


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	:		Optional[int]    =       re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
A	:		Union[str, Any]    =       1_0
A	:		Union[str, Any]    =       2_5_6






def 			UpperCamelCase (     __magic_name__					:							List[str]					)       ->  Optional[MinHash]:
		"""simple docstring"""




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






def 			UpperCamelCase (     __magic_name__					:							str					)       ->  Set[str]:
		"""simple docstring"""




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



class 							A   :
					'''simple docstring'''






					def __init__(self   :						int				, *,
					    _UpperCAmelCase   :						float = 0.85				, )						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		duplication_jaccard_threshold
							lowercase__					      =		NUM_PERM
							lowercase__					      =		MinHashLSH(threshold=self._duplication_jaccard_threshold				, num_perm=self._num_perm		)

							lowercase__					      =		defaultdict(_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						MinHash		)						->       None:




							"""simple docstring"""


							lowercase__					      =		self._index.query(_UpperCAmelCase		)
							if code_key in self._index.keys:
									print(f'''Duplicate key {code_key}'''		)
									return

							self._index.insert(_UpperCAmelCase				, _UpperCAmelCase		)
							if len(_UpperCAmelCase		) > 0:
									for base_duplicate in close_duplicates:
											if base_duplicate in self._duplicate_clusters:
													self._duplicate_clusters[base_duplicate].add(_UpperCAmelCase		)
													break
									else:
											self._duplicate_clusters[close_duplicates[0]].add(_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       List[List[Dict]]:




							"""simple docstring"""


							lowercase__					      =		[]
							for base, duplicates in self._duplicate_clusters.items():
									lowercase__					      =		[base] + list(_UpperCAmelCase		)
									# reformat the cluster to be a list of dict
									lowercase__					      =		[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
									duplicate_clusters.append(_UpperCAmelCase		)
							return duplicate_clusters


					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						Optional[int]		)						->       None:




							"""simple docstring"""


							lowercase__					      =		self.get_duplicate_clusters()
							with open(_UpperCAmelCase				, """w"""		) as f:
									json.dump(_UpperCAmelCase				, _UpperCAmelCase		)






def 			UpperCamelCase (     __magic_name__					:							Any					)       ->  Tuple:
		"""simple docstring"""




		lowercase__      ,     lowercase__					      =		element
		lowercase__					      =		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 (     __magic_name__					:							Type[Dataset]					)       ->  List[Any]:
		"""simple docstring"""




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






def 			UpperCamelCase (     __magic_name__					:							Type[Dataset]   ,   __magic_name__					:							float					)       ->  Union[str, Any]:
		"""simple docstring"""




		lowercase__					      =		DuplicationIndex(duplication_jaccard_threshold=__magic_name__					)

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

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






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  float:
		"""simple docstring"""




		lowercase__					      =		get_tokens(__magic_name__					)
		lowercase__					      =		get_tokens(__magic_name__					)
		return len(tokensa & tokensa					) / len(tokensa | tokensa					)


A	:		Optional[Any]    =       None






def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							str					)       ->  int:
		"""simple docstring"""




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






def 			UpperCamelCase (     __magic_name__					:							Optional[int]   ,   __magic_name__					:							Tuple   ,   __magic_name__					:							str					)       ->  Union[str, Any]:
		"""simple docstring"""




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







def 			UpperCamelCase (     __magic_name__					:							Type[Dataset]   ,   __magic_name__					:							float = 0.8_5					)       ->  Tuple[Type[Dataset], List[List[Dict]]]:
		"""simple docstring"""




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

		# update duplicate_clusters
		for cluster in duplicate_clusters:
				for element in cluster:
						lowercase__					      =		element["""base_index"""] in extreme_dict
						if element["is_extreme"]:
								lowercase__					      =		extreme_dict[element["""base_index"""]]["""copies"""]

		print(f'''Original dataset size: {len(__magic_name__					)}'''					)
		print(f'''Number of duplicate clusters: {len(__magic_name__					)}'''					)
		print(f'''Files in duplicate cluster: {len(__magic_name__					)}'''					)
		print(f'''Unique files in duplicate cluster: {len(__magic_name__					)}'''					)
		print(f'''Filtered dataset size: {len(__magic_name__					)}'''					)

		return ds_filter, duplicate_clusters










import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			StableDiffusionInpaintPipeline
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__      =			frozenset(
					    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A__      =			frozenset([]							)

					def   lowerCamelCase__					(self   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		UNetaDConditionModel(
							    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=9				, out_channels=4				, down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				, up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				, cross_attention_dim=32				, attention_head_dim=(2, 4)				, use_linear_projection=_UpperCAmelCase				, )
							lowercase__					      =		PNDMScheduler(skip_prk_steps=_UpperCAmelCase		)
							torch.manual_seed(0		)
							lowercase__					      =		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				, sample_size=128				, )
							torch.manual_seed(0		)
							lowercase__					      =		CLIPTextConfig(
							    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-0_5				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="""gelu"""				, projection_dim=512				, )
							lowercase__					      =		CLIPTextModel(_UpperCAmelCase		)
							lowercase__					      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

							lowercase__					      =		{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[Any]=0		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		floats_tensor((1, 3, 32, 32)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		)[0]
							lowercase__					      =		Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert("""RGB"""		).resize((64, 64)		)
							lowercase__					      =		Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							lowercase__					      =		{
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": init_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		StableDiffusionInpaintPipeline(**_UpperCAmelCase		)
							lowercase__					      =		sd_pipe.to(_UpperCAmelCase		)
							sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

							lowercase__					      =		self.get_dummy_inputs(_UpperCAmelCase		)
							lowercase__					      =		sd_pipe(**_UpperCAmelCase		).images
							lowercase__					      =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowercase__					      =		np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]		)

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


					def   lowerCamelCase__					(self   :						str		)						->       int:




							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






@slow
@require_torch_gpu
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

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




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase				, safety_checker=_UpperCAmelCase		)
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 9E-3

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, torch_dtype=torch.floataa				, safety_checker=_UpperCAmelCase				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 5E-1


					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		PNDMScheduler.from_pretrained(_UpperCAmelCase				, subfolder="""scheduler"""		)
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(
							    _UpperCAmelCase				, safety_checker=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, torch_dtype=torch.floataa				, )
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing(1		)
							pipe.enable_sequential_cpu_offload()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=2				, output_type="""np"""				, )

							lowercase__					      =		torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9

