


"""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, KandinskyInpaintPipeline, 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 ):
    a_    :				Any             =    KandinskyInpaintPipeline
    a_    :				List[str]             =    ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
    a_    :				Dict             =    [
        'prompt',
        'negative_prompt',
        'image_embeds',
        'negative_image_embeds',
        'image',
        'mask_image',
    ]
    a_    :				Optional[int]             =    [
        'generator',
        'height',
        'width',
        'latents',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    a_    :				Tuple             =    False
    @property
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return 32
    @property
    def      _UpperCamelCase       ( self							:					int				):
          return self.time_input_dim
    @property
    def      _UpperCamelCase       ( self							:					str				):
          return self.time_input_dim * 4
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return 1_00
    @property
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'				)
          return tokenizer
    @property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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=10_05							,					)

          lowerCamelCase__           =		MultilingualCLIP(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		text_encoder.eval()

          return text_encoder
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          torch.manual_seed(0				)

          lowerCamelCase__           =		{
              'in_channels': 9,
              # 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__           =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE__				)
          return model
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          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      _UpperCamelCase       ( self							:					Tuple				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		VQModel(**self.dummy_movq_kwargs				)
          return model
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.dummy_text_encoder
          lowerCamelCase__           =		self.dummy_tokenizer
          lowerCamelCase__           =		self.dummy_unet
          lowerCamelCase__           =		self.dummy_movq

          lowerCamelCase__           =		DDIMScheduler(
              num_train_timesteps=10_00							,					beta_schedule='linear'							,					beta_start=0.0_00_85							,					beta_end=0.0_12							,					clip_sample=SCREAMING_SNAKE_CASE__							,					set_alpha_to_one=SCREAMING_SNAKE_CASE__							,					steps_offset=1							,					prediction_type='epsilon'							,					thresholding=SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0				):
          lowerCamelCase__           =		floats_tensor((1, self.cross_attention_dim)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		floats_tensor((1, self.cross_attention_dim)							,					rng=random.Random(seed + 1				)				).to(SCREAMING_SNAKE_CASE__				)
          # create init_image
          lowerCamelCase__           =		floats_tensor((1, 3, 64, 64)							,					rng=random.Random(SCREAMING_SNAKE_CASE__				)				).to(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		image.cpu().permute(0							,					2							,					3							,					1				)[0]
          lowerCamelCase__           =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__				)				).convert('RGB'				).resize((2_56, 2_56)				)
          # create mask
          lowerCamelCase__           =		np.ones((64, 64)							,					dtype=np.floataa				)
          lowerCamelCase__           =		0

          if str(SCREAMING_SNAKE_CASE__				).startswith('mps'				):
                lowerCamelCase__           =		torch.manual_seed(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		torch.Generator(device=SCREAMING_SNAKE_CASE__				).manual_seed(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		{
              'prompt': 'horse',
              'image': init_image,
              'mask_image': mask,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 2,
              'guidance_scale': 4.0,
              'output_type': 'np',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		'cpu'

          lowerCamelCase__           =		self.get_dummy_components()

          lowerCamelCase__           =		self.pipeline_class(**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		pipe.to(SCREAMING_SNAKE_CASE__				)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		output.images

          lowerCamelCase__           =		pipe(
              **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__				)							,					return_dict=SCREAMING_SNAKE_CASE__							,					)[0]

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

          print(F'image.shape {image.shape}'				)

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

          lowerCamelCase__           =		np.array(
              [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]				)

          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()}'


    def      _UpperCamelCase       ( self							:					Dict				):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3				)





