




'''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


_SCREAMING_SNAKE_CASE							:       int						=			re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_SCREAMING_SNAKE_CASE							:       Dict						=			10
_SCREAMING_SNAKE_CASE							:       Tuple						=			256


def 					UpperCamelCase_(     snake_case							:	List[str]					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






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




class 			_snake_case		:




			def __init__( self    ,							*,
			    a__ = 0.8_5    ,							)    ->    Dict:

										'''simple docstring'''




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

										snake_case_      =				defaultdict(__UpperCamelCase							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__							)    ->    None:

										'''simple docstring'''




										snake_case_      =				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							)    ->    List[List[Dict]]:

										'''simple docstring'''




										snake_case_      =				[]
										for base, duplicates in self._duplicate_clusters.items():
																	snake_case_      =				[base] + list(__UpperCamelCase							)
																	# reformat the cluster to be a list of dict
																	snake_case_      =				[{"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    ,							a__							)    ->    None:

										'''simple docstring'''




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


def 					UpperCamelCase_(     snake_case							:	List[str]					):

							'''simple docstring'''






							snake_case_ ,				snake_case_      =				element
							snake_case_      =				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_(     snake_case							:	Type[Dataset]					):

							'''simple docstring'''






							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash							,    ThreadedIterator(a__							,    max_queue_size=1_0_0_0_0					)							,    chunksize=1_0_0							,    ):
																					if data is not None:
																												yield data


def 					UpperCamelCase_(     snake_case							:	Type[Dataset]							,    snake_case							:	float					):

							'''simple docstring'''






							snake_case_      =				DuplicationIndex(duplication_jaccard_threshold=a__					)

							for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a__					)					)							,    max_queue_size=1_0_0					)					):
														di.add(a__							,    a__					)

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


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				get_tokens(a__					)
							snake_case_      =				get_tokens(a__					)
							return len(tokensa & tokensa					) / len(tokensa | tokensa					)


_SCREAMING_SNAKE_CASE							:       List[Any]						=			None


def 					UpperCamelCase_(     snake_case							:	Dict							,    snake_case							:	Any					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							global _shared_dataset
							snake_case_      =				dataset
							snake_case_      =				[]
							snake_case_      =				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_(     snake_case							:	Type[Dataset]							,    snake_case							:	float = 0.85					):

							'''simple docstring'''






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

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






'''simple docstring'''





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin




@skip_mps
class 			_snake_case		(     lowercase_						,						lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_					:				List[str]							=   IFPipeline
			lowerCAmelCase_					:				int							=   TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
			lowerCAmelCase_					:				Optional[int]							=   TEXT_TO_IMAGE_BATCH_PARAMS
			lowerCAmelCase_					:				List[Any]							=   PipelineTesterMixin.required_optional_params - {"latents"}




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										return self._get_dummy_components()




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__=0							)    ->    str:

										'''simple docstring'''




										if str(a__							).startswith("mps"							):
																	snake_case_      =				torch.manual_seed(a__							)
										else:
																	snake_case_      =				torch.Generator(device=a__							).manual_seed(a__							)

										snake_case_      =				{
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != "cuda"    ,							reason="float16 requires CUDA"							)
			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										super().test_save_load_floataa(expected_max_diff=1e-1							)




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										self._test_attention_slicing_forward_pass(expected_max_diff=1e-2							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										self._test_save_load_local()




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										self._test_inference_batch_single_identical(
										    expected_max_diff=1e-2    ,							)




			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()    ,							reason="XFormers attention is only available with CUDA and `xformers` installed"    ,							)
			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)




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




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

										'''simple docstring'''




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




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

										'''simple docstring'''




										snake_case_      =				IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"    ,							variant="fp16"    ,							torch_dtype=torch.floataa							)

										snake_case_      =				IFSuperResolutionPipeline.from_pretrained(
										    "DeepFloyd/IF-II-L-v1.0"    ,							variant="fp16"    ,							torch_dtype=torch.floataa    ,							text_encoder=a__    ,							tokenizer=a__							)

										# pre compute text embeddings and remove T5 to save memory

										pipe_a.text_encoder.to("cuda"							)

										snake_case_ ,				snake_case_      =				pipe_a.encode_prompt("anime turtle"    ,							device="cuda"							)

										del pipe_a.tokenizer
										del pipe_a.text_encoder
										gc.collect()

										snake_case_      =				None
										snake_case_      =				None

										pipe_a.enable_model_cpu_offload()
										pipe_a.enable_model_cpu_offload()

										pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
										pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

										self._test_if(a__    ,							a__    ,							a__    ,							a__							)

										pipe_a.remove_all_hooks()
										pipe_a.remove_all_hooks()

										# img2img

										snake_case_      =				IFImgaImgPipeline(**pipe_a.components							)
										snake_case_      =				IFImgaImgSuperResolutionPipeline(**pipe_a.components							)

										pipe_a.enable_model_cpu_offload()
										pipe_a.enable_model_cpu_offload()

										pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
										pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

										self._test_if_imgaimg(a__    ,							a__    ,							a__    ,							a__							)

										pipe_a.remove_all_hooks()
										pipe_a.remove_all_hooks()

