"""simple docstring"""




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     1
       _lowerCAmelCase						:       Tuple  			=     3
       _lowerCAmelCase						:       Optional[int]  			=     (32, 32)

       _lowerCAmelCase						:       int  			=     floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0  )  ).to(a__  )
       return image






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Dict  			=     UNetaDConditionModel(
           block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,     cross_attention_dim=32				,     )
       return model






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     AutoencoderKL(
           block_out_channels=[32, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,     latent_channels=4				,     )
       return model






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     CLIPTextConfig(
           bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1e-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1000				,     )
       return CLIPTextModel(a__  )






     @property
     def     __A  (		self  ):
       def extract(*a__				,     **a__  ):
         class 						__A							:






              def __init__(		self  ):
                _lowerCAmelCase						:       Dict  			=     torch.ones([0]  )






              def     __A  (		self				,     a__  ):
                self.pixel_values.to(a__  )
                return self







         return Out()

       return extract






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase						:       Optional[Any]  			=     self.dummy_cond_unet
       _lowerCAmelCase						:       Any  			=     DDIMScheduler(
           beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="""scaled_linear"""				,     clip_sample=a__				,     set_alpha_to_one=a__				,     )

       _lowerCAmelCase						:       str  			=     self.dummy_vae
       _lowerCAmelCase						:       Tuple  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       Optional[Any]  			=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

       # make sure here that pndm scheduler skips prk
       _lowerCAmelCase						:       Optional[int]  			=     StableDiffusionPipeline(
           unet=a__				,     scheduler=a__				,     vae=a__				,     text_encoder=a__				,     tokenizer=a__				,     safety_checker=a__				,     feature_extractor=self.dummy_extractor				,     )
       _lowerCAmelCase						:       int  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     """A painting of a squirrel eating a burger"""

       _lowerCAmelCase						:       List[Any]  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     sd_pipe([prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""  )
       _lowerCAmelCase						:       Any  			=     output.images

       _lowerCAmelCase						:       str  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       int  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Tuple  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Dict  			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _lowerCAmelCase						:       Dict  			=     np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase						:       int  			=     self.dummy_cond_unet
       _lowerCAmelCase						:       Optional[int]  			=     PNDMScheduler(skip_prk_steps=a__  )
       _lowerCAmelCase						:       List[str]  			=     self.dummy_vae
       _lowerCAmelCase						:       Any  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       Any  			=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

       # make sure here that pndm scheduler skips prk
       _lowerCAmelCase						:       str  			=     StableDiffusionPipeline(
           unet=a__				,     scheduler=a__				,     vae=a__				,     text_encoder=a__				,     tokenizer=a__				,     safety_checker=a__				,     feature_extractor=self.dummy_extractor				,     )
       _lowerCAmelCase						:       Optional[Any]  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       str  			=     """A painting of a squirrel eating a burger"""
       _lowerCAmelCase						:       str  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     sd_pipe([prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""  )

       _lowerCAmelCase						:       Tuple  			=     output.images

       _lowerCAmelCase						:       Dict  			=     torch.Generator(device=a__  ).manual_seed(0  )
       _lowerCAmelCase						:       List[str]  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Any  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[Any]  			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _lowerCAmelCase						:       Union[str, Any]  			=     np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     StableDiffusionPipeline.from_pretrained(
           """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""				,     safety_checker=a__  )
       assert isinstance(a__				,     a__  )
       assert isinstance(pipe.scheduler				,     a__  )
       assert pipe.safety_checker is None

       _lowerCAmelCase						:       Any  			=     pipe("""example prompt"""				,     num_inference_steps=2  ).images[0]
       assert image is not None

       # check that there's no error when saving a pipeline with one of the models being None
       with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__  )
         _lowerCAmelCase						:       str  			=     StableDiffusionPipeline.from_pretrained(a__  )

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       _lowerCAmelCase						:       Optional[Any]  			=     pipe("""example prompt"""				,     num_inference_steps=2  ).images[0]
       assert image is not None






     @unittest.skipIf(torch_device != """cuda"""				,     """This test requires a GPU"""  )
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.dummy_cond_unet
       _lowerCAmelCase						:       str  			=     PNDMScheduler(skip_prk_steps=a__  )
       _lowerCAmelCase						:       Any  			=     self.dummy_vae
       _lowerCAmelCase						:       Dict  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       Tuple  			=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

       # put models in fp16
       _lowerCAmelCase						:       str  			=     unet.half()
       _lowerCAmelCase						:       List[str]  			=     vae.half()
       _lowerCAmelCase						:       Tuple  			=     bert.half()

       # make sure here that pndm scheduler skips prk
       _lowerCAmelCase						:       Dict  			=     StableDiffusionPipeline(
           unet=a__				,     scheduler=a__				,     vae=a__				,     text_encoder=a__				,     tokenizer=a__				,     safety_checker=a__				,     feature_extractor=self.dummy_extractor				,     )
       _lowerCAmelCase						:       int  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       int  			=     """A painting of a squirrel eating a burger"""
       _lowerCAmelCase						:       Dict  			=     sd_pipe([prompt]				,     num_inference_steps=2				,     output_type="""np"""  ).images

       assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=a__  )
       _lowerCAmelCase						:       List[Any]  			=     LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
       _lowerCAmelCase						:       Optional[int]  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     (
           """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
           """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
           """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
           """ children from bahnhof zoo, detailed """
       )
       _lowerCAmelCase						:       List[Any]  			=     4003660346
       _lowerCAmelCase						:       List[Any]  			=     7

       # without safety guidance (sld_guidance_scale = 0)
       _lowerCAmelCase						:       int  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       Dict  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

       _lowerCAmelCase						:       int  			=     output.images
       _lowerCAmelCase						:       Optional[int]  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Dict  			=     [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2