@slow
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					str				):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'				)

          lowerCamelCase__           =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
          lowerCamelCase__           =		np.ones((7_68, 7_68)							,					dtype=np.floataa				)
          lowerCamelCase__           =		0

          lowerCamelCase__           =		'a hat'

          lowerCamelCase__           =		KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'							,					torch_dtype=torch.floataa				)
          pipe_prior.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		KandinskyInpaintPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-inpaint'							,					torch_dtype=torch.floataa				)
          lowerCamelCase__           =		pipeline.to(SCREAMING_SNAKE_CASE__				)
          pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.Generator(device='cpu'				).manual_seed(0				)
          lowerCamelCase__    ,  lowerCamelCase__           =		pipe_prior(
              SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=5							,					negative_prompt=''							,					).to_tuple()

          lowerCamelCase__           =		pipeline(
              SCREAMING_SNAKE_CASE__							,					image=SCREAMING_SNAKE_CASE__							,					mask_image=SCREAMING_SNAKE_CASE__							,					image_embeds=SCREAMING_SNAKE_CASE__							,					negative_image_embeds=SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					num_inference_steps=1_00							,					height=7_68							,					width=7_68							,					output_type='np'							,					)

          lowerCamelCase__           =		output.images[0]

          assert image.shape == (7_68, 7_68, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)







"""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


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



def   snake_case						(				_a:     List[str]							)->		Optional[MinHash]:
      '''simple docstring'''


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



def   snake_case						(				_a:     str							)->		Set[str]:
      '''simple docstring'''


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



class   _a      :
    def __init__( self							:					List[Any]							,					*,
        SCREAMING_SNAKE_CASE__							:					float = 0.85							,					):
          lowerCamelCase__           =		duplication_jaccard_threshold
          lowerCamelCase__           =		NUM_PERM
          lowerCamelCase__           =		MinHashLSH(threshold=self._duplication_jaccard_threshold							,					num_perm=self._num_perm				)

          lowerCamelCase__           =		defaultdict(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					MinHash				):
          lowerCamelCase__           =		self._index.query(SCREAMING_SNAKE_CASE__				)
          if code_key in self._index.keys:
                print(F'Duplicate key {code_key}'				)
                return

          self._index.insert(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          if len(SCREAMING_SNAKE_CASE__				) > 0:
                for base_duplicate in close_duplicates:
                      if base_duplicate in self._duplicate_clusters:
                            self._duplicate_clusters[base_duplicate].add(SCREAMING_SNAKE_CASE__				)
                            break
                else:
                      self._duplicate_clusters[close_duplicates[0]].add(SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		[]
          for base, duplicates in self._duplicate_clusters.items():
                lowerCamelCase__           =		[base] + list(SCREAMING_SNAKE_CASE__				)
                # reformat the cluster to be a list of dict
                lowerCamelCase__           =		[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
                duplicate_clusters.append(SCREAMING_SNAKE_CASE__				)
          return duplicate_clusters


    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]				):
          lowerCamelCase__           =		self.get_duplicate_clusters()
          with open(SCREAMING_SNAKE_CASE__							,					'w'				) as f:
                json.dump(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)





def   snake_case						(				_a:     Union[str, Any]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__    ,  lowerCamelCase__           =		element
      lowerCamelCase__           =		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   snake_case						(				_a:     Type[Dataset]							)->		Tuple:
      '''simple docstring'''


      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash		,   ThreadedIterator(_a		,   max_queue_size=10000							)		,   chunksize=100		,   ):
                  if data is not None:
                        yield data



def   snake_case						(				_a:     Type[Dataset]		,   _a:     float							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		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   snake_case						(				_a:     str		,   _a:     str							)->		float:
      '''simple docstring'''


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


_snake_case          =							None



def   snake_case						(				_a:     Dict		,   _a:     Union[str, Any]							)->		List[str]:
      '''simple docstring'''


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



def   snake_case						(				_a:     Any		,   _a:     Tuple		,   _a:     Dict							)->		Union[str, Any]:
      '''simple docstring'''


      global _shared_dataset
      lowerCamelCase__           =		dataset
      lowerCamelCase__           =		[]
      lowerCamelCase__           =		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   snake_case						(				_a:     Type[Dataset]		,   _a:     float = 0.85							)->		Tuple[Type[Dataset], List[List[Dict]]]:
      '''simple docstring'''


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



