



'''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


lowercase_									=    re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_									=    10
lowercase_									=    256




def lowerCAmelCase    (__A):



     """simple docstring"""



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




def lowerCAmelCase    (__A):



     """simple docstring"""



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






class __A     :



 '''simple docstring'''







 def __init__(self					,  *,
     A = 0.85					,  )       ->	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(A			)



 def    a__    (self					,  A					,  A			)       ->	None:


      """simple docstring"""



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


      """simple docstring"""



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



 def    a__    (self					,  A			)       ->	None:


      """simple docstring"""



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





def lowerCAmelCase    (__A):



     """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 lowerCAmelCase    (__A):



     """simple docstring"""



     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash    ,      ThreadedIterator(__A    ,      max_queue_size=10_000)    ,      chunksize=100    ,      ):
               if data is not None:
                    yield data




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    DuplicationIndex(duplication_jaccard_threshold=__A)

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

     # Returns a List[Cluster] where Cluster is List[str] with the filenames.
     return di.get_duplicate_clusters()




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



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


lowercase_									=    None




def lowerCAmelCase    (__A    ,      __A):



     """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(__A    ,      __A) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               _a           =    1
               extremes.append(__A)
     return extremes




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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




def lowerCAmelCase    (__A    ,      __A = 0.85):



     """simple docstring"""



     _a           =    make_duplicate_clusters(__A    ,      __A)
     _a           =    {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     _a           =    {}
     _a           =    find_extremes(__A    ,      __A    ,      __A)
     for extremes in extremes_clusters:
          for element in extremes:
               _a           =    element
     _a           =    duplicate_indices - set(extreme_dict.keys())
     _a           =    dataset.filter(lambda __A    ,      __A: idx not in remove_indices    ,      with_indices=__A)

     # 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(__A)}''')
     print(F'''Number of duplicate clusters: {len(__A)}''')
     print(F'''Files in duplicate cluster: {len(__A)}''')
     print(F'''Unique files in duplicate cluster: {len(__A)}''')
     print(F'''Filtered dataset size: {len(__A)}''')

     return ds_filter, duplicate_clusters








"""simple docstring"""







import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A		=						False

class       _a  ( unittest.TestCase):



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



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Any:
									return 12



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									return 12



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									return 32



			@property
			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[str]						    = VQModel(
									    block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
									return model



			@property
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
									return tokenizer



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									torch.manual_seed(0		)
									snake_case   :			Optional[int]						    = CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	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(_lowercase		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									torch.manual_seed(0		)

									snake_case   :			List[Any]						    = 12
									snake_case   :			Dict						    = 12

									snake_case   :			Tuple						    = {
									    "attention_bias": True,
									    "cross_attention_dim": 32,
									    "attention_head_dim": height * width,
									    "num_attention_heads": 1,
									    "num_vector_embeds": self.num_embed,
									    "num_embeds_ada_norm": self.num_embeds_ada_norm,
									    "norm_num_groups": 32,
									    "sample_size": width,
									    "activation_fn": "geglu-approximate",
									}

									snake_case   :			Dict						    = TransformeraDModel(**_lowercase		)
									return model



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = "cpu"

									snake_case   :			Optional[int]						    = self.dummy_vqvae
									snake_case   :			Dict						    = self.dummy_text_encoder
									snake_case   :			Tuple						    = self.dummy_tokenizer
									snake_case   :			List[Any]						    = self.dummy_transformer
									snake_case   :			List[Any]						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			List[Any]						    = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase		)

									snake_case   :			Dict						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = "teddy bear playing in the pool"

									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Tuple						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[int]						    = output.images

									snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			List[str]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

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

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			str						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

									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 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = "cpu"

									snake_case   :			Dict						    = self.dummy_vqvae
									snake_case   :			List[Any]						    = self.dummy_text_encoder
									snake_case   :			Optional[Any]						    = self.dummy_tokenizer
									snake_case   :			int						    = self.dummy_transformer
									snake_case   :			str						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			Optional[int]						    = LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_lowercase   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length		)

									snake_case   :			List[str]						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Optional[Any]						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = "teddy bear playing in the pool"

									snake_case   :			Union[str, Any]						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[Any]						    = output.images

									snake_case   :			Dict						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

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

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			Optional[Any]						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			Union[str, Any]						    = load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

									snake_case   :			Tuple						    = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
									snake_case   :			Union[str, Any]						    = pipeline.to(_lowercase		)
									pipeline.set_progress_bar_config(disable=_lowercase		)

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Optional[int]						    = pipeline(
									    "teddy bear playing in the pool"   ,	num_images_per_prompt=1   ,	generator=_lowercase   ,	output_type="np"   ,	)

									snake_case   :			List[str]						    = output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image		).max() < 2.0



