
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__	:int										=					{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:str										=					["RobertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:List[str]										=					[
							    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "RobertaForCausalLM",
							    "RobertaForMaskedLM",
							    "RobertaForMultipleChoice",
							    "RobertaForQuestionAnswering",
							    "RobertaForSequenceClassification",
							    "RobertaForTokenClassification",
							    "RobertaModel",
							    "RobertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:List[Any]										=					[
							    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFRobertaForCausalLM",
							    "TFRobertaForMaskedLM",
							    "TFRobertaForMultipleChoice",
							    "TFRobertaForQuestionAnswering",
							    "TFRobertaForSequenceClassification",
							    "TFRobertaForTokenClassification",
							    "TFRobertaMainLayer",
							    "TFRobertaModel",
							    "TFRobertaPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Any										=					[
							    "FlaxRobertaForCausalLM",
							    "FlaxRobertaForMaskedLM",
							    "FlaxRobertaForMultipleChoice",
							    "FlaxRobertaForQuestionAnswering",
							    "FlaxRobertaForSequenceClassification",
							    "FlaxRobertaForTokenClassification",
							    "FlaxRobertaModel",
							    "FlaxRobertaPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
							from .tokenization_roberta import RobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roberta_fast import RobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta import (
														    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaForCausalLM,
														    RobertaForMaskedLM,
														    RobertaForMultipleChoice,
														    RobertaForQuestionAnswering,
														    RobertaForSequenceClassification,
														    RobertaForTokenClassification,
														    RobertaModel,
														    RobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta import (
														    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaForCausalLM,
														    TFRobertaForMaskedLM,
														    TFRobertaForMultipleChoice,
														    TFRobertaForQuestionAnswering,
														    TFRobertaForSequenceClassification,
														    TFRobertaForTokenClassification,
														    TFRobertaMainLayer,
														    TFRobertaModel,
														    TFRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta import (
														    FlaxRobertaForCausalLM,
														    FlaxRobertaForMaskedLM,
														    FlaxRobertaForMultipleChoice,
														    FlaxRobertaForQuestionAnswering,
														    FlaxRobertaForSequenceClassification,
														    FlaxRobertaForTokenClassification,
														    FlaxRobertaModel,
														    FlaxRobertaPreTrainedModel,
														)

else:
							import sys

							lowercase__	:Dict										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






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__	:Optional[Any]										=					re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase__	:List[str]										=					10
lowercase__	:Tuple										=					256



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < MIN_NUM_TOKENS:
										return None
					lowercase					=		MinHash(num_perm=lowerCAmelCase__	)
					for token in set(lowerCAmelCase__	):
										min_hash.update(token.encode()	)
					return min_hash



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return {t for t in NON_ALPHA.split(lowerCAmelCase__	) if len(t.strip()	) > 0}







class        lowercase	:




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

												lowercase					=		defaultdict(A__)




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





							def 				A__   (       self     ,A__):
												lowercase					=		self.get_duplicate_clusters()
												with open(A__     ,'''w''') as f:
																	json.dump(A__     ,A__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		element
					lowercase					=		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					(							lowerCAmelCase__	):
					'''simple docstring'''
					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(lowerCAmelCase__				,    max_queue_size=1_0000	)				,    chunksize=100				,    ):
															if data is not None:
																				yield data



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		get_tokens(lowerCAmelCase__	)
					lowercase					=		get_tokens(lowerCAmelCase__	)
					return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowercase__	:List[Any]										=					None



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



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					global _shared_dataset
					lowercase					=		dataset
					lowercase					=		[]
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.85	):
					'''simple docstring'''
					lowercase					=		make_duplicate_clusters(lowerCAmelCase__				,    lowerCAmelCase__	)
					lowercase					=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
					lowercase					=		{}
					lowercase					=		find_extremes(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
					for extremes in extremes_clusters:
										for element in extremes:
															lowercase					=		element
					lowercase					=		duplicate_indices - set(extreme_dict.keys()	)
					lowercase					=		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:
															lowercase					=		element['''base_index'''] in extreme_dict
															if element["is_extreme"]:
																				lowercase					=		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




