


'''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				:					Dict    						=			re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
A				:					List[str]    						=			10
A				:					Optional[int]    						=			256






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]       ):






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






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
							return {t for t in NON_ALPHA.split(_lowercase       ) if len(t.strip()       ) > 0}


class 			__lowerCamelCase			:
				"""simple docstring"""


				def __init__(  self      :							Union[str, Any]				,							*,
				    SCREAMING_SNAKE_CASE      :							Any = 0.85				,							):
											_A					:     Any     = duplication_jaccard_threshold
											_A					:     Dict     = NUM_PERM
											_A					:     Dict     = MinHashLSH(threshold=self._duplication_jaccard_threshold				,							num_perm=self._num_perm)

											_A					:     List[str]     = defaultdict(lowerCAmelCase__)


				def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
											_A					:     Any     = self._index.query(lowerCAmelCase__)
											if code_key in self._index.keys:
																		print(F'Duplicate key {code_key}')
																		return

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


				def        A   (  self      :							List[str]):
											_A					:     Any     = []
											for base, duplicates in self._duplicate_clusters.items():
																		_A					:     Any     = [base] + list(lowerCAmelCase__)
																		# reformat the cluster to be a list of dict
																		_A					:     Optional[int]     = [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
																		duplicate_clusters.append(lowerCAmelCase__)
											return duplicate_clusters


				def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[Any]):
											_A					:     Optional[int]     = self.get_duplicate_clusters()
											with open(lowerCAmelCase__				,							'w') as f:
																		json.dump(lowerCAmelCase__				,							lowerCAmelCase__)






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]       ):
							_A					:     Optional[Any]     = element
							_A					:     Any     = 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 				lowerCAmelCase__	(    lowerCamelCase					:      Type[Dataset]       ):






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






def 				lowerCAmelCase__	(    lowerCamelCase					:      Type[Dataset]  ,lowerCamelCase					:      float       ):
							_A					:     Optional[int]     = DuplicationIndex(duplication_jaccard_threshold=_lowercase       )

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

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      str       ):
							_A					:     Any     = get_tokens(_lowercase       )
							_A					:     List[str]     = get_tokens(_lowercase       )
							return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


A				:					Dict    						=			None






def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Dict       ):
							_A					:     Union[str, Any]     = []
							for elementa in cluster:
														_A					:     List[Any]     = _shared_dataset[elementa["base_index"]]["content"]
														for elementa in extremes:
																					_A					:     Optional[int]     = _shared_dataset[elementa["base_index"]]["content"]
																					if jaccard_similarity(_lowercase  ,_lowercase       ) >= jaccard_threshold:
																												elementa["copies"] += 1
																												break
														else:
																					_A					:     Optional[Any]     = 1
																					extremes.append(_lowercase       )
							return extremes






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      Any  ,lowerCamelCase					:      Optional[Any]       ):
							global _shared_dataset
							_A					:     Tuple     = dataset
							_A					:     List[Any]     = []
							_A					:     int     = partial(_find_cluster_extremes_shared  ,jaccard_threshold=_lowercase       )
							with mp.Pool() as pool:
														for extremes in tqdm(
														    pool.imap_unordered(
														        _lowercase  ,_lowercase  ,)  ,total=len(_lowercase       )  ,):
																					extremes_list.append(_lowercase       )
							return extremes_list






def 				lowerCAmelCase__	(    lowerCamelCase					:      Type[Dataset]  ,lowerCamelCase					:      float = 0.85       ):
							_A					:     str     = make_duplicate_clusters(_lowercase  ,_lowercase       )
							_A					:     List[Any]     = {x["base_index"] for cluster in duplicate_clusters for x in cluster}
							_A					:     List[Any]     = {}
							_A					:     str     = find_extremes(_lowercase  ,_lowercase  ,_lowercase       )
							for extremes in extremes_clusters:
														for element in extremes:
																					_A					:     Optional[Any]     = element
							_A					:     Union[str, Any]     = duplicate_indices - set(extreme_dict.keys()       )
							_A					:     Union[str, Any]     = dataset.filter(lambda lowerCamelCase  ,lowerCamelCase       : idx not in remove_indices  ,with_indices=_lowercase       )

