


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


__lowerCAmelCase     							=			re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
__lowerCAmelCase     							=			1_0
__lowerCAmelCase     							=			2_5_6
def 			__lowerCamelCase			( _lowerCAmelCase	)					->	Optional[MinHash]:
  if len(UpperCAmelCase_	) < MIN_NUM_TOKENS:
    return None
  _UpperCAmelCase												=     MinHash(num_perm=UpperCAmelCase_	)
  for token in set(UpperCAmelCase_	):
    min_hash.update(token.encode()	)
  return min_hash
def 			__lowerCamelCase			( _lowerCAmelCase	)					->	Set[str]:
  return {t for t in NON_ALPHA.split(UpperCAmelCase_	) if len(t.strip()	) > 0}


class  __SCREAMING_SNAKE_CASE       :


     def __init__(			self      :		int       ,  *,
         __UpperCamelCase      :		str = 0.85       ,  ):
       _UpperCAmelCase												=     duplication_jaccard_threshold
       _UpperCAmelCase												=     NUM_PERM
       _UpperCAmelCase												=     MinHashLSH(threshold=self._duplication_jaccard_threshold       ,  num_perm=self._num_perm )

       _UpperCAmelCase												=     defaultdict(UpperCamelCase_ )


     def 							UpperCAmelCase__  (			self      :		Union[str, Any]       ,  __UpperCamelCase      :		List[Any]       ,  __UpperCamelCase      :		Any ):
       _UpperCAmelCase												=     self._index.query(UpperCamelCase_ )
       if code_key in self._index.keys:
         print(F'''Duplicate key {code_key}''' )
         return

       self._index.insert(UpperCamelCase_       ,  UpperCamelCase_ )
       if len(UpperCamelCase_ ) > 0:
         for base_duplicate in close_duplicates:
           if base_duplicate in self._duplicate_clusters:
             self._duplicate_clusters[base_duplicate].add(UpperCamelCase_ )
             break
         else:
           self._duplicate_clusters[close_duplicates[0]].add(UpperCamelCase_ )


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



     def 							UpperCAmelCase__  (			self      :		Tuple       ,  __UpperCamelCase      :		int ):
       _UpperCAmelCase												=     self.get_duplicate_clusters()
       with open(UpperCamelCase_       ,  "w" ) as f:
         json.dump(UpperCamelCase_       ,  UpperCamelCase_ )





def 			__lowerCamelCase			( _lowerCAmelCase	)					->	Union[str, Any]:
  _UpperCAmelCase						,		_UpperCAmelCase												=     element
  _UpperCAmelCase												=     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			( _lowerCAmelCase	)					->	List[str]:
  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash						,  ThreadedIterator(UpperCAmelCase_						,  max_queue_size=10_000	)						,  chunksize=100						,  ):
      if data is not None:
        yield data
def 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase	)					->	Dict:
  _UpperCAmelCase												=     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 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase	)					->	float:
  _UpperCAmelCase												=     get_tokens(UpperCAmelCase_	)
  _UpperCAmelCase												=     get_tokens(UpperCAmelCase_	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


__lowerCAmelCase     							=			None
def 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase	)					->	Optional[int]:
  _UpperCAmelCase												=     []
  for elementa in cluster:
    _UpperCAmelCase												=     _shared_dataset[elementa["base_index"]]["content"]
    for elementa in extremes:
      _UpperCAmelCase												=     _shared_dataset[elementa["base_index"]]["content"]
      if jaccard_similarity(UpperCAmelCase_						,  UpperCAmelCase_	) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      _UpperCAmelCase												=     1
      extremes.append(UpperCAmelCase_	)
  return extremes
def 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase	)					->	Optional[int]:
  global _shared_dataset
  _UpperCAmelCase												=     dataset
  _UpperCAmelCase												=     []
  _UpperCAmelCase												=     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 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase = 0.85	)					->	Tuple[Type[Dataset], List[List[Dict]]]:
  _UpperCAmelCase												=     make_duplicate_clusters(UpperCAmelCase_						,  UpperCAmelCase_	)
  _UpperCAmelCase												=     {x["base_index"] for cluster in duplicate_clusters for x in cluster}
  _UpperCAmelCase												=     {}
  _UpperCAmelCase												=     find_extremes(UpperCAmelCase_						,  UpperCAmelCase_						,  UpperCAmelCase_	)
  for extremes in extremes_clusters:
    for element in extremes:
      _UpperCAmelCase												=     element
  _UpperCAmelCase												=     duplicate_indices - set(extreme_dict.keys()	)
  _UpperCAmelCase												=     dataset.filter(lambda _lowerCAmelCase						,  _lowerCAmelCase	: idx not in remove_indices						,  with_indices=UpperCAmelCase_	)

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      _UpperCAmelCase												=     element["base_index"] in extreme_dict
      if element["is_extreme"]:
        _UpperCAmelCase												=     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 gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 				__snake_case     (					unittest.TestCase							):





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





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   1
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

		snake_case__	       =   floats_tensor((batch_size, num_channels) + sizes      ,       rng=random.Random(0		)		).to(UpperCamelCase_		)
		return image





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=7      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       attention_head_dim=8      ,       use_linear_projection=UpperCamelCase_      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   AutoencoderKL(
		    block_out_channels=[32, 32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=4      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		torch.manual_seed(0		)
		snake_case__	       =   CLIPTextConfig(
		    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       hidden_act='gelu'      ,       projection_dim=512      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )

		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       )[0]

		snake_case__	       =   image[0, -3:, -3:, -1]
		snake_case__	       =   image_from_tuple[0, -3:, -3:, -1]

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		snake_case__	       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]		)

		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   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   sd_pipe(
		    2 * [prompt]      ,       image=2 * [low_res_image]      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_images_per_prompt=2      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'      ,       'This test requires a GPU'		)
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# put models in fp16, except vae as it overflows in fp16
		snake_case__	       =   unet.half()
		snake_case__	       =   text_encoder.half()

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'      ,       ).images

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)






@slow
@require_torch_gpu
class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		List[str]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 5E-1





	def   _snake_case   (		self		)      ->		int:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing(1		)
		pipe.enable_sequential_cpu_offload()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=5      ,       output_type='np'      ,       )

		snake_case__	       =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9



