

'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class   _SCREAMING_SNAKE_CASE   (   _lowercase		,_lowercase		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					CycleDiffusionPipeline
 __SCREAMING_SNAKE_CASE					:str      	=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
 }
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					PipelineTesterMixin.required_optional_params - {"""latents"""}
 __SCREAMING_SNAKE_CASE					:Any      	=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}     )
 __SCREAMING_SNAKE_CASE					:List[str]      	=					IMAGE_TO_IMAGE_IMAGE_PARAMS
 __SCREAMING_SNAKE_CASE					:List[str]      	=					IMAGE_TO_IMAGE_IMAGE_PARAMS



 def 			snake_case__  (   self		:   List[str]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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			,				)
      __magic_name__						       =    DDIMScheduler(
          beta_start=0.00_085			,				beta_end=0.012			,				beta_schedule='''scaled_linear'''			,				num_train_timesteps=1000			,				clip_sample=__UpperCamelCase			,				set_alpha_to_one=__UpperCamelCase			,				)
      torch.manual_seed(0      )
      __magic_name__						       =    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			,				)
      torch.manual_seed(0      )
      __magic_name__						       =    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			,				)
      __magic_name__						       =    CLIPTextModel(__UpperCamelCase      )
      __magic_name__						       =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

      __magic_name__						       =    {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Union[str, Any]			,				a__		:   int=0      ):
      __magic_name__						       =    floats_tensor((1, 3, 32, 32)			,				rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
      __magic_name__						       =    image / 2 + 0.5
      if str(__UpperCamelCase      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(__UpperCamelCase      )
      else:
           __magic_name__						       =    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
      __magic_name__						       =    {
          '''prompt''': '''An astronaut riding an elephant''',
          '''source_prompt''': '''An astronaut riding a horse''',
          '''image''': image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''eta''': 0.1,
          '''strength''': 0.8,
          '''guidance_scale''': 3,
          '''source_guidance_scale''': 1,
          '''output_type''': '''numpy''',
      }
      return inputs



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    CycleDiffusionPipeline(**__UpperCamelCase      )
      __magic_name__						       =    pipe.to(__UpperCamelCase      )
      pipe.set_progress_bar_config(disable=__UpperCamelCase      )

      __magic_name__						       =    self.get_dummy_inputs(__UpperCamelCase      )
      __magic_name__						       =    pipe(**__UpperCamelCase      )
      __magic_name__						       =    output.images

      __magic_name__						       =    images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      __magic_name__						       =    np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]      )

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



 @unittest.skipIf(torch_device != '''cuda'''			,				'''This test requires a GPU'''      )
 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    self.get_dummy_components()
      for name, module in components.items():
           if hasattr(__UpperCamelCase			,				'''half'''      ):
                __magic_name__						       =    module.half()
      __magic_name__						       =    CycleDiffusionPipeline(**__UpperCamelCase      )
      __magic_name__						       =    pipe.to(__UpperCamelCase      )
      pipe.set_progress_bar_config(disable=__UpperCamelCase      )

      __magic_name__						       =    self.get_dummy_inputs(__UpperCamelCase      )
      __magic_name__						       =    pipe(**__UpperCamelCase      )
      __magic_name__						       =    output.images

      __magic_name__						       =    images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      __magic_name__						       =    np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]      )

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



 @skip_mps
 def 			snake_case__  (   self		:   Any      ):
      return super().test_save_load_local()



 @unittest.skip('''non-deterministic pipeline'''      )
 def 			snake_case__  (   self		:   List[str]      ):
      return super().test_inference_batch_single_identical()



 @skip_mps
 def 			snake_case__  (   self		:   str      ):
      return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def 			snake_case__  (   self		:   Tuple      ):
      return super().test_save_load_optional_components()



 @skip_mps
 def 			snake_case__  (   self		:   Any      ):
      return super().test_attention_slicing_forward_pass()






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



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



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/cycle-diffusion/black_colored_car.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''      )
      __magic_name__						       =    init_image.resize((512, 512)      )

