



"""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


_lowerCAmelCase       :Tuple	=   logging.get_logger(__name__)





def 					lowerCamelCase_		(UpperCamelCase__						:						Optional[int]				):
    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 				_UpperCAmelCase				(					a ):





     '''simple docstring'''



     a__   							=['''pixel_values''']





     def __init__(   self			,      A = True			,      A = None			,      A = PILImageResampling.BILINEAR			,      A = True			,      A = None			,      A = True			,      A = 1 / 2_5_5			,      A = True			,      A = None			,      A = None			,      **A			,      )				->   None:
         super().__init__(**a_						)
         _UpperCAmelCase				:    Any											=       size if size is not None else {"shortest_edge": 2_2_4}
         _UpperCAmelCase				:    List[str]											=       get_size_dict(a_			,      default_to_square=a_						)
         _UpperCAmelCase				:    Optional[int]											=       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         _UpperCAmelCase				:    List[str]											=       get_size_dict(a_			,      param_name='''crop_size'''						)

         _UpperCAmelCase				:    int											=       do_resize
         _UpperCAmelCase				:    int											=       size
         _UpperCAmelCase				:    Tuple											=       do_center_crop
         _UpperCAmelCase				:    Dict											=       crop_size
         _UpperCAmelCase				:    List[str]											=       resample
         _UpperCAmelCase				:    Dict											=       do_rescale
         _UpperCAmelCase				:    Optional[int]											=       rescale_factor
         _UpperCAmelCase				:    Optional[int]											=       do_normalize
         _UpperCAmelCase				:    Tuple											=       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 					__lowerCAmelCase      (   self			,      A			,      A			,      A = PILImageResampling.BILINEAR			,      A = None			,      **A			,      )				->   np.ndarray:
         _UpperCAmelCase				:    Dict											=       get_size_dict(a_			,      default_to_square=a_						)
         if "shortest_edge" in size:
             _UpperCAmelCase				:    int											=       get_resize_output_image_size(a_			,      size['''shortest_edge''']			,      default_to_square=a_						)
         elif "height" in size and "width" in size:
             _UpperCAmelCase				:    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(a_			,      size=a_			,      resample=a_			,      data_format=a_			,      **a_						)





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A = None			,      **A			,      )				->   np.ndarray:
         _UpperCAmelCase				:    Optional[int]											=       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 					__lowerCAmelCase      (   self			,      A			,      A			,      A = None			,      **A			,      )				->   Optional[Any]:
         return rescale(a_			,      scale=a_			,      data_format=a_			,      **a_						)





     def 					__lowerCAmelCase      (   self			,      A			,      A			,      A			,      A = None			,      **A			,      )				->   np.ndarray:
         return normalize(a_			,      mean=a_			,      std=a_			,      data_format=a_			,      **a_						)





     def 					__lowerCAmelCase      (   self			,      A			,      A = None			,      A = None			,      A = None			,      A = None			,      A = None			,      A = None			,      A = None			,      A = None			,      A = None			,      A = None			,      A = 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.'''						)

         # All transformations expect numpy arrays.
         _UpperCAmelCase				:    Union[str, Any]											=       to_numpy_array(a_						)

         if do_resize:
             _UpperCAmelCase				:    str											=       self.resize(image=a_			,      size=a_			,      resample=a_						)

         if do_center_crop:
             _UpperCAmelCase				:    Dict											=       self.center_crop(a_			,      size=a_						)

         if do_rescale:
             _UpperCAmelCase				:    Tuple											=       self.rescale(image=a_			,      scale=a_						)

         if do_normalize:
             _UpperCAmelCase				:    str											=       self.normalize(image=a_			,      mean=a_			,      std=a_						)

         _UpperCAmelCase				:    Union[str, Any]											=       to_channel_dimension_format(a_			,      a_						)
         return image





     def 					__lowerCAmelCase      (   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			,      **A			,      )				->   PIL.Image.Image:
         _UpperCAmelCase				:    int											=       do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase				:    List[str]											=       resample if resample is not None else self.resample
         _UpperCAmelCase				:    Tuple											=       do_center_crop if do_center_crop is not None else self.do_center_crop
         _UpperCAmelCase				:    int											=       do_rescale if do_rescale is not None else self.do_rescale
         _UpperCAmelCase				:    Union[str, Any]											=       rescale_factor if rescale_factor is not None else self.rescale_factor
         _UpperCAmelCase				:    str											=       do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase				:    Dict											=       image_mean if image_mean is not None else self.image_mean
         _UpperCAmelCase				:    List[Any]											=       image_std if image_std is not None else self.image_std

