




"""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


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

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Optional[MinHash]:


			"""simple docstring"""
			if len(UpperCamelCase							) < MIN_NUM_TOKENS:
						return None
			__UpperCAmelCase	: Dict		=					MinHash(num_perm=UpperCamelCase							)
			for token in set(UpperCamelCase							):
						min_hash.update(token.encode()							)
			return min_hash

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Set[str]:


			"""simple docstring"""
			return {t for t in NON_ALPHA.split(UpperCamelCase							) if len(t.strip()							) > 0}



class      a__				:


			def __init__(      self      :    List[Any]      ,  *,
			    UpperCamelCase_      :    float = 0.85      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					duplication_jaccard_threshold
						__UpperCAmelCase	: List[Any]		=					NUM_PERM
						__UpperCAmelCase	: Union[str, Any]		=					MinHashLSH(threshold=self._duplication_jaccard_threshold      ,  num_perm=self._num_perm)

						__UpperCAmelCase	: Optional[int]		=					defaultdict(UpperCamelCase_)


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    MinHash):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self._index.query(UpperCamelCase_)
						if code_key in self._index.keys:
									print(F"Duplicate key {code_key}")
									return

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


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_duplicate_clusters()
						with open(UpperCamelCase_      ,  "w") as f:
									json.dump(UpperCamelCase_      ,  UpperCamelCase_)






def 					_UpperCamelCase    (			UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			__UpperCAmelCase	,     __UpperCAmelCase	: Optional[int]		=					element
			__UpperCAmelCase	: Optional[Any]		=					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    (			UpperCamelCase							)			->			Optional[Any]:


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

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase							)

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

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

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			float:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					get_tokens(UpperCamelCase							)
			__UpperCAmelCase	: List[str]		=					get_tokens(UpperCamelCase							)
			return len(tokensa & tokensa							) / len(tokensa | tokensa							)


A   =       None

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			str:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					[]
			for elementa in cluster:
						__UpperCAmelCase	: List[str]		=					_shared_dataset[elementa["base_index"]]["content"]
						for elementa in extremes:
									__UpperCAmelCase	: Any		=					_shared_dataset[elementa["base_index"]]["content"]
									if jaccard_similarity(UpperCamelCase				,       UpperCamelCase							) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									__UpperCAmelCase	: Union[str, Any]		=					1
									extremes.append(UpperCamelCase							)
			return extremes

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global _shared_dataset
			__UpperCAmelCase	: Dict		=					dataset
			__UpperCAmelCase	: List[Any]		=					[]
			__UpperCAmelCase	: List[Any]		=					partial(_find_cluster_extremes_shared				,       jaccard_threshold=UpperCamelCase							)
			with mp.Pool() as pool:
						for extremes in tqdm(
						    pool.imap_unordered(
						        UpperCamelCase				,       UpperCamelCase				,       )				,       total=len(UpperCamelCase							)				,       ):
									extremes_list.append(UpperCamelCase							)
			return extremes_list





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = 0.85							)			->			Tuple[Type[Dataset], List[List[Dict]]]:


			"""simple docstring"""
			__UpperCAmelCase	: List[str]		=					make_duplicate_clusters(UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: Optional[int]		=					{x["base_index"] for cluster in duplicate_clusters for x in cluster}
			__UpperCAmelCase	: int		=					{}
			__UpperCAmelCase	: Optional[int]		=					find_extremes(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			for extremes in extremes_clusters:
						for element in extremes:
									__UpperCAmelCase	: Union[str, Any]		=					element
			__UpperCAmelCase	: Optional[int]		=					duplicate_indices - set(extreme_dict.keys()							)
			__UpperCAmelCase	: List[str]		=					dataset.filter(lambda UpperCamelCase				,       UpperCamelCase							: idx not in remove_indices				,       with_indices=UpperCamelCase							)

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

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

			return ds_filter, duplicate_clusters








"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

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 TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


A   =       logging.get_logger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[List[ImageInput]]:


			"""simple docstring"""
			if isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(videos[0]				,       (list, tuple)							) and is_valid_image(videos[0][0]							):
						return videos

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

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

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





class      a__				(	__magic_name__   ):
			lowercase_    =	["pixel_values"]


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


						"""simple docstring"""




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

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


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


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						if "shortest_edge" in size:
									__UpperCAmelCase	: Dict		=					get_resize_output_image_size(UpperCamelCase_      ,  size["shortest_edge"]      ,  default_to_square=UpperCamelCase_)
						elif "height" in size and "width" in size:
									__UpperCAmelCase	: Dict		=					(size["height"], size["width"])
						else:
									raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
						return resize(UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Tuple      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_)
						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(UpperCamelCase_      ,  size=(size["height"], size["width"])      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[int, float]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Optional[int]      ,  ):


						"""simple docstring"""




						return rescale(UpperCamelCase_      ,  scale=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Union[str, Any]      ,  ):


						"""simple docstring"""




						return normalize(UpperCamelCase_      ,  mean=UpperCamelCase_      ,  std=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    ImageInput      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    float = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    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.")

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

						if do_resize:
									__UpperCAmelCase	: Tuple		=					self.resize(image=UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_)

						if do_center_crop:
									__UpperCAmelCase	: str		=					self.center_crop(UpperCamelCase_      ,  size=UpperCamelCase_)

						if do_rescale:
									__UpperCAmelCase	: Union[str, Any]		=					self.rescale(image=UpperCamelCase_      ,  scale=UpperCamelCase_)

						if do_normalize:
									__UpperCAmelCase	: Optional[int]		=					self.normalize(image=UpperCamelCase_      ,  mean=UpperCamelCase_      ,  std=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					to_channel_dimension_format(UpperCamelCase_      ,  UpperCamelCase_)
						return image


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


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					do_resize if do_resize is not None else self.do_resize
						__UpperCAmelCase	: Optional[Any]		=					resample if resample is not None else self.resample
						__UpperCAmelCase	: Any		=					do_center_crop if do_center_crop is not None else self.do_center_crop
						__UpperCAmelCase	: str		=					do_rescale if do_rescale is not None else self.do_rescale
						__UpperCAmelCase	: str		=					rescale_factor if rescale_factor is not None else self.rescale_factor
						__UpperCAmelCase	: Tuple		=					do_normalize if do_normalize is not None else self.do_normalize
						__UpperCAmelCase	: str		=					image_mean if image_mean is not None else self.image_mean
						__UpperCAmelCase	: Optional[Any]		=					image_std if image_std is not None else self.image_std

						__UpperCAmelCase	: Tuple		=					size if size is not None else self.size
						__UpperCAmelCase	: str		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Any		=					crop_size if crop_size is not None else self.crop_size
						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

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

						__UpperCAmelCase	: Dict		=					make_batched(UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					[
						    [
						        self._preprocess_image(
						            image=UpperCamelCase_      ,  do_resize=UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  do_center_crop=UpperCamelCase_      ,  crop_size=UpperCamelCase_      ,  do_rescale=UpperCamelCase_      ,  rescale_factor=UpperCamelCase_      ,  do_normalize=UpperCamelCase_      ,  image_mean=UpperCamelCase_      ,  image_std=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  )
						        for img in video
						    ]
						    for video in videos
						]

						__UpperCAmelCase	: int		=					{"pixel_values": videos}
						return BatchFeature(data=UpperCamelCase_      ,  tensor_type=UpperCamelCase_)


