






"""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					      =  re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
_UpperCamelCase					      =  10
_UpperCamelCase					      =  256

def _a			(		_snake_case							):







      """simple docstring"""






      if len(_snake_case							) < MIN_NUM_TOKENS:
            return None
      UpperCAmelCase          =	MinHash(num_perm=_snake_case							)
      for token in set(_snake_case							):
            min_hash.update(token.encode()							)
      return min_hash

def _a			(		_snake_case							):







      """simple docstring"""






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







class  lowerCamelCase__ :






    def __init__(			self     ,*,
        A = 0.85     ,):
          UpperCAmelCase          =	duplication_jaccard_threshold
          UpperCAmelCase          =	NUM_PERM
          UpperCAmelCase          =	MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm )

          UpperCAmelCase          =	defaultdict(A )






    def     _UpperCamelCase							(			self     ,A     ,A ):
          UpperCAmelCase          =	self._index.query(A )
          if code_key in self._index.keys:
                print(F'''Duplicate key {code_key}''' )
                return

          self._index.insert(A     ,A )
          if len(A ) > 0:
                for base_duplicate in close_duplicates:
                      if base_duplicate in self._duplicate_clusters:
                            self._duplicate_clusters[base_duplicate].add(A )
                            break
                else:
                      self._duplicate_clusters[close_duplicates[0]].add(A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	[]
          for base, duplicates in self._duplicate_clusters.items():
                UpperCAmelCase          =	[base] + list(A )
                # reformat the cluster to be a list of dict
                UpperCAmelCase          =	[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
                duplicate_clusters.append(A )
          return duplicate_clusters






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.get_duplicate_clusters()
          with open(A     ,"""w""" ) as f:
                json.dump(A     ,A )







def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	element
      UpperCAmelCase          =	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			(		_snake_case							):







      """simple docstring"""






      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash				,	ThreadedIterator(_snake_case				,	max_queue_size=1_0000							)				,	chunksize=100				,	):
                  if data is not None:
                        yield data

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	DuplicationIndex(duplication_jaccard_threshold=_snake_case							)

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_snake_case							)							)				,	max_queue_size=100							)							):
            di.add(_snake_case				,	_snake_case							)

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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	get_tokens(_snake_case							)
      UpperCAmelCase          =	get_tokens(_snake_case							)
      return len(tokensa & tokensa							) / len(tokensa | tokensa							)


_UpperCamelCase					      =  None

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      for elementa in cluster:
            UpperCAmelCase          =	_shared_dataset[elementa["""base_index"""]]["""content"""]
            for elementa in extremes:
                  UpperCAmelCase          =	_shared_dataset[elementa["""base_index"""]]["""content"""]
                  if jaccard_similarity(_snake_case				,	_snake_case							) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  UpperCAmelCase          =	1
                  extremes.append(_snake_case							)
      return extremes

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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






def _a			(		_snake_case				,	_snake_case = 0.85							):







      """simple docstring"""






      UpperCAmelCase          =	make_duplicate_clusters(_snake_case				,	_snake_case							)
      UpperCAmelCase          =	{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
      UpperCAmelCase          =	{}
      UpperCAmelCase          =	find_extremes(_snake_case				,	_snake_case				,	_snake_case							)
      for extremes in extremes_clusters:
            for element in extremes:
                  UpperCAmelCase          =	element
      UpperCAmelCase          =	duplicate_indices - set(extreme_dict.keys()							)
      UpperCAmelCase          =	dataset.filter(lambda _snake_case				,	_snake_case							: idx not in remove_indices				,	with_indices=_snake_case							)

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  UpperCAmelCase          =	element["""base_index"""] in extreme_dict
                  if element["is_extreme"]:
                        UpperCAmelCase          =	extreme_dict[element["""base_index"""]]["""copies"""]

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

      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					      =  logging.get_logger(__name__)

def _a			(		_snake_case							):







      """simple docstring"""






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

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

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

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







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      ['''pixel_values''']






    def __init__(			self     ,A = True     ,A = None     ,A = PILImageResampling.BILINEAR     ,A = True     ,A = None     ,A = True     ,A = 1 / 255     ,A = True     ,A = True     ,A = None     ,A = None     ,**A     ,):
          super().__init__(**A )
          UpperCAmelCase          =	size if size is not None else {"""shortest_edge""": 256}
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size""" )

          UpperCAmelCase          =	do_resize
          UpperCAmelCase          =	size
          UpperCAmelCase          =	do_center_crop
          UpperCAmelCase          =	crop_size
          UpperCAmelCase          =	resample
          UpperCAmelCase          =	do_rescale
          UpperCAmelCase          =	rescale_factor
          UpperCAmelCase          =	offset
          UpperCAmelCase          =	do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase          =	image_std if image_std is not None else IMAGENET_STANDARD_STD






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = PILImageResampling.BILINEAR     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          if "shortest_edge" in size:
                UpperCAmelCase          =	get_resize_output_image_size(A     ,size["""shortest_edge"""]     ,default_to_square=A )
          elif "height" in size and "width" in size:
                UpperCAmelCase          =	(size["""height"""], size["""width"""])
          else:
                raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
          return resize(A     ,size=A     ,resample=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(A )
          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(A     ,size=(size["""height"""], size["""width"""])     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = True     ,A = None     ,**A     ,):
          UpperCAmelCase          =	image.astype(np.floataa )
          if offset:
                UpperCAmelCase          =	image - (scale / 2)
          return rescale(A     ,scale=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A = None     ,**A     ,):
          return normalize(A     ,mean=A     ,std=A     ,data_format=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,):
          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.
          UpperCAmelCase          =	to_numpy_array(A )

          if do_resize:
                UpperCAmelCase          =	self.resize(image=A     ,size=A     ,resample=A )

          if do_center_crop:
                UpperCAmelCase          =	self.center_crop(A     ,size=A )

          if do_rescale:
                UpperCAmelCase          =	self.rescale(image=A     ,scale=A     ,offset=A )

          if do_normalize:
                UpperCAmelCase          =	self.normalize(image=A     ,mean=A     ,std=A )

          UpperCAmelCase          =	to_channel_dimension_format(A     ,A )
          return image






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,**A     ,):
          UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase          =	resample if resample is not None else self.resample
          UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase          =	offset if offset is not None else self.offset
          UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase          =	image_std if image_std is not None else self.image_std

          UpperCAmelCase          =	size if size is not None else self.size
          UpperCAmelCase          =	get_size_dict(A     ,default_to_square=A )
          UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase          =	get_size_dict(A     ,param_name="""crop_size""" )

          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.""" )

          UpperCAmelCase          =	make_batched(A )

          UpperCAmelCase          =	[
              [
                  self._preprocess_image(
                      image=A     ,do_resize=A     ,size=A     ,resample=A     ,do_center_crop=A     ,crop_size=A     ,do_rescale=A     ,rescale_factor=A     ,offset=A     ,do_normalize=A     ,image_mean=A     ,image_std=A     ,data_format=A     ,)
                  for img in video
              ]
              for video in videos
          ]

          UpperCAmelCase          =	{"""pixel_values""": videos}
          return BatchFeature(data=A     ,tensor_type=A )



