


'''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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase   : str             =				logging.get_logger(__name__)




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				['''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_							)
					lowercase						:		Any   					=		size if size is not None else {"shortest_edge": 2_5_6}
					lowercase						:		Union[str, Any]   					=		get_size_dict(a_							,      default_to_square=a_							)
					lowercase						:		List[str]   					=		crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
					lowercase						:		List[Any]   					=		get_size_dict(a_							)
					lowercase						:		List[str]   					=		do_resize
					lowercase						:		List[Any]   					=		size
					lowercase						:		List[Any]   					=		resample
					lowercase						:		List[Any]   					=		do_center_crop
					lowercase						:		Optional[Any]   					=		crop_size
					lowercase						:		Tuple   					=		do_rescale
					lowercase						:		Union[str, Any]   					=		rescale_factor
					lowercase						:		Tuple   					=		do_normalize
					lowercase						:		Any   					=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowercase						:		List[str]   					=		image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 	a__		(     self							,      a_							,      a_							,      a_ = PILImageResampling.BICUBIC							,      a_ = None							,      **a_							,      )    ->  np.ndarray:
					lowercase						:		Union[str, Any]   					=		get_size_dict(a_							,      default_to_square=a_							)
					if "shortest_edge" not in size:
						raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
					lowercase						:		int   					=		get_resize_output_image_size(a_							,      size=size["shortest_edge"]							,      default_to_square=a_							)
					return resize(a_							,      size=a_							,      resample=a_							,      data_format=a_							,      **a_							)

				def 	a__		(     self							,      a_							,      a_							,      a_ = None							,      **a_							,      )    ->  np.ndarray:
					lowercase						:		Optional[Any]   					=		get_size_dict(a_							)
					return center_crop(a_							,      size=(size["height"], size["width"])							,      data_format=a_							,      **a_							)

				def 	a__		(     self							,      a_							,      a_							,      a_ = None							,      **a_							)    ->  np.ndarray:
					return rescale(a_							,      scale=a_							,      data_format=a_							,      **a_							)

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


				def 	a__		(     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_							,      )    ->  Dict:
					lowercase						:		List[Any]   					=		do_resize if do_resize is not None else self.do_resize
					lowercase						:		List[str]   					=		size if size is not None else self.size
					lowercase						:		Optional[int]   					=		get_size_dict(a_							,      default_to_square=a_							)
					lowercase						:		List[str]   					=		resample if resample is not None else self.resample
					lowercase						:		Optional[int]   					=		do_center_crop if do_center_crop is not None else self.do_center_crop
					lowercase						:		Dict   					=		crop_size if crop_size is not None else self.crop_size
					lowercase						:		Any   					=		get_size_dict(a_							)
					lowercase						:		List[Any]   					=		do_rescale if do_rescale is not None else self.do_rescale
					lowercase						:		str   					=		rescale_factor if rescale_factor is not None else self.rescale_factor
					lowercase						:		List[str]   					=		do_normalize if do_normalize is not None else self.do_normalize
					lowercase						:		List[str]   					=		image_mean if image_mean is not None else self.image_mean
					lowercase						:		str   					=		image_std if image_std is not None else self.image_std

					lowercase						:		str   					=		make_list_of_images(a_							)

					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."							)

					if do_resize and size is None:
						raise ValueError("Size 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.
					lowercase						:		Any   					=		[to_numpy_array(a_							) for image in images]

					if do_resize:
						lowercase						:		List[Any]   					=		[self.resize(image=a_							,      size=a_							,      resample=a_							) for image in images]

					if do_center_crop:
						lowercase						:		List[Any]   					=		[self.center_crop(image=a_							,      size=a_							) for image in images]

					if do_rescale:
						lowercase						:		List[Any]   					=		[self.rescale(image=a_							,      scale=a_							) for image in images]

					if do_normalize:
						lowercase						:		List[Any]   					=		[self.normalize(image=a_							,      mean=a_							,      std=a_							) for image in images]

					lowercase						:		List[Any]   					=		[to_channel_dimension_format(a_							,      a_							) for image in images]

					lowercase						:		Dict   					=		{"pixel_values": images}
					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