      __magic_name__						       =    '''CompVis/stable-diffusion-v1-4'''
      __magic_name__						       =    DDIMScheduler.from_pretrained(__UpperCamelCase			,				subfolder='''scheduler'''      )
      __magic_name__						       =    CycleDiffusionPipeline.from_pretrained(
          __UpperCamelCase			,				scheduler=__UpperCamelCase			,				safety_checker=__UpperCamelCase			,				torch_dtype=torch.floataa			,				revision='''fp16'''      )

      pipe.to(__UpperCamelCase      )
      pipe.set_progress_bar_config(disable=__UpperCamelCase      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''A black colored car'''
      __magic_name__						       =    '''A blue colored car'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=__UpperCamelCase			,				source_prompt=__UpperCamelCase			,				image=__UpperCamelCase			,				num_inference_steps=100			,				eta=0.1			,				strength=0.85			,				guidance_scale=3			,				source_guidance_scale=1			,				generator=__UpperCamelCase			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images

      # the values aren't exactly equal, but the images look the same visually
      assert np.abs(image - expected_image      ).max() < 5E-1



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/cycle-diffusion/black_colored_car.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''      )
      __magic_name__						       =    init_image.resize((512, 512)      )

      __magic_name__						       =    '''CompVis/stable-diffusion-v1-4'''
      __magic_name__						       =    DDIMScheduler.from_pretrained(__UpperCamelCase			,				subfolder='''scheduler'''      )
      __magic_name__						       =    CycleDiffusionPipeline.from_pretrained(__UpperCamelCase			,				scheduler=__UpperCamelCase			,				safety_checker=__UpperCamelCase      )

      pipe.to(__UpperCamelCase      )
      pipe.set_progress_bar_config(disable=__UpperCamelCase      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''A black colored car'''
      __magic_name__						       =    '''A blue colored car'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=__UpperCamelCase			,				source_prompt=__UpperCamelCase			,				image=__UpperCamelCase			,				num_inference_steps=100			,				eta=0.1			,				strength=0.85			,				guidance_scale=3			,				source_guidance_scale=1			,				generator=__UpperCamelCase			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images

      assert np.abs(image - expected_image      ).max() < 2E-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


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




def 						UpperCamelCase	(			a			)				->     Optional[MinHash]:






     '''simple docstring'''
     if len(a			) < MIN_NUM_TOKENS:
          return None
     __magic_name__						       =    MinHash(num_perm=a			)
     for token in set(a			):
          min_hash.update(token.encode()			)
     return min_hash




def 						UpperCamelCase	(			a			)				->     Set[str]:






     '''simple docstring'''
     return {t for t in NON_ALPHA.split(a			) if len(t.strip()			) > 0}






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Any			,				*,
     a__		:   float = 0.85			,				):
      __magic_name__						       =    duplication_jaccard_threshold
      __magic_name__						       =    NUM_PERM
      __magic_name__						       =    MinHashLSH(threshold=self._duplication_jaccard_threshold			,				num_perm=self._num_perm      )

      __magic_name__						       =    defaultdict(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple			,				a__		:   MinHash      ):
      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    []
      for base, duplicates in self._duplicate_clusters.items():
           __magic_name__						       =    [base] + list(a__      )
           # reformat the cluster to be a list of dict
           __magic_name__						       =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(a__      )
      return duplicate_clusters



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple      ):
      __magic_name__						       =    self.get_duplicate_clusters()
      with open(a__			,				'''w'''      ) as f:
           json.dump(a__			,				a__      )




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    element
     __magic_name__						       =    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 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash		,						ThreadedIterator(a		,						max_queue_size=1_0000			)		,						chunksize=100		,						):
               if data is not None:
                    yield data




def 						UpperCamelCase	(			a		,						a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     float:






     '''simple docstring'''
     __magic_name__						       =    get_tokens(a			)
     __magic_name__						       =    get_tokens(a			)
     return len(tokensa & tokensa			) / len(tokensa | tokensa			)


_lowerCAmelCase         =						None




def 						UpperCamelCase	(			a		,						a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    []
     for elementa in cluster:
          __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
          for elementa in extremes:
               __magic_name__						       =    _shared_dataset[elementa['''base_index''']]['''content''']
               if jaccard_similarity(a		,						a			) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               __magic_name__						       =    1
               extremes.append(a			)
     return extremes




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     global _shared_dataset
     __magic_name__						       =    dataset
     __magic_name__						       =    []
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a = 0.85			)				->     Tuple[Type[Dataset], List[List[Dict]]]:






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