										# inpainting

										snake_case_      =				IFInpaintingPipeline(**pipe_a.components							)
										snake_case_      =				IFInpaintingSuperResolutionPipeline(**pipe_a.components							)

										pipe_a.enable_model_cpu_offload()
										pipe_a.enable_model_cpu_offload()

										pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
										pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

										self._test_if_inpainting(a__    ,							a__    ,							a__    ,							a__							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__							)    ->    Dict:

										'''simple docstring'''




										_start_torch_memory_measurement()

										snake_case_      =				torch.Generator(device="cpu"							).manual_seed(0							)
										snake_case_      =				pipe_a(
										    prompt_embeds=a__    ,							negative_prompt_embeds=a__    ,							num_inference_steps=2    ,							generator=a__    ,							output_type="np"    ,							)

										snake_case_      =				output.images[0]

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

										snake_case_      =				torch.cuda.max_memory_allocated()
										assert mem_bytes < 13 * 10**9

										snake_case_      =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"							)
										assert_mean_pixel_difference(a__    ,							a__							)

										# pipeline 2

										_start_torch_memory_measurement()

										snake_case_      =				torch.Generator(device="cpu"							).manual_seed(0							)

										snake_case_      =				floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(0							)							).to(a__							)

										snake_case_      =				pipe_a(
										    prompt_embeds=a__    ,							negative_prompt_embeds=a__    ,							image=a__    ,							generator=a__    ,							num_inference_steps=2    ,							output_type="np"    ,							)

										snake_case_      =				output.images[0]

										assert image.shape == (256, 256, 3)

										snake_case_      =				torch.cuda.max_memory_allocated()
										assert mem_bytes < 4 * 10**9

										snake_case_      =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"							)
										assert_mean_pixel_difference(a__    ,							a__							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__							)    ->    Dict:

										'''simple docstring'''




										_start_torch_memory_measurement()

										snake_case_      =				floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(0							)							).to(a__							)

										snake_case_      =				torch.Generator(device="cpu"							).manual_seed(0							)

										snake_case_      =				pipe_a(
										    prompt_embeds=a__    ,							negative_prompt_embeds=a__    ,							image=a__    ,							num_inference_steps=2    ,							generator=a__    ,							output_type="np"    ,							)

										snake_case_      =				output.images[0]

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

										snake_case_      =				torch.cuda.max_memory_allocated()
										assert mem_bytes < 10 * 10**9

										snake_case_      =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"							)
										assert_mean_pixel_difference(a__    ,							a__							)

										# pipeline 2

										_start_torch_memory_measurement()

										snake_case_      =				torch.Generator(device="cpu"							).manual_seed(0							)

										snake_case_      =				floats_tensor((1, 3, 256, 256)    ,							rng=random.Random(0							)							).to(a__							)
										snake_case_      =				floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(0							)							).to(a__							)

										snake_case_      =				pipe_a(
										    prompt_embeds=a__    ,							negative_prompt_embeds=a__    ,							image=a__    ,							original_image=a__    ,							generator=a__    ,							num_inference_steps=2    ,							output_type="np"    ,							)

										snake_case_      =				output.images[0]

										assert image.shape == (256, 256, 3)

										snake_case_      =				torch.cuda.max_memory_allocated()
										assert mem_bytes < 4 * 10**9

										snake_case_      =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"							)
										assert_mean_pixel_difference(a__    ,							a__							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__							)    ->    str:

										'''simple docstring'''




										_start_torch_memory_measurement()

										snake_case_      =				floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(0							)							).to(a__							)
										snake_case_      =				floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(1							)							).to(a__							)

										snake_case_      =				torch.Generator(device="cpu"							).manual_seed(0							)
										snake_case_      =				pipe_a(
										    prompt_embeds=a__    ,							negative_prompt_embeds=a__    ,							image=a__    ,							mask_image=a__    ,							num_inference_steps=2    ,							generator=a__    ,							output_type="np"    ,							)

										snake_case_      =				output.images[0]

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

										snake_case_      =				torch.cuda.max_memory_allocated()
										assert mem_bytes < 10 * 10**9

										snake_case_      =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"							)
										assert_mean_pixel_difference(a__    ,							a__							)

										# pipeline 2

										_start_torch_memory_measurement()

										snake_case_      =				torch.Generator(device="cpu"							).manual_seed(0							)

										snake_case_      =				floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(0							)							).to(a__							)
										snake_case_      =				floats_tensor((1, 3, 256, 256)    ,							rng=random.Random(0							)							).to(a__							)
										snake_case_      =				floats_tensor((1, 3, 256, 256)    ,							rng=random.Random(1							)							).to(a__							)

										snake_case_      =				pipe_a(
										    prompt_embeds=a__    ,							negative_prompt_embeds=a__    ,							image=a__    ,							mask_image=a__    ,							original_image=a__    ,							generator=a__    ,							num_inference_steps=2    ,							output_type="np"    ,							)

										snake_case_      =				output.images[0]

										assert image.shape == (256, 256, 3)

										snake_case_      =				torch.cuda.max_memory_allocated()
										assert mem_bytes < 4 * 10**9

										snake_case_      =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"							)
										assert_mean_pixel_difference(a__    ,							a__							)


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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




