
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_A     =  get_tests_dir("fixtures/test_sentencepiece.model")

_A     =  {"target_lang": "fi", "source_lang": "en"}
_A     =  ">>zh<<"
_A     =  "Helsinki-NLP/"

if is_torch_available():
			_A     =  "pt"
elif is_tf_available():
			_A     =  "tf"
else:
			_A     =  "jax"





@require_sentencepiece
class 			_lowerCAmelCase      (			__a      ,   unittest.TestCase ):
				_lowercase    	=MarianTokenizer
				_lowercase    	=False
				_lowercase    	=True




				def   __a		(      self  )       ->   Optional[Any]:
							super().setUp()
							lowerCAmelCase_      			=			["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
							lowerCAmelCase_      			=			dict(zip(_UpperCamelCase       ,       range(len(_UpperCamelCase  )  )  )  )
							lowerCAmelCase_      			=			Path(self.tmpdirname  )
							save_json(_UpperCamelCase       ,       save_dir / VOCAB_FILES_NAMES["vocab"]  )
							save_json(_UpperCamelCase       ,       save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]  )
							if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
										copyfile(_UpperCamelCase       ,       save_dir / VOCAB_FILES_NAMES["source_spm"]  )
										copyfile(_UpperCamelCase       ,       save_dir / VOCAB_FILES_NAMES["target_spm"]  )

							lowerCAmelCase_      			=			MarianTokenizer.from_pretrained(self.tmpdirname  )
							tokenizer.save_pretrained(self.tmpdirname  )




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   MarianTokenizer:
							return MarianTokenizer.from_pretrained(self.tmpdirname       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Any:
							return (
							    "This is a test",
							    "This is a test",
							)




				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			"</s>"
							lowerCAmelCase_      			=			0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase  )       ,       _UpperCamelCase  )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase  )       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			list(self.get_tokenizer().get_vocab().keys()  )

							self.assertEqual(vocab_keys[0]       ,       "</s>"  )
							self.assertEqual(vocab_keys[1]       ,       "<unk>"  )
							self.assertEqual(vocab_keys[-1]       ,       "<pad>"  )
							self.assertEqual(len(_UpperCamelCase  )       ,       9  )




				def   __a		(      self  )       ->   str:
							self.assertEqual(self.get_tokenizer().vocab_size       ,       9  )




				def   __a		(      self  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""  )
							lowerCAmelCase_      			=			en_de_tokenizer(["I am a small frog"]       ,       return_tensors=_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			[38, 121, 14, 697, 38_848, 0]
							self.assertListEqual(_UpperCamelCase       ,       batch.input_ids[0]  )

							lowerCAmelCase_      			=			tempfile.mkdtemp()
							en_de_tokenizer.save_pretrained(_UpperCamelCase  )
							lowerCAmelCase_      			=			[x.name for x in Path(_UpperCamelCase  ).glob("*"  )]
							self.assertIn("source.spm"       ,       _UpperCamelCase  )
							MarianTokenizer.from_pretrained(_UpperCamelCase  )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.get_tokenizer()

							lowerCAmelCase_      			=			tok(
							    ["I am a small frog" * 1_000, "I am a small frog"]       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase       ,       return_tensors=_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )
							self.assertEqual(batch.input_ids.shape       ,       (2, 512)  )




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			self.get_tokenizer()
							lowerCAmelCase_      			=			tok(["I am a tiny frog", "I am a small frog"]       ,       padding=_UpperCamelCase       ,       return_tensors=_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )
							self.assertEqual(batch_smaller.input_ids.shape       ,       (2, 10)  )




				@slow
				def   __a		(      self  )       ->   List[Any]:
							# fmt: off
							lowerCAmelCase_      			=			{"input_ids": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_UpperCamelCase       ,       model_name="Helsinki-NLP/opus-mt-en-de"       ,       revision="1a8c2263da11e68e50938f97e10cd57820bd504c"       ,       decode_kwargs={"use_source_tokenizer": True}       ,       )




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"  )

							lowerCAmelCase_      			=			"Tämä on testi"
							lowerCAmelCase_      			=			"This is a test"

							lowerCAmelCase_      			=			[76, 7, 2_047, 2]
							lowerCAmelCase_      			=			[69, 12, 11, 940, 2]

							lowerCAmelCase_      			=			tokenizer(_UpperCamelCase  ).input_ids
							self.assertListEqual(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			tokenizer(text_target=_UpperCamelCase  ).input_ids
							self.assertListEqual(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			tokenizer.decode(_UpperCamelCase       ,       skip_special_tokens=_UpperCamelCase  )
							self.assertEqual(_UpperCamelCase       ,       _UpperCamelCase  )





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


_A     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_A     =  10
_A     =  2_56






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


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





class 			_lowerCAmelCase      :




				def __init__(      self       ,       *,
				    _UpperCamelCase = 0.85       ,       )       ->   Dict:
							lowerCAmelCase_      			=			duplication_jaccard_threshold
							lowerCAmelCase_      			=			NUM_PERM
							lowerCAmelCase_      			=			MinHashLSH(threshold=self._duplication_jaccard_threshold       ,       num_perm=self._num_perm  )

							lowerCAmelCase_      			=			defaultdict(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							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   __a		(      self  )       ->   List[List[Dict]]:
							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   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.get_duplicate_clusters()
							with open(_UpperCamelCase       ,       "w"  ) as f:
										json.dump(_UpperCamelCase       ,       _UpperCamelCase  )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple       ):




			"""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      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]       ):




			"""simple docstring"""


			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      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			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      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_A     =  None






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						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(__lowerCAmelCase					,							__lowerCAmelCase       ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									lowerCAmelCase_      			=			1
									extremes.append(__lowerCAmelCase       )
			return extremes






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			global _shared_dataset
			lowerCAmelCase_      			=			dataset
			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			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      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float = 0.85       ):




			"""simple docstring"""


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



