size_dict(lowercase_						,	param_name='''crop_size''')

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

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		make_batched(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[
              [
                  self._preprocess_image(
                      image=lowercase_						,	do_resize=lowercase_						,	size=lowercase_						,	resample=lowercase_						,	do_center_crop=lowercase_						,	crop_size=lowercase_						,	do_rescale=lowercase_						,	rescale_factor=lowercase_						,	offset=lowercase_						,	do_normalize=lowercase_						,	image_mean=lowercase_						,	image_std=lowercase_						,	data_format=lowercase_						,	)
                  for img in video
              ]
              for video in videos
          ]

          SCREAMING_SNAKE_CASE_      :  int    						=		{'''pixel_values''': videos}
          return BatchFeature(data=lowercase_						,	tensor_type=lowercase_)


