



'''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(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 lowerCAmelCase						(__A):



     """simple docstring"""



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






class __A    :



 '''simple docstring'''







 def __init__(self				,     *,
     A = 0.85				,     )					->  Union[str, Any]:


      """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 )					->  List[str]:


      """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 )					->  Tuple:


      """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 )					->  str:


      """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(UpperCamelCase__						,			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=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						(__A						,			__A):



     """simple docstring"""



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




def lowerCAmelCase						(__A						,			__A						,			__A):



     """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 lowerCAmelCase						(__A						,			__A = 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 __A						,			__A: 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'''





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = KandinskyVaaPriorPipeline
 __lowerCamelCase					: List[str]    = ['prompt']
 __lowerCamelCase					: str    = ['prompt', 'negative_prompt']
 __lowerCamelCase					: Union[str, Any]    = [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 __lowerCamelCase					: Union[str, Any]    = False



 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.time_input_dim



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      return self.time_input_dim * 4



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return 100



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
      return tokenizer



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPTextConfig(
          bos_token_id=0				,     eos_token_id=2				,     hidden_size=self.text_embedder_hidden_size				,     projection_dim=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=1_000				,     )
      return CLIPTextModelWithProjection(A )



 @property
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      torch.manual_seed(0 )

      _a											=						{
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 12,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      _a											=						PriorTransformer(**A )
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _a											=						nn.Parameter(torch.ones(model.clip_std.shape ) )
      return model



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size				,     image_size=224				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_channels=3				,     num_hidden_layers=5				,     patch_size=14				,     )

      _a											=						CLIPVisionModelWithProjection(A )
      return model



 @property
 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						CLIPImageProcessor(
          crop_size=224				,     do_center_crop=A				,     do_normalize=A				,     do_resize=A				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

      return image_processor



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						self.dummy_prior
      _a											=						self.dummy_image_encoder
      _a											=						self.dummy_text_encoder
      _a											=						self.dummy_tokenizer
      _a											=						self.dummy_image_processor

      _a											=						UnCLIPScheduler(
          variance_type='''fixed_small_log'''				,     prediction_type='''sample'''				,     num_train_timesteps=1_000				,     clip_sample=A				,     clip_sample_range=10.0				,     )

      _a											=						{
          '''prior''': prior,
          '''image_encoder''': image_encoder,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''scheduler''': scheduler,
          '''image_processor''': image_processor,
      }

      return components



 def       a__						(self				,     A				,     A=0 )					->  Optional[int]:


      """simple docstring"""



      if str(A ).startswith('''mps''' ):
           _a											=						torch.manual_seed(A )
      else:
           _a											=						torch.Generator(device=A ).manual_seed(A )
      _a											=						{
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						'''cpu'''

      _a											=						self.get_dummy_components()

      _a											=						self.pipeline_class(**A )
      _a											=						pipe.to(A )

      pipe.set_progress_bar_config(disable=A )

      _a											=						pipe(**self.get_dummy_inputs(A ) )
      _a											=						output.image_embeds

      _a											=						pipe(
          **self.get_dummy_inputs(A )				,     return_dict=A				,     )[0]

      _a											=						image[0, -10:]
      _a											=						image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _a											=						np.array(
          [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )

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



 @skip_mps
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						True
      _a											=						False

      self._test_inference_batch_single_identical(
          test_max_difference=A				,     relax_max_difference=A				,     test_mean_pixel_difference=A				,     )



 @skip_mps
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						False

      self._test_attention_slicing_forward_pass(
          test_max_difference=A				,     test_mean_pixel_difference=A				,     )



