





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 						=      256





def lowerCAmelCase__(__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 lowerCAmelCase__(__snake_case							)				->      Set[str]:







	'''simple docstring'''







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






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  *,
			    __lowerCAmelCase = 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(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				for base, duplicates in self._duplicate_clusters.items():
					lowerCamelCase__								=		[base] + list(__lowerCAmelCase							)
					# 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(__lowerCAmelCase							)
				return duplicate_clusters





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







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





def lowerCAmelCase__(__snake_case							)				->      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 lowerCAmelCase__(__snake_case							)				->      List[str]:







	'''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 lowerCAmelCase__(__snake_case       ,__snake_case							)				->      List[Any]:







	'''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 lowerCAmelCase__(__snake_case       ,__snake_case							)				->      float:







	'''simple docstring'''







	lowerCamelCase__								=		get_tokens(__snake_case							)
	lowerCamelCase__								=		get_tokens(__snake_case							)
	return len(tokensa & tokensa							) / len(tokensa | tokensa							)


_a 						=      None





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Any:







	'''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 lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case							)				->      Tuple:







	'''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 lowerCAmelCase__(__snake_case       ,__snake_case = 0.8_5							)				->      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










from typing import TYPE_CHECKING

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


_a 						=      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      ["LxmertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "LxmertEncoder",
			    "LxmertForPreTraining",
			    "LxmertForQuestionAnswering",
			    "LxmertModel",
			    "LxmertPreTrainedModel",
			    "LxmertVisualFeatureEncoder",
			    "LxmertXLayer",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFLxmertForPreTraining",
			    "TFLxmertMainLayer",
			    "TFLxmertModel",
			    "TFLxmertPreTrainedModel",
			    "TFLxmertVisualFeatureEncoder",
			]


if TYPE_CHECKING:
			from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
			from .tokenization_lxmert import LxmertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_lxmert_fast import LxmertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_lxmert import (
						    LxmertEncoder,
						    LxmertForPreTraining,
						    LxmertForQuestionAnswering,
						    LxmertModel,
						    LxmertPreTrainedModel,
						    LxmertVisualFeatureEncoder,
						    LxmertXLayer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_lxmert import (
						    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLxmertForPreTraining,
						    TFLxmertMainLayer,
						    TFLxmertModel,
						    TFLxmertPreTrainedModel,
						    TFLxmertVisualFeatureEncoder,
						)

else:
			import sys

			_a 						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



