



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







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[MinHash]:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     Set[str]:



				"""simple docstring"""





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


class 	__snake_case   :
		def __init__(							self    ,*,
		    a_ = 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(a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_						):







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







						"""simple docstring"""
						lowerCAmelCase__		  =  []
						for base, duplicates in self._duplicate_clusters.items():
										lowerCAmelCase__		  =  [base] + list(a_						)
										# 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(a_						)
						return duplicate_clusters



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







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







def     UpperCAmelCase_		(					snake_case__					)	->     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_		(					snake_case__					)	->     List[Any]:



				"""simple docstring"""





				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash    ,			ThreadedIterator(snake_case__    ,			max_queue_size=10000					)    ,			chunksize=100    ,			):
												if data is not None:
																yield data







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[str]:



				"""simple docstring"""





				lowerCAmelCase__		  =  DuplicationIndex(duplication_jaccard_threshold=snake_case__					)

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

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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     float:



				"""simple docstring"""





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


_lowerCAmelCase	:   Union[str, Any]    =   None







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""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(snake_case__    ,			snake_case__					) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												lowerCAmelCase__		  =  1
												extremes.append(snake_case__					)
				return extremes







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				global _shared_dataset
				lowerCAmelCase__		  =  dataset
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  partial(_find_cluster_extremes_shared    ,			jaccard_threshold=snake_case__					)
				with mp.Pool() as pool:
								for extremes in tqdm(
								    pool.imap_unordered(
								        snake_case__    ,			snake_case__    ,			)    ,			total=len(snake_case__					)    ,			):
												extremes_list.append(snake_case__					)
				return extremes_list







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



				"""simple docstring"""





				lowerCAmelCase__		  =  make_duplicate_clusters(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  {x['base_index'] for cluster in duplicate_clusters for x in cluster}
				lowerCAmelCase__		  =  {}
				lowerCAmelCase__		  =  find_extremes(snake_case__    ,			snake_case__    ,			snake_case__					)
				for extremes in extremes_clusters:
								for element in extremes:
												lowerCAmelCase__		  =  element
				lowerCAmelCase__		  =  duplicate_indices - set(extreme_dict.keys()					)
				lowerCAmelCase__		  =  dataset.filter(lambda snake_case__    ,			snake_case__					: idx not in remove_indices    ,			with_indices=snake_case__					)

				# 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(snake_case__					)}'					)
				print(f'Number of duplicate clusters: {len(snake_case__					)}'					)
				print(f'Files in duplicate cluster: {len(snake_case__					)}'					)
				print(f'Unique files in duplicate cluster: {len(snake_case__					)}'					)
				print(f'Filtered dataset size: {len(snake_case__					)}'					)

				return ds_filter, duplicate_clusters





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  tempfile.mkdtemp()

						# fmt: off
						lowerCAmelCase__		  =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']						)
						with open(self.vocab_file    ,'w'    ,encoding='utf-8'						) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)

						lowerCAmelCase__		  =  {
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,a_						)
						with open(self.image_processor_file    ,'w'    ,encoding='utf-8'						) as fp:
										json.dump(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return BertTokenizer.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return ViTImageProcessor.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						shutil.rmtree(self.tmpdirname						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [np.random.randint(255    ,size=(3, 30, 400)    ,dtype=np.uinta						)]

						lowerCAmelCase__		  =  [Image.fromarray(np.moveaxis(a_    ,0    ,-1						)						) for x in image_inputs]

						return image_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_tokenizer()
						lowerCAmelCase__		  =  self.get_image_processor()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						processor.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()    ,image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						lowerCAmelCase__		  =  self.get_tokenizer(bos_token='(BOS)'    ,eos_token='(EOS)'						)
						lowerCAmelCase__		  =  self.get_image_processor(do_normalize=a_    ,padding_value=1.0						)

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname    ,bos_token='(BOS)'    ,eos_token='(EOS)'    ,do_normalize=a_    ,padding_value=1.0						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  image_processor(a_    ,return_tensors='np'						)
						lowerCAmelCase__		  =  processor(images=a_    ,return_tensors='np'						)

						for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()    ,input_processor[key].sum()    ,delta=1e-2						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'

						lowerCAmelCase__		  =  processor(text=a_						)

						lowerCAmelCase__		  =  tokenizer(a_						)

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]    ,encoded_processor[key]						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

						self.assertListEqual(list(inputs.keys()						)    ,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

						# test if it raises when no input is passed
						with self.assertRaises(a_						):
										processor()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__		  =  processor.batch_decode(a_						)
						lowerCAmelCase__		  =  tokenizer.batch_decode(a_						)

						self.assertListEqual(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

						self.assertListEqual(list(inputs.keys()						)    ,processor.model_input_names						)