         _UpperCAmelCase				:    List[Any]											=       size if size is not None else self.size
         _UpperCAmelCase				:    Optional[int]											=       get_size_dict(a_			,      default_to_square=a_						)
         _UpperCAmelCase				:    Optional[int]											=       crop_size if crop_size is not None else self.crop_size
         _UpperCAmelCase				:    List[str]											=       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				:    str											=       make_batched(a_						)

         _UpperCAmelCase				:    List[str]											=       [
             [
                 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_			,      do_normalize=a_			,      image_mean=a_			,      image_std=a_			,      data_format=a_			,      )
                 for img in video
             ]
             for video in videos
         ]

         _UpperCAmelCase				:    Tuple											=       {"pixel_values": videos}
         return BatchFeature(data=a_			,      tensor_type=a_						)






'''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


lowerCAmelCase   : List[str]             =				re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
lowerCAmelCase   : Union[str, Any]             =				1_0
lowerCAmelCase   : Optional[Any]             =				2_5_6




def 	_A  (       A							)  ->				Optional[MinHash]:
	if len(A							) < MIN_NUM_TOKENS:
		return None
	lowercase						:		List[Any]   					=		MinHash(num_perm=A							)
	for token in set(A							):
		min_hash.update(token.encode()							)
	return min_hash




def 	_A  (       A							)  ->				Set[str]:
	return {t for t in NON_ALPHA.split(A							) if len(t.strip()							) > 0}




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      *,
				    a_ = 0.85							,      )    ->  List[str]:
					lowercase						:		Any   					=		duplication_jaccard_threshold
					lowercase						:		str   					=		NUM_PERM
					lowercase						:		Union[str, Any]   					=		MinHashLSH(threshold=self._duplication_jaccard_threshold							,      num_perm=self._num_perm							)

					lowercase						:		Any   					=		defaultdict(a_							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					lowercase						:		Dict   					=		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 	a__		(     self							)    ->  List[List[Dict]]:
					lowercase						:		str   					=		[]
					for base, duplicates in self._duplicate_clusters.items():
						lowercase						:		str   					=		[base] + list(a_							)
						# reformat the cluster to be a list of dict
						lowercase						:		Optional[Any]   					=		[{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
						duplicate_clusters.append(a_							)
					return duplicate_clusters


				def 	a__		(     self							,      a_							)    ->  None:
					lowercase						:		Tuple   					=		self.get_duplicate_clusters()
					with open(a_							,      "w"							) as f:
						json.dump(a_							,      a_							)




def 	_A  (       A							)  ->				Dict:
	lowercase					,		lowercase						:		List[str]   					=		element
	lowercase						:		int   					=		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							)  ->				Any:
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,ThreadedIterator(A							,max_queue_size=1_0_0_0_0							)							,chunksize=1_0_0							,):
			if data is not None:
				yield data




def 	_A  (       A							,A							)  ->				List[str]:
	lowercase						:		Dict   					=		DuplicationIndex(duplication_jaccard_threshold=A							)

	for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A							)							)							,max_queue_size=1_0_0							)							):
		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:
	lowercase						:		int   					=		get_tokens(A							)
	lowercase						:		Dict   					=		get_tokens(A							)
	return len(tokensa & tokensa							) / len(tokensa | tokensa							)


lowerCAmelCase   : List[str]             =				None




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	lowercase						:		int   					=		[]
	for elementa in cluster:
		lowercase						:		Any   					=		_shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			lowercase						:		Optional[int]   					=		_shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(A							,A							) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			lowercase						:		List[Any]   					=		1
			extremes.append(A							)
	return extremes




def 	_A  (       A							,A							,A							)  ->				Optional[Any]:
	global _shared_dataset
	lowercase						:		Dict   					=		dataset
	lowercase						:		int   					=		[]
	lowercase						:		int   					=		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]]]:
	lowercase						:		Dict   					=		make_duplicate_clusters(A							,A							)
	lowercase						:		List[str]   					=		{x["base_index"] for cluster in duplicate_clusters for x in cluster}
	lowercase						:		Any   					=		{}
	lowercase						:		int   					=		find_extremes(A							,A							,A							)
	for extremes in extremes_clusters:
		for element in extremes:
			lowercase						:		str   					=		element
	lowercase						:		str   					=		duplicate_indices - set(extreme_dict.keys()							)
	lowercase						:		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:
			lowercase						:		List[str]   					=		element["base_index"] in extreme_dict
			if element["is_extreme"]:
				lowercase						:		str   					=		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


