

'''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


_snake_case   :   str     							=	re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
_snake_case   :   List[Any]     							=	10
_snake_case   :   Optional[Any]     							=	256
def 	snake_case_					(UpperCamelCase :						List[str]   ):





							'''simple docstring'''


							if len(UpperCamelCase   ) < MIN_NUM_TOKENS:
														return None
							_a			  =       MinHash(num_perm=UpperCamelCase   )
							for token in set(UpperCamelCase   ):
														min_hash.update(token.encode()   )
							return min_hash
def 	snake_case_					(UpperCamelCase :						str   ):





							'''simple docstring'''


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

class     A					:







							def __init__(						self :						str							,    *,
							    lowerCAmelCase_ :						float = 0.8_5							,    )    ->							Optional[int]:


														"""simple docstring"""


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

														_a			  =       defaultdict(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						MinHash      )    ->							None:


														"""simple docstring"""


														_a			  =       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 :						Any      )    ->							List[List[Dict]]:


														"""simple docstring"""


														_a			  =       []
														for base, duplicates in self._duplicate_clusters.items():
																					_a			  =       [base] + list(lowerCAmelCase_      )
																					# reformat the cluster to be a list of dict
																					_a			  =       [{'''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 :						List[str]							,    lowerCAmelCase_ :						Tuple      )    ->							None:


														"""simple docstring"""


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



def 	snake_case_					(UpperCamelCase :						Dict   ):





							'''simple docstring'''


							_a  ,							_a			  =       element
							_a			  =       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 	snake_case_					(UpperCamelCase :						Type[Dataset]   ):





							'''simple docstring'''


							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash				,		ThreadedIterator(UpperCamelCase				,		max_queue_size=1_0000   )				,		chunksize=100				,		):
																					if data is not None:
																												yield data
def 	snake_case_					(UpperCamelCase :						Type[Dataset]				,		UpperCamelCase :						float   ):





							'''simple docstring'''


							_a			  =       DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase   )

							for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(UpperCamelCase   )   )				,		max_queue_size=100   )   ):
														di.add(UpperCamelCase				,		UpperCamelCase   )

							# Returns a List[Cluster] where Cluster is List[str] with the filenames.
							return di.get_duplicate_clusters()
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       get_tokens(UpperCamelCase   )
							_a			  =       get_tokens(UpperCamelCase   )
							return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


_snake_case   :   Union[str, Any]     							=	None
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       []
							for elementa in cluster:
														_a			  =       _shared_dataset[elementa['''base_index''']]['''content''']
														for elementa in extremes:
																					_a			  =       _shared_dataset[elementa['''base_index''']]['''content''']
																					if jaccard_similarity(UpperCamelCase				,		UpperCamelCase   ) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					_a			  =       1
																					extremes.append(UpperCamelCase   )
							return extremes
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							global _shared_dataset
							_a			  =       dataset
							_a			  =       []
							_a			  =       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 	snake_case_					(UpperCamelCase :						Type[Dataset]				,		UpperCamelCase :						float = 0.85   ):





							'''simple docstring'''


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


from manim import *

class     A					(	_a					):







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Dict:


														"""simple docstring"""


														_a			  =       Rectangle(height=0.5							,    width=0.5      )
														_a			  =       Rectangle(height=0.4_6							,    width=0.4_6      ).set_stroke(width=0      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       VGroup(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       Text('''CPU'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														cpu.move_to([-2.5, -0.5, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(4      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       Text('''GPU'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														gpu.move_to([-1, -1, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )

														_a			  =       Text('''Model'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														model.move_to([3, -1.0, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       []
														for i, rect in enumerate(lowerCAmelCase_      ):
																					rect.set_stroke(lowerCAmelCase_      )
																					# target = fill.copy().set_fill(YELLOW, opacity=0.7)
																					# target.move_to(rect)
																					# self.add(target)

																					_a			  =       Rectangle(height=0.4_6 / 4							,    width=0.4_6 / 3      ).set_stroke(width=0.0      ).set_fill(lowerCAmelCase_							,    opacity=0.7      )

																					if i == 0:
																												cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )							,    buff=0.0_2							,    direction=lowerCAmelCase_      )
																												cpu_target.set_x(cpu_target.get_x() + 0.1      )
																					elif i == 3:
																												cpu_target.next_to(cpu_targs[0]							,    direction=lowerCAmelCase_							,    buff=0.0      )
																					else:
																												cpu_target.next_to(cpu_targs[i - 1]							,    direction=lowerCAmelCase_							,    buff=0.0      )
																					self.add(lowerCAmelCase_      )
																					cpu_targs.append(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )

														_a			  =       Text('''Loaded Checkpoint'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    aligned_edge=lowerCAmelCase_							,    buff=0.4      )
														checkpoint.move_to([3, 0.5, 0]      )

														_a			  =       Square(side_length=2.2      )
														key.move_to([-5, 2, 0]      )

														_a			  =       MarkupText(
														    F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'							,    font_size=18							,    )

														key_text.move_to([-5, 2.4, 0]      )

														self.add(lowerCAmelCase_							,    lowerCAmelCase_      )

														_a			  =       MarkupText(
														    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'							,    font_size=18							,    )

														blue_text.next_to(lowerCAmelCase_							,    DOWN * 2.4							,    aligned_edge=key_text.get_left()      )

														_a			  =       MarkupText(
														    F'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'							,    font_size=24							,    )
														step_a.move_to([2, 2, 0]      )
														self.play(Write(lowerCAmelCase_      )							,    Write(lowerCAmelCase_      )      )

														self.play(Write(lowerCAmelCase_							,    run_time=1      )							,    Create(lowerCAmelCase_							,    run_time=1      )      )

														_a			  =       []
														_a			  =       []
														for i, rect in enumerate(lowerCAmelCase_      ):
																					_a			  =       fill.copy().set_fill(lowerCAmelCase_							,    opacity=0.7      )
																					target.move_to(lowerCAmelCase_      )
																					first_animations.append(GrowFromCenter(lowerCAmelCase_							,    run_time=1      )      )

																					_a			  =       target.copy()
																					cpu_target.generate_target()
																					if i < 5:
																												cpu_target.target.move_to(cpu_left_col_base[i + 1]      )
																					else:
																												cpu_target.target.move_to(cpu_right_col_base[i - 5]      )
																					second_animations.append(MoveToTarget(lowerCAmelCase_							,    run_time=1.5      )      )

														self.play(*lowerCAmelCase_      )
														self.play(*lowerCAmelCase_      )
														self.wait()

