"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		KandinskyImgaImgPipeline
     _UpperCamelCase							:			Optional[Any]							=		["prompt", "image_embeds", "negative_image_embeds", "image"]
     _UpperCamelCase							:			List[Any]							=		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _UpperCamelCase							:			Dict							=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _UpperCamelCase							:			Union[str, Any]							=		False






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return self.time_input_dim






     @property
     def     __A  (		self  ):
       return self.time_input_dim * 4






     @property
     def     __A  (		self  ):
       return 100






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
       return tokenizer






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       List[Any]  			=     MCLIPConfig(
           numDims=self.cross_attention_dim				,     transformerDimensions=self.text_embedder_hidden_size				,     hidden_size=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_hidden_layers=5				,     vocab_size=1005				,     )

       _lowerCAmelCase						:       int  			=     MultilingualCLIP(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     text_encoder.eval()

       return text_encoder






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )

       _lowerCAmelCase						:       str  			=     {
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     UNetaDConditionModel(**a__  )
       return model






     @property
     def     __A  (		self  ):
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     VQModel(**self.dummy_movq_kwargs  )
       return model






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       List[Any]  			=     self.dummy_tokenizer
       _lowerCAmelCase						:       int  			=     self.dummy_unet
       _lowerCAmelCase						:       Dict  			=     self.dummy_movq

       _lowerCAmelCase						:       Tuple  			=     {
           """num_train_timesteps""": 1000,
           """beta_schedule""": """linear""",
           """beta_start""": 0.0_0_0_8_5,
           """beta_end""": 0.0_1_2,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     DDIMScheduler(**a__  )

       _lowerCAmelCase						:       List[Any]  			=     {
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       _lowerCAmelCase						:       Optional[int]  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1  )  ).to(a__  )
       # create init_image
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     image.cpu().permute(0				,     2				,     3				,     1  )[0]
       _lowerCAmelCase						:       List[Any]  			=     Image.fromarray(np.uinta(a__  )  ).convert("""RGB"""  ).resize((256, 256)  )

       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[Any]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Tuple  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     {
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     """cpu"""

       _lowerCAmelCase						:       int  			=     self.get_dummy_components()

       _lowerCAmelCase						:       int  			=     self.pipeline_class(**a__  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe.to(a__  )

       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Tuple  			=     pipe(**self.get_dummy_inputs(a__  )  )
       _lowerCAmelCase						:       List[Any]  			=     output.images

       _lowerCAmelCase						:       Tuple  			=     pipe(
           **self.get_dummy_inputs(a__  )				,     return_dict=a__				,     )[0]

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

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

       _lowerCAmelCase						:       str  			=     np.array(
           [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]  )
       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@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						:       Dict  			=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_img2img_frog.npy"""  )

       _lowerCAmelCase						:       List[str]  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     """A red cartoon frog, 4k"""

       _lowerCAmelCase						:       int  			=     KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""				,     torch_dtype=torch.floataa  )
       pipe_prior.to(a__  )

       _lowerCAmelCase						:       Tuple  			=     KandinskyImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1"""				,     torch_dtype=torch.floataa  )
       _lowerCAmelCase						:       Any  			=     pipeline.to(a__  )

       pipeline.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Any  			=     torch.Generator(device="""cpu"""  ).manual_seed(0  )
       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     pipe_prior(
           a__				,     generator=a__				,     num_inference_steps=5				,     negative_prompt=""""""				,     ).to_tuple()

       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline(
           a__				,     image=a__				,     image_embeds=a__				,     negative_image_embeds=a__				,     generator=a__				,     num_inference_steps=100				,     height=768				,     width=768				,     strength=0.2				,     output_type="""np"""				,     )

       _lowerCAmelCase						:       Dict  			=     output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(a__				,     a__  )





"""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




