






'''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

__lowerCAmelCase									=   logging.get_logger(__name__)





def 		_UpperCAmelCase (				__A				: str   ):
				if isinstance(lowerCAmelCase_    ,		(list, tuple)   ) and isinstance(videos[0]    ,		(list, tuple)   ) and is_valid_image(videos[0][0]   ):
								return videos

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

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

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





class 						SCREAMING_SNAKE_CASE       (       __snake_case							):
			snake_case__	=					["pixel_values"]






			def __init__(			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      PILImageResampling = PILImageResampling.BILINEAR	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Union[int, float] = 1 / 255	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    **__SCREAMING_SNAKE_CASE   :      Optional[Any]	,    )      ->      None:
							super().__init__(**__lowerCamelCase       )
							a_		:	Any									=   size if size is not None else {'''shortest_edge''': 256}
							a_		:	Union[str, Any]									=   get_size_dict(__lowerCamelCase	,    default_to_square=__lowerCamelCase       )
							a_		:	Optional[Any]									=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
							a_		:	Optional[int]									=   get_size_dict(__lowerCamelCase	,    param_name='''crop_size'''       )

							a_		:	Any									=   do_resize
							a_		:	List[str]									=   size
							a_		:	Optional[int]									=   do_center_crop
							a_		:	List[str]									=   crop_size
							a_		:	List[Any]									=   resample
							a_		:	int									=   do_rescale
							a_		:	Union[str, Any]									=   rescale_factor
							a_		:	Dict									=   offset
							a_		:	str									=   do_normalize
							a_		:	Tuple									=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							a_		:	Dict									=   image_std if image_std is not None else IMAGENET_STANDARD_STD






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int]	,    __SCREAMING_SNAKE_CASE   :      PILImageResampling = PILImageResampling.BILINEAR	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    )      ->      np.ndarray:
							a_		:	Optional[Any]									=   get_size_dict(__lowerCamelCase	,    default_to_square=__lowerCamelCase       )
							if "shortest_edge" in size:
											a_		:	Dict									=   get_resize_output_image_size(__lowerCamelCase	,    size['''shortest_edge''']	,    default_to_square=__lowerCamelCase       )
							elif "height" in size and "width" in size:
											a_		:	str									=   (size['''height'''], size['''width'''])
							else:
											raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
							return resize(__lowerCamelCase	,    size=__lowerCamelCase	,    resample=__lowerCamelCase	,    data_format=__lowerCamelCase	,    **__lowerCamelCase       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int]	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      str	,    )      ->      np.ndarray:
							a_		:	Optional[Any]									=   get_size_dict(__lowerCamelCase       )
							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(__lowerCamelCase	,    size=(size['''height'''], size['''width'''])	,    data_format=__lowerCamelCase	,    **__lowerCamelCase       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Union[int, float]	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      List[Any]	,    )      ->      Tuple:
							a_		:	List[Any]									=   image.astype(np.floataa       )
							if offset:
											a_		:	Tuple									=   image - (scale / 2)
							return rescale(__lowerCamelCase	,    scale=__lowerCamelCase	,    data_format=__lowerCamelCase	,    **__lowerCamelCase       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Union[float, List[float]]	,    __SCREAMING_SNAKE_CASE   :      Union[float, List[float]]	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, ChannelDimension]] = None	,    **__SCREAMING_SNAKE_CASE   :      Optional[int]	,    )      ->      np.ndarray:
							return normalize(__lowerCamelCase	,    mean=__lowerCamelCase	,    std=__lowerCamelCase	,    data_format=__lowerCamelCase	,    **__lowerCamelCase       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      ImageInput	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      PILImageResampling = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      float = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    __SCREAMING_SNAKE_CASE   :      Optional[ChannelDimension] = ChannelDimension.FIRST	,    )      ->      np.ndarray:







							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.
							a_		:	Optional[Any]									=   to_numpy_array(__lowerCamelCase       )

							if do_resize:
											a_		:	int									=   self.resize(image=__lowerCamelCase	,    size=__lowerCamelCase	,    resample=__lowerCamelCase       )

							if do_center_crop:
											a_		:	Any									=   self.center_crop(__lowerCamelCase	,    size=__lowerCamelCase       )

							if do_rescale:
											a_		:	Optional[Any]									=   self.rescale(image=__lowerCamelCase	,    scale=__lowerCamelCase	,    offset=__lowerCamelCase       )

							if do_normalize:
											a_		:	int									=   self.normalize(image=__lowerCamelCase	,    mean=__lowerCamelCase	,    std=__lowerCamelCase       )

							a_		:	Tuple									=   to_channel_dimension_format(__lowerCamelCase	,    __lowerCamelCase       )
							return image