       # without safety guidance (strong configuration)
       _lowerCAmelCase						:       Optional[int]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       List[str]  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2000				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

       _lowerCAmelCase						:       Optional[int]  			=     output.images
       _lowerCAmelCase						:       Tuple  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       str  			=     [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=a__  )
       _lowerCAmelCase						:       List[str]  			=     LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
       _lowerCAmelCase						:       Optional[Any]  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       List[str]  			=     """padme amidala taking a bath artwork, safe for work, no nudity"""
       _lowerCAmelCase						:       Tuple  			=     2734971755
       _lowerCAmelCase						:       Union[str, Any]  			=     7

       _lowerCAmelCase						:       Optional[int]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       Any  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

       _lowerCAmelCase						:       int  			=     output.images
       _lowerCAmelCase						:       int  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Dict  			=     [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2

       _lowerCAmelCase						:       Optional[Any]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       Any  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2000				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

       _lowerCAmelCase						:       List[Any]  			=     output.images
       _lowerCAmelCase						:       List[Any]  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Optional[int]  			=     [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""  )
       _lowerCAmelCase						:       Any  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     (
           """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
           """ leyendecker"""
       )
       _lowerCAmelCase						:       int  			=     1044355234
       _lowerCAmelCase						:       Tuple  			=     12

       _lowerCAmelCase						:       int  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       str  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

       _lowerCAmelCase						:       List[str]  			=     output.images
       _lowerCAmelCase						:       List[str]  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       int  			=     np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]  )

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-7

       _lowerCAmelCase						:       Optional[int]  			=     torch.manual_seed(a__  )
       _lowerCAmelCase						:       List[Any]  			=     sd_pipe(
           [prompt]				,     generator=a__				,     guidance_scale=a__				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2000				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

       _lowerCAmelCase						:       List[str]  			=     output.images
       _lowerCAmelCase						:       Any  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[str]  			=     np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]  )
       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2





"""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      :		Union[str, Any]           =   re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
_a      :		List[str]           =   10
_a      :		List[Any]           =   256





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]	)       ->      Optional[MinHash]:
  if len(_lowerCamelCase	) < MIN_NUM_TOKENS:
    return None
  _lowerCAmelCase						:       Optional[Any]  			=     MinHash(num_perm=_lowerCamelCase	)
  for token in set(_lowerCamelCase	):
    min_hash.update(token.encode()	)
  return min_hash





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Set[str]:
  return {t for t in NON_ALPHA.split(_lowerCamelCase	) if len(t.strip()	) > 0}
class 						__A							:






     def __init__(		self				,     *,
         a__ = 0.8_5				,     ):
       _lowerCAmelCase						:       List[Any]  			=     duplication_jaccard_threshold
       _lowerCAmelCase						:       Union[str, Any]  			=     NUM_PERM
       _lowerCAmelCase						:       Optional[int]  			=     MinHashLSH(threshold=self._duplication_jaccard_threshold				,     num_perm=self._num_perm  )

       _lowerCAmelCase						:       Optional[int]  			=     defaultdict(a__  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[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     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     []
       for base, duplicates in self._duplicate_clusters.items():
         _lowerCAmelCase						:       List[str]  			=     [base] + list(a__  )
         # reformat the cluster to be a list of dict
         _lowerCAmelCase						:       List[Any]  			=     [{"""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__  ):
       _lowerCAmelCase						:       Dict  			=     self.get_duplicate_clusters()
       with open(a__				,     """w"""  ) as f:
         json.dump(a__				,     a__  )







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      Tuple:
  _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       Tuple  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]	)       ->      Optional[Any]:
  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash			,ThreadedIterator(_lowerCamelCase			,max_queue_size=10000	)			,chunksize=100			,):
      if data is not None:
        yield data





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float	)       ->      List[str]:
  _lowerCAmelCase						:       Optional[Any]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      float:
  _lowerCAmelCase						:       Any  			=     get_tokens(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     get_tokens(_lowerCamelCase	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


_a      :		str           =   None





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]	)       ->      Dict:
  _lowerCAmelCase						:       int  			=     []
  for elementa in cluster:
    _lowerCAmelCase						:       Dict  			=     _shared_dataset[elementa["""base_index"""]]["""content"""]
    for elementa in extremes:
      _lowerCAmelCase						:       Any  			=     _shared_dataset[elementa["""base_index"""]]["""content"""]
      if jaccard_similarity(_lowerCamelCase			,_lowerCamelCase	) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      _lowerCAmelCase						:       Any  			=     1
      extremes.append(_lowerCamelCase	)
  return extremes





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Any	)       ->      str:
  global _shared_dataset
  _lowerCAmelCase						:       Tuple  			=     dataset
  _lowerCAmelCase						:       Optional[Any]  			=     []
  _lowerCAmelCase						:       Optional[Any]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Type[Dataset]			,_lowerCamelCase						:       float = 0.85	)       ->      Tuple[Type[Dataset], List[List[Dict]]]:
  _lowerCAmelCase						:       Tuple  			=     make_duplicate_clusters(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
  _lowerCAmelCase						:       Optional[int]  			=     {}
  _lowerCAmelCase						:       Tuple  			=     find_extremes(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
  for extremes in extremes_clusters:
    for element in extremes:
      _lowerCAmelCase						:       Union[str, Any]  			=     element
  _lowerCAmelCase						:       List[Any]  			=     duplicate_indices - set(extreme_dict.keys()	)
  _lowerCAmelCase						:       List[Any]  			=     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:
      _lowerCAmelCase						:       Tuple  			=     element["""base_index"""] in extreme_dict
      if element["is_extreme"]:
        _lowerCAmelCase						:       Dict  			=     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




