






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


__snake_case      :      str             =       re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__snake_case      :      Optional[Any]             =       10
__snake_case      :      Tuple             =       2_56







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]						)					->				Optional[MinHash]:

						"""simple docstring"""



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







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

						"""simple docstring"""



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


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  *,
						    _UpperCamelCase = 0.85       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  duplication_jaccard_threshold
												lowerCAmelCase__        =  NUM_PERM
												lowerCAmelCase__        =  MinHashLSH(threshold=self._duplication_jaccard_threshold       ,  num_perm=self._num_perm				)

												lowerCAmelCase__        =  defaultdict(_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






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







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.get_duplicate_clusters()
												with open(_UpperCamelCase       ,  'w'				) as f:
																		json.dump(_UpperCamelCase       ,  _UpperCamelCase				)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  element
						lowerCAmelCase__        =  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_    :   Type[Dataset]						)					->				Union[str, 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_    :   Type[Dataset]      ,    UpperCamelCase_    :   float						)					->				List[str]:

						"""simple docstring"""



						lowerCAmelCase__        =  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_    :   str      ,    UpperCamelCase_    :   str						)					->				float:

						"""simple docstring"""



						lowerCAmelCase__        =  get_tokens(UpperCamelCase_						)
						lowerCAmelCase__        =  get_tokens(UpperCamelCase_						)
						return len(tokensa & tokensa						) / len(tokensa | tokensa						)


__snake_case      :      Optional[Any]             =       None







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Optional[int]						)					->				Dict:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Union[str, Any]						)					->				Union[str, Any]:

						"""simple docstring"""



						global _shared_dataset
						lowerCAmelCase__        =  dataset
						lowerCAmelCase__        =  []
						lowerCAmelCase__        =  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_    :   Type[Dataset]      ,    UpperCamelCase_    :   float = 0.85						)					->				Tuple[Type[Dataset], List[List[Dict]]]:

						"""simple docstring"""



						lowerCAmelCase__        =  make_duplicate_clusters(UpperCamelCase_      ,    UpperCamelCase_						)
						lowerCAmelCase__        =  {x['base_index'] for cluster in duplicate_clusters for x in cluster}
						lowerCAmelCase__        =  {}
						lowerCAmelCase__        =  find_extremes(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						for extremes in extremes_clusters:
												for element in extremes:
																		lowerCAmelCase__        =  element
						lowerCAmelCase__        =  duplicate_indices - set(extreme_dict.keys()						)
						lowerCAmelCase__        =  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:
																		lowerCAmelCase__        =  element['base_index'] in extreme_dict
																		if element["is_extreme"]:
																								lowerCAmelCase__        =  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













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


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''pixel_values''']






						def __init__(		self       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PILImageResampling.BILINEAR       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  _UpperCamelCase = 1 / 2_55       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(**_UpperCamelCase				)
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 2_56}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  resample
												lowerCAmelCase__        =  do_center_crop
												lowerCAmelCase__        =  crop_size
												lowerCAmelCase__        =  do_rescale
												lowerCAmelCase__        =  rescale_factor
												lowerCAmelCase__        =  do_normalize
												lowerCAmelCase__        =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCAmelCase__        =  image_std if image_std is not None else IMAGENET_STANDARD_STD






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = PILImageResampling.BICUBIC       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												if "shortest_edge" not in size:
																		raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
												lowerCAmelCase__        =  get_resize_output_image_size(_UpperCamelCase       ,  size=size['shortest_edge']       ,  default_to_square=_UpperCamelCase				)
												return resize(_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												return center_crop(_UpperCamelCase       ,  size=(size['height'], size['width'])       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return rescale(_UpperCamelCase       ,  scale=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												return normalize(_UpperCamelCase       ,  mean=_UpperCamelCase       ,  std=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = ChannelDimension.FIRST       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  do_resize if do_resize is not None else self.do_resize
												lowerCAmelCase__        =  size if size is not None else self.size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  resample if resample is not None else self.resample
												lowerCAmelCase__        =  do_center_crop if do_center_crop is not None else self.do_center_crop
												lowerCAmelCase__        =  crop_size if crop_size is not None else self.crop_size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_rescale if do_rescale is not None else self.do_rescale
												lowerCAmelCase__        =  rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCAmelCase__        =  do_normalize if do_normalize is not None else self.do_normalize
												lowerCAmelCase__        =  image_mean if image_mean is not None else self.image_mean
												lowerCAmelCase__        =  image_std if image_std is not None else self.image_std

												lowerCAmelCase__        =  make_list_of_images(_UpperCamelCase				)

												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.'				)

												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.
												lowerCAmelCase__        =  [to_numpy_array(_UpperCamelCase				) for image in images]

												if do_resize:
																		lowerCAmelCase__        =  [self.resize(image=_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase				) for image in images]

												if do_center_crop:
																		lowerCAmelCase__        =  [self.center_crop(image=_UpperCamelCase       ,  size=_UpperCamelCase				) for image in images]

												if do_rescale:
																		lowerCAmelCase__        =  [self.rescale(image=_UpperCamelCase       ,  scale=_UpperCamelCase				) for image in images]

												if do_normalize:
																		lowerCAmelCase__        =  [self.normalize(image=_UpperCamelCase       ,  mean=_UpperCamelCase       ,  std=_UpperCamelCase				) for image in images]

												lowerCAmelCase__        =  [to_channel_dimension_format(_UpperCamelCase       ,  _UpperCamelCase				) for image in images]

												lowerCAmelCase__        =  {'pixel_values': images}
												return BatchFeature(data=_UpperCamelCase       ,  tensor_type=_UpperCamelCase				)