			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      ImageInput	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      PILImageResampling = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      Dict[str, int] = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      float = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      bool = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[float, List[float]]] = None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, TensorType]] = None	,    __SCREAMING_SNAKE_CASE   :      ChannelDimension = ChannelDimension.FIRST	,    **__SCREAMING_SNAKE_CASE   :      Optional[int]	,    )      ->      PIL.Image.Image:
							a_		:	Union[str, Any]									=   do_resize if do_resize is not None else self.do_resize
							a_		:	Optional[Any]									=   resample if resample is not None else self.resample
							a_		:	Union[str, Any]									=   do_center_crop if do_center_crop is not None else self.do_center_crop
							a_		:	Dict									=   do_rescale if do_rescale is not None else self.do_rescale
							a_		:	Optional[Any]									=   rescale_factor if rescale_factor is not None else self.rescale_factor
							a_		:	str									=   offset if offset is not None else self.offset
							a_		:	Dict									=   do_normalize if do_normalize is not None else self.do_normalize
							a_		:	int									=   image_mean if image_mean is not None else self.image_mean
							a_		:	Optional[int]									=   image_std if image_std is not None else self.image_std

							a_		:	Union[str, Any]									=   size if size is not None else self.size
							a_		:	Optional[Any]									=   get_size_dict(__lowerCamelCase	,    default_to_square=__lowerCamelCase       )
							a_		:	Dict									=   crop_size if crop_size is not None else self.crop_size
							a_		:	List[str]									=   get_size_dict(__lowerCamelCase	,    param_name='''crop_size'''       )

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

							a_		:	Dict									=   make_batched(__lowerCamelCase       )

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

							a_		:	Tuple									=   {'''pixel_values''': videos}
							return BatchFeature(data=__lowerCamelCase	,    tensor_type=__lowerCamelCase       )








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


__a	 =      re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__a	 =      10
__a	 =      256
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[MinHash]:
   if len(lowerCAmelCase_ ) < MIN_NUM_TOKENS:
      return None
   UpperCAmelCase      					=  MinHash(num_perm=lowerCAmelCase_ )
   for token in set(lowerCAmelCase_ ):
      min_hash.update(token.encode() )
   return min_hash
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Set[str]:
   return {t for t in NON_ALPHA.split(lowerCAmelCase_ ) if len(t.strip() ) > 0}






class 	__lowercase					:





      def __init__(		self    :       List[str]       ,							*,
          __lowerCamelCase    :       float = 0.85       ,							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  duplication_jaccard_threshold
         UpperCAmelCase      					=  NUM_PERM
         UpperCAmelCase      					=  MinHashLSH(threshold=self._duplication_jaccard_threshold       ,							num_perm=self._num_perm							)

         UpperCAmelCase      					=  defaultdict(__lowerCamelCase							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       MinHash							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  self._index.query(__lowerCamelCase							)
         if code_key in self._index.keys:
            print(F"""Duplicate key {code_key}"""							)
            return

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





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> List[List[Dict]]:






         """simple docstring"""







         UpperCAmelCase      					=  []
         for base, duplicates in self._duplicate_clusters.items():
            UpperCAmelCase      					=  [base] + list(__lowerCamelCase							)
            # 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(__lowerCamelCase							)
         return duplicate_clusters





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]							)     -> None:






         """simple docstring"""







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

def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Tuple:
   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 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   with mp.Pool() as pool:
      for data in pool.imap_unordered(
          _compute_min_hash	,    ThreadedIterator(lowerCAmelCase_	,    max_queue_size=1_0_0_0_0 )	,    chunksize=1_0_0	,    ):
         if data is not None:
            yield data
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Any:
   UpperCAmelCase      					=  DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase_ )

   for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase_ ) )	,    max_queue_size=1_0_0 ) ):
      di.add(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Returns a List[Cluster] where Cluster is List[str] with the filenames.
   return di.get_duplicate_clusters()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->float:
   UpperCAmelCase      					=  get_tokens(lowerCAmelCase_ )
   UpperCAmelCase      					=  get_tokens(lowerCAmelCase_ )
   return len(tokensa & tokensa ) / len(tokensa | tokensa )


__a	 =      None
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   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(lowerCAmelCase_	,    lowerCAmelCase_ ) >= jaccard_threshold:
            elementa["copies"] += 1
            break
      else:
         UpperCAmelCase      					=  1
         extremes.append(lowerCAmelCase_ )
   return extremes
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   global _shared_dataset
   UpperCAmelCase      					=  dataset
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  partial(_find_cluster_extremes_shared	,    jaccard_threshold=lowerCAmelCase_ )
   with mp.Pool() as pool:
      for extremes in tqdm(
          pool.imap_unordered(
              lowerCAmelCase_	,    lowerCAmelCase_	,    )	,    total=len(lowerCAmelCase_ )	,    ):
         extremes_list.append(lowerCAmelCase_ )
   return extremes_list







def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ = 0.85 )    ->Tuple[Type[Dataset], List[List[Dict]]]:
   UpperCAmelCase      					=  make_duplicate_clusters(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
   UpperCAmelCase      					=  {}
   UpperCAmelCase      					=  find_extremes(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )
   for extremes in extremes_clusters:
      for element in extremes:
         UpperCAmelCase      					=  element
   UpperCAmelCase      					=  duplicate_indices - set(extreme_dict.keys() )
   UpperCAmelCase      					=  dataset.filter(lambda lowerCAmelCase_	,    lowerCAmelCase_ : idx not in remove_indices	,    with_indices=lowerCAmelCase_ )

   # 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(lowerCAmelCase_ )}""" )
   print(F"""Number of duplicate clusters: {len(lowerCAmelCase_ )}""" )
   print(F"""Files in duplicate cluster: {len(lowerCAmelCase_ )}""" )
   print(F"""Unique files in duplicate cluster: {len(lowerCAmelCase_ )}""" )
   print(F"""Filtered dataset size: {len(lowerCAmelCase_ )}""" )

   return ds_filter, duplicate_clusters



