"""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


_a  :  int=				re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_a  :  int=				10
_a  :  Optional[Any]=				256
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str]						)							->    Optional[MinHash]:

	'''simple docstring'''





	if len(UpperCAmelCase_						) < MIN_NUM_TOKENS:
		return None
	__snake_case    :							Dict   	=				MinHash(num_perm=UpperCAmelCase_						)
	for token in set(UpperCAmelCase_						):
		min_hash.update(token.encode()						)
	return min_hash
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    Set[str]:

	'''simple docstring'''





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

class 	UpperCamelCase    :



				def __init__(self       :   str			,						*,
				    _A       :   float = 0.85			,						)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				duplication_jaccard_threshold
					__snake_case    :							int   	=				NUM_PERM
					__snake_case    :							int   	=				MinHashLSH(threshold=self._duplication_jaccard_threshold			,						num_perm=self._num_perm)

					__snake_case    :							List[Any]   	=				defaultdict(_A)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Tuple			,						_A       :   MinHash)					->			None:
					__snake_case    :							Any   	=				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       _lowercase  (self       :   List[Any])					->			List[List[Dict]]:
					__snake_case    :							str   	=				[]
					for base, duplicates in self._duplicate_clusters.items():
						__snake_case    :							Any   	=				[base] + list(_A)
						# reformat the cluster to be a list of dict
						__snake_case    :							int   	=				[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
						duplicate_clusters.append(_A)
					return duplicate_clusters






				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			None:
					__snake_case    :							Any   	=				self.get_duplicate_clusters()
					with open(_A			,						'w') as f:
						json.dump(_A			,						_A)







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict						)							->    Union[str, Any]:

	'''simple docstring'''





	__snake_case	,       __snake_case    :							Optional[Any]   	=				element
	__snake_case    :							List[Any]   	=				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       (					UpperCAmelCase_					:       Type[Dataset]						)							->    Union[str, Any]:

	'''simple docstring'''





	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash    ,							ThreadedIterator(UpperCAmelCase_    ,							max_queue_size=1_00_00						)    ,							chunksize=1_00    ,							):
			if data is not None:
				yield data
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Type[Dataset]    ,							UpperCAmelCase_					:       float						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				DuplicationIndex(duplication_jaccard_threshold=UpperCAmelCase_						)

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

	# Returns a List[Cluster] where Cluster is List[str] with the filenames.
	return di.get_duplicate_clusters()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       str						)							->    float:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				get_tokens(UpperCAmelCase_						)
	__snake_case    :							str   	=				get_tokens(UpperCAmelCase_						)
	return len(tokensa & tokensa						) / len(tokensa | tokensa						)


_a  :  Any=				None
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Optional[Any]   	=				[]
	for elementa in cluster:
		__snake_case    :							Tuple   	=				_shared_dataset[elementa['base_index']]['content']
		for elementa in extremes:
			__snake_case    :							str   	=				_shared_dataset[elementa['base_index']]['content']
			if jaccard_similarity(UpperCAmelCase_    ,							UpperCAmelCase_						) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			__snake_case    :							Dict   	=				1
			extremes.append(UpperCAmelCase_						)
	return extremes
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       Union[str, Any]						)							->    str:

	'''simple docstring'''





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

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

	'''simple docstring'''





	__snake_case    :							Any   	=				make_duplicate_clusters(UpperCAmelCase_    ,							UpperCAmelCase_						)
	__snake_case    :							Any   	=				{x['base_index'] for cluster in duplicate_clusters for x in cluster}
	__snake_case    :							int   	=				{}
	__snake_case    :							Tuple   	=				find_extremes(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
	for extremes in extremes_clusters:
		for element in extremes:
			__snake_case    :							int   	=				element
	__snake_case    :							Union[str, Any]   	=				duplicate_indices - set(extreme_dict.keys()						)
	__snake_case    :							Optional[Any]   	=				dataset.filter(lambda UpperCAmelCase_    ,							UpperCAmelCase_						: idx not in remove_indices    ,							with_indices=UpperCAmelCase_						)

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			__snake_case    :							int   	=				element['base_index'] in extreme_dict
			if element["is_extreme"]:
				__snake_case    :							List[str]   	=				extreme_dict[element['base_index']]['copies']

	print(F"Original dataset size: {len(UpperCAmelCase_						)}"						)
	print(F"Number of duplicate clusters: {len(UpperCAmelCase_						)}"						)
	print(F"Files in duplicate cluster: {len(UpperCAmelCase_						)}"						)
	print(F"Unique files in duplicate cluster: {len(UpperCAmelCase_						)}"						)
	print(F"Filtered dataset size: {len(UpperCAmelCase_						)}"						)

	return ds_filter, duplicate_clusters






"""simple docstring"""







import math

import tensorflow as tf
from packaging import version
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    Any:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							List[str]   	=				0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)    ,							x.dtype						)						))

	return x * cdf
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Any   	=				tf.cast(math.pi    ,							x.dtype						)
	__snake_case    :							List[Any]   	=				tf.cast(0.044_715    ,							x.dtype						)
	__snake_case    :							Union[str, Any]   	=				0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(UpperCAmelCase_    ,							3						))						))

	return x * cdf
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				tf.convert_to_tensor(UpperCAmelCase_						)

	return x * tf.tanh(tf.math.softplus(UpperCAmelCase_						)						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    List[str]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				tf.cast(0.044_715    ,							x.dtype						)
	__snake_case    :							int   	=				tf.cast(0.7_978_845_608    ,							x.dtype						)

	return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							Any   	=				tf.convert_to_tensor(UpperCAmelCase_						)
	__snake_case    :							Dict   	=				tf.cast(1.702    ,							x.dtype						)
	return x * tf.math.sigmoid(coeff * x						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    List[Any]:

	'''simple docstring'''





	return tf.clip_by_value(_gelu(UpperCAmelCase_						)    ,							-10    ,							10						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       Any=-1						)							->    int:

	'''simple docstring'''





	__snake_case	,       __snake_case    :							str   	=				tf.split(UpperCAmelCase_    ,							2    ,							axis=UpperCAmelCase_						)
	return a * tf.math.sigmoid(UpperCAmelCase_						)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):
	def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    int:

		'''simple docstring'''





		return tf.keras.activations.gelu(UpperCAmelCase_    ,							approximate=UpperCAmelCase_						)

	_a  :  Tuple=				tf.keras.activations.gelu

	_a  :  Tuple=				approximate_gelu_wrap
else:
	_a  :  Any=				_gelu
	_a  :  str=				_gelu_new


_a  :  List[str]=				{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    List[Any]:

	'''simple docstring'''





	if activation_string in ACTaFN:
		return ACTaFN[activation_string]
	else:
		raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"						)





