


'''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      :      Dict            =					re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
_lowercase      :      Optional[int]            =					10
_lowercase      :      Dict            =					256


def 				lowerCamelCase__				(	A       :      List[str]					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






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






class 	UpperCamelCase__:




				def __init__(		self      :			Dict				,   *,
				    lowerCAmelCase      :			float = 0.85				,   )->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     duplication_jaccard_threshold
										UpperCAmelCase					    =     NUM_PERM
										UpperCAmelCase					    =     MinHashLSH(threshold=self._duplication_jaccard_threshold				,   num_perm=self._num_perm				)

										UpperCAmelCase					    =     defaultdict(lowerCAmelCase				)




				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			MinHash				)->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     self._index.query(lowerCAmelCase				)
										if code_key in self._index.keys:
																print(F"""Duplicate key {code_key}"""				)
																return

										self._index.insert(lowerCAmelCase				,   lowerCAmelCase				)
										if len(lowerCAmelCase				) > 0:
																for base_duplicate in close_duplicates:
																						if base_duplicate in self._duplicate_clusters:
																												self._duplicate_clusters[base_duplicate].add(lowerCAmelCase				)
																												break
																else:
																						self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase				)




				def 					a__(		self      :			Tuple				)->					List[List[Dict]]:

										"""simple docstring"""




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





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			str				)->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_duplicate_clusters()
										with open(lowerCAmelCase				,   '''w'''				) as f:
																json.dump(lowerCAmelCase				,   lowerCAmelCase				)







def 				lowerCamelCase__				(	A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase    ,    UpperCAmelCase					    =     element
						UpperCAmelCase					    =     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__				(	A       :      Type[Dataset]					):




						'''simple docstring'''






						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash				,       ThreadedIterator(A				,       max_queue_size=1_00_00					)				,       chunksize=1_00				,       ):
																		if data is not None:
																								yield data


def 				lowerCamelCase__				(	A       :      Type[Dataset]				,       A       :      float					):




						'''simple docstring'''






						UpperCAmelCase					    =     DuplicationIndex(duplication_jaccard_threshold=A					)

						for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A					)					)				,       max_queue_size=1_00					)					):
												di.add(A				,       A					)

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


def 				lowerCamelCase__				(	A       :      str				,       A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     get_tokens(A					)
						UpperCAmelCase					    =     get_tokens(A					)
						return len(tokensa & tokensa					) / len(tokensa | tokensa					)


_lowercase      :      Union[str, Any]            =					None


def 				lowerCamelCase__				(	A       :      Optional[Any]				,       A       :      Optional[Any]					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      List[Any]				,       A       :      int					):




						'''simple docstring'''






						global _shared_dataset
						UpperCAmelCase					    =     dataset
						UpperCAmelCase					    =     []
						UpperCAmelCase					    =     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 				lowerCamelCase__				(	A       :      Type[Dataset]				,       A       :      float = 0.85					):




						'''simple docstring'''






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









'''simple docstring'''



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
	import os

	import jax.numpy as jnp
	from jax import jit

	from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
	from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

	_lowercase      :      List[str]            =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch


def 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if rng is None:
												UpperCAmelCase					    =     random.Random()

						UpperCAmelCase					    =     1
						for dim in shape:
												total_dims *= dim

						UpperCAmelCase					    =     []
						for _ in range(A					):
												values.append(rng.randint(0				,       vocab_size - 1					)					)

						UpperCAmelCase					    =     np.array(A				,       dtype=jnp.intaa					).reshape(A					)

						return output


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[int]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     ids_tensor(A				,       vocab_size=2				,       rng=A					)
						# make sure that at least one token is attended to for each batch
						UpperCAmelCase					    =     1
						return attn_mask






@require_flax
class 	UpperCamelCase__:
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[Any]  		=	()




				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										# cut to half length & take max batch_size 3
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     inputs['''input_ids'''].shape[-1] // 2
										UpperCAmelCase					    =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

										UpperCAmelCase					    =     jnp.ones_like(lowerCAmelCase				)
										UpperCAmelCase					    =     attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										UpperCAmelCase					    =     input_ids.shape[-1] + 5
										if config.eos_token_id is not None and config.pad_token_id is None:
																# hack to allow generate for models such as GPT2 as is done in `generate()`
																UpperCAmelCase					    =     config.eos_token_id
										return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     pt_model_class(lowerCAmelCase				).eval()
																UpperCAmelCase					    =     load_flax_weights_in_pytorch_model(lowerCAmelCase				,   flax_model.params				)

																UpperCAmelCase					    =     flax_model.generate(lowerCAmelCase				).sequences
																UpperCAmelCase					    =     pt_model.generate(torch.tensor(lowerCAmelCase				,   dtype=torch.long				)				)

																if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						UpperCAmelCase					    =     flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																self.assertListEqual(pt_generation_outputs.numpy().tolist()				,   flax_generation_outputs.tolist()				)




				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[0]				,   input_ids.shape[0] * config.num_return_sequences				)




				def 					a__(		self      :			Tuple				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0.8
										UpperCAmelCase					    =     10
										UpperCAmelCase					    =     0.3
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Union[str, Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)





				def 					a__(		self      :			Tuple				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)







@require_flax
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
										UpperCAmelCase					    =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

										UpperCAmelCase					    =     '''Hello world'''
										UpperCAmelCase					    =     tokenizer(lowerCAmelCase				,   return_tensors='''np'''				).input_ids

										# typos are quickly detected (the correct argument is `do_sample`)
										with self.assertRaisesRegex(lowerCAmelCase				,   '''do_samples'''				):
																model.generate(lowerCAmelCase				,   do_samples=lowerCAmelCase				)

										# arbitrary arguments that will not be used anywhere are also not accepted
										with self.assertRaisesRegex(lowerCAmelCase				,   '''foo'''				):
																UpperCAmelCase					    =     {'''foo''': '''bar'''}
																model.generate(lowerCAmelCase				,   **lowerCAmelCase				)





