


"""simple docstring"""




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


lowercase_			     =  logging.get_logger(__name__)

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     Union[int, Iterable[int]]    ,					lowerCAmelCase__ :     bool    ,					lowerCAmelCase__ :     int			) ->      Tuple[int, int]:
		def constraint_to_multiple_of(lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     List[str]=0    ,					lowerCAmelCase__ :     List[str]=None			):
				__a           =			round(val / multiple			) * multiple

				if max_val is not None and x > max_val:
						__a           =			math.floor(val / multiple			) * multiple

				if x < min_val:
						__a           =			math.ceil(val / multiple			) * multiple

				return x

		__a           =			(output_size, output_size) if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) else output_size

		__a      ,     __a           =			get_image_size(lowerCAmelCase__			)
		__a      ,     __a           =			output_size

		# determine new height and width
		__a           =			output_height / input_height
		__a           =			output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width			) < abs(1 - scale_height			):
						# fit width
						__a           =			scale_width
				else:
						# fit height
						__a           =			scale_height

		__a           =			constraint_to_multiple_of(scale_height * input_height    ,					multiple=lowerCAmelCase__			)
		__a           =			constraint_to_multiple_of(scale_width * input_width    ,					multiple=lowerCAmelCase__			)

		return (new_height, new_width)




class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Dict										=				['pixel_values']

		def __init__(  self							, _a = True							, _a = None							, _a = PILImageResampling.BILINEAR							, _a = False							, _a = 1							, _a = True							, _a = 1 / 255							, _a = True							, _a = None							, _a = None							, **_a							, ):
				super().__init__(**_a      )
				__a           =			size if size is not None else {'''height''': 384, '''width''': 384}
				__a           =			get_size_dict(_a      )
				__a           =			do_resize
				__a           =			size
				__a           =			keep_aspect_ratio
				__a           =			ensure_multiple_of
				__a           =			resample
				__a           =			do_rescale
				__a           =			rescale_factor
				__a           =			do_normalize
				__a           =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__a           =			image_std if image_std is not None else IMAGENET_STANDARD_STD

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = False							, _a = 1							, _a = PILImageResampling.BICUBIC							, _a = None							, **_a							, ):
				__a           =			get_size_dict(_a      )
				if "height" not in size or "width" not in size:
						raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''      )
				__a           =			get_resize_output_image_size(
				    _a							, output_size=(size['''height'''], size['''width'''])							, keep_aspect_ratio=_a							, multiple=_a							, )
				return resize(_a							, size=_a							, resample=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a = None							, **_a							, ):
				return rescale(_a							, scale=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a = None							, **_a							, ):
				return normalize(_a							, mean=_a							, std=_a							, data_format=_a							, **_a      )

		def 			__UpperCAmelCase       (  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							, ):
				__a           =			do_resize if do_resize is not None else self.do_resize
				__a           =			size if size is not None else self.size
				__a           =			get_size_dict(_a      )
				__a           =			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
				__a           =			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
				__a           =			resample if resample is not None else self.resample
				__a           =			do_rescale if do_rescale is not None else self.do_rescale
				__a           =			rescale_factor if rescale_factor is not None else self.rescale_factor
				__a           =			do_normalize if do_normalize is not None else self.do_normalize
				__a           =			image_mean if image_mean is not None else self.image_mean
				__a           =			image_std if image_std is not None else self.image_std

				__a           =			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 or resample is None:
						raise ValueError('''Size and resample must be specified if do_resize 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.
				__a           =			[to_numpy_array(_a      ) for image in images]

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

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

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

				__a           =			[to_channel_dimension_format(_a							, _a      ) for image in images]

				__a           =			{'''pixel_values''': images}
				return BatchFeature(data=_a							, tensor_type=_a      )





		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
						if len(_a      ) != len(_a      ):
								raise ValueError(
								    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''      )

						if is_torch_tensor(_a      ):
								__a           =			target_sizes.numpy()

						__a           =			[]

						for idx in range(len(_a      )      ):
								__a           =			torch.nn.functional.interpolate(
								    logits[idx].unsqueeze(dim=0      )							, size=target_sizes[idx]							, mode='''bilinear'''							, align_corners=_a      )
								__a           =			resized_logits[0].argmax(dim=0      )
								semantic_segmentation.append(_a      )
				else:
						__a           =			logits.argmax(dim=1      )
						__a           =			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

				return semantic_segmentation










"""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


lowercase_			     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_			     =  1_0
lowercase_			     =  2_5_6

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

def        lowercase							(		lowerCAmelCase__ :     str			) ->      Set[str]:
		return {t for t in NON_ALPHA.split(lowerCAmelCase__			) if len(t.strip()			) > 0}
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, *,
		    _a = 0.85							, ):
				__a           =			duplication_jaccard_threshold
				__a           =			NUM_PERM
				__a           =			MinHashLSH(threshold=self._duplication_jaccard_threshold							, num_perm=self._num_perm      )

				__a           =			defaultdict(_a      )

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





		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.get_duplicate_clusters()
				with open(_a							, '''w'''      ) as f:
						json.dump(_a							, _a      )







def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      int:
		__a      ,     __a           =			element
		__a           =			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        lowercase							(		lowerCAmelCase__ :     Type[Dataset]			) ->      str:
		with mp.Pool() as pool:
				for data in pool.imap_unordered(
				    _compute_min_hash    ,					ThreadedIterator(lowerCAmelCase__    ,					max_queue_size=10000			)    ,					chunksize=100    ,					):
						if data is not None:
								yield data

def        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float			) ->      Dict:
		__a           =			DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase__			)

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

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

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      float:
		__a           =			get_tokens(lowerCAmelCase__			)
		__a           =			get_tokens(lowerCAmelCase__			)
		return len(tokensa & tokensa			) / len(tokensa | tokensa			)


lowercase_			     =  None

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

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[int]			) ->      Optional[int]:
		global _shared_dataset
		__a           =			dataset
		__a           =			[]
		__a           =			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        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float = 0.85			) ->      Tuple[Type[Dataset], List[List[Dict]]]:
		__a           =			make_duplicate_clusters(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
		__a           =			{}
		__a           =			find_extremes(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		for extremes in extremes_clusters:
				for element in extremes:
						__a           =			element
		__a           =			duplicate_indices - set(extreme_dict.keys()			)
		__a           =			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:
						__a           =			element['''base_index'''] in extreme_dict
						if element["is_extreme"]:
								__a           =			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