							# update duplicate_clusters
							for cluster in duplicate_clusters:
														for element in cluster:
																					_A					:     Union[str, Any]     = element["base_index"] in extreme_dict
																					if element["is_extreme"]:
																												_A					:     Optional[int]     = extreme_dict[element["base_index"]]["copies"]

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

							return ds_filter, duplicate_clusters










'''simple docstring'''







import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   KandinskyVaaPriorPipeline
    a													=   ["prompt"]
    a													=   ["prompt", "negative_prompt"]
    a													=   [
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    a													=   False


    @property
    def        A   (  self      :							List[str]):
           return 32


    @property
    def        A   (  self      :							List[Any]):
           return 32


    @property
    def        A   (  self      :							Dict):
           return self.time_input_dim


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


    @property
    def        A   (  self      :							Optional[int]):
           return 100


    @property
    def        A   (  self      :							Dict):
           _A					:     Tuple     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
           return tokenizer


    @property
    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(0)
           _A					:     Any     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE)


    @property
    def        A   (  self      :							List[Any]):
           torch.manual_seed(0)

           _A					:     Optional[Any]     = {
               'num_attention_heads': 2,
               'attention_head_dim': 12,
               'embedding_dim': self.text_embedder_hidden_size,
               'num_layers': 1,
           }

           _A					:     Any     = PriorTransformer(**SCREAMING_SNAKE_CASE)
           # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
           _A					:     str     = nn.Parameter(torch.ones(model.clip_std.shape))
           return model


    @property
    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[Any]     = CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size				,							image_size=224				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=37				,							num_attention_heads=4				,							num_channels=3				,							num_hidden_layers=5				,							patch_size=14				,							)

           _A					:     Union[str, Any]     = CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE)
           return model


    @property
    def        A   (  self      :							int):
           _A					:     Optional[Any]     = CLIPImageProcessor(
               crop_size=224				,							do_center_crop=SCREAMING_SNAKE_CASE				,							do_normalize=SCREAMING_SNAKE_CASE				,							do_resize=SCREAMING_SNAKE_CASE				,							image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]				,							image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,							resample=3				,							size=224				,							)

           return image_processor


    def        A   (  self      :							Optional[Any]):
           _A					:     Optional[int]     = self.dummy_prior
           _A					:     Dict     = self.dummy_image_encoder
           _A					:     Dict     = self.dummy_text_encoder
           _A					:     str     = self.dummy_tokenizer
           _A					:     Optional[Any]     = self.dummy_image_processor

           _A					:     Optional[Any]     = UnCLIPScheduler(
               variance_type='fixed_small_log'				,							prediction_type='sample'				,							num_train_timesteps=1000				,							clip_sample=SCREAMING_SNAKE_CASE				,							clip_sample_range=10.0				,							)

           _A					:     Dict     = {
               'prior': prior,
               'image_encoder': image_encoder,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'scheduler': scheduler,
               'image_processor': image_processor,
           }

           return components


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Dict     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     int     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = {
               'prompt': 'horse',
               'generator': generator,
               'guidance_scale': 4.0,
               'num_inference_steps': 2,
               'output_type': 'np',
           }
           return inputs


    def        A   (  self      :							List[Any]):
           _A					:     str     = 'cpu'

           _A					:     Tuple     = self.get_dummy_components()

           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           _A					:     Any     = pipe.to(SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     Dict     = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
           _A					:     str     = output.image_embeds

           _A					:     Optional[int]     = pipe(
               **self.get_dummy_inputs(SCREAMING_SNAKE_CASE)				,							return_dict=SCREAMING_SNAKE_CASE				,							)[0]

           _A					:     Optional[int]     = image[0, -10:]
           _A					:     int     = image_from_tuple[0, -10:]

           assert image.shape == (1, 32)

           _A					:     Dict     = np.array(
               [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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


    @skip_mps
    def        A   (  self      :							Any):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[int]     = True
           _A					:     Tuple     = False

           self._test_inference_batch_single_identical(
               test_max_difference=SCREAMING_SNAKE_CASE				,							relax_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)


    @skip_mps
    def        A   (  self      :							int):
           _A					:     Tuple     = torch_device == 'cpu'
           _A					:     Optional[Any]     = False

           self._test_attention_slicing_forward_pass(
               test_max_difference=SCREAMING_SNAKE_CASE				,							test_mean_pixel_difference=SCREAMING_SNAKE_CASE				,							)






