

'''simple docstring'''




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_lowerCAmelCase         =						logging.get_logger(__name__)  # pylint: disable=invalid-name






class   _SCREAMING_SNAKE_CASE   (   A_     ):



 def __init__(   self		:   Dict			,				a__		:   CLIPSegForImageSegmentation			,				a__		:   CLIPSegProcessor			,				a__		:   AutoencoderKL			,				a__		:   CLIPTextModel			,				a__		:   CLIPTokenizer			,				a__		:   UNetaDConditionModel			,				a__		:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,				a__		:   StableDiffusionSafetyChecker			,				a__		:   CLIPImageProcessor			,				):
      super().__init__()

      if hasattr(scheduler.config			,				'''steps_offset'''      ) and scheduler.config.steps_offset != 1:
           __magic_name__						       =    (
               F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
               F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
               "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
               " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
               " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
               " file"
           )
           deprecate('''steps_offset!=1'''			,				'''1.0.0'''			,				snake_case__			,				standard_warn=snake_case__      )
           __magic_name__						       =    dict(scheduler.config      )
           __magic_name__						       =    1
           __magic_name__						       =    FrozenDict(snake_case__      )

      if hasattr(scheduler.config			,				'''skip_prk_steps'''      ) and scheduler.config.skip_prk_steps is False:
           __magic_name__						       =    (
               F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
               " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
               " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
               " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
               " Hub, it would be very nice if you could open a Pull request for the"
               " `scheduler/scheduler_config.json` file"
           )
           deprecate('''skip_prk_steps not set'''			,				'''1.0.0'''			,				snake_case__			,				standard_warn=snake_case__      )
           __magic_name__						       =    dict(scheduler.config      )
           __magic_name__						       =    True
           __magic_name__						       =    FrozenDict(snake_case__      )

      if safety_checker is None:
           logger.warning(
               F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
               ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
               ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
               ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
               ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
               ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''      )

      self.register_modules(
          segmentation_model=snake_case__			,				segmentation_processor=snake_case__			,				vae=snake_case__			,				text_encoder=snake_case__			,				tokenizer=snake_case__			,				unet=snake_case__			,				scheduler=snake_case__			,				safety_checker=snake_case__			,				feature_extractor=snake_case__			,				)



 def 			snake_case__  (   self		:   int			,				a__		:   Optional[Union[str, int]] = "auto"      ):


      if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           __magic_name__						       =    self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(snake_case__      )



 def 			snake_case__  (   self		:   Tuple      ):
      self.enable_attention_slicing(snake_case__      )



 def 			snake_case__  (   self		:   List[str]      ):


      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

      __magic_name__						       =    torch.device('''cuda'''      )

      for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
           if cpu_offloaded_model is not None:
                cpu_offload(snake_case__			,				snake_case__      )



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 			snake_case__  (   self		:   Dict      ):


      if self.device != torch.device('''meta'''      ) or not hasattr(self.unet			,				'''_hf_hook'''      ):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(snake_case__			,				'''_hf_hook'''      )
               and hasattr(module._hf_hook			,				'''execution_device'''      )
               and module._hf_hook.execution_device is not None
           ):
                return torch.device(module._hf_hook.execution_device      )
      return self.device



 @torch.no_grad()
 def __call__(   self		:   Optional[Any]			,				a__		:   Union[str, List[str]]			,				a__		:   Union[torch.FloatTensor, PIL.Image.Image]			,				a__		:   str			,				a__		:   int = 512			,				a__		:   int = 512			,				a__		:   int = 50			,				a__		:   float = 7.5			,				a__		:   Optional[Union[str, List[str]]] = None			,				a__		:   Optional[int] = 1			,				a__		:   float = 0.0			,				a__		:   Optional[torch.Generator] = None			,				a__		:   Optional[torch.FloatTensor] = None			,				a__		:   Optional[str] = "pil"			,				a__		:   bool = True			,				a__		:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,				a__		:   int = 1			,				**a__		:   Union[str, Any]			,				):
      __magic_name__						       =    self.segmentation_processor(
          text=[text]			,				images=[image]			,				padding='''max_length'''			,				return_tensors='''pt'''      ).to(self.device      )
      __magic_name__						       =    self.segmentation_model(**snake_case__      )
      __magic_name__						       =    torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
      __magic_name__						       =    self.numpy_to_pil(snake_case__      )[0].resize(image.size      )

      # Run inpainting pipeline with the generated mask
      __magic_name__						       =    StableDiffusionInpaintPipeline(
          vae=self.vae			,				text_encoder=self.text_encoder			,				tokenizer=self.tokenizer			,				unet=self.unet			,				scheduler=self.scheduler			,				safety_checker=self.safety_checker			,				feature_extractor=self.feature_extractor			,				)
      return inpainting_pipeline(
          prompt=snake_case__			,				image=snake_case__			,				mask_image=snake_case__			,				height=snake_case__			,				width=snake_case__			,				num_inference_steps=snake_case__			,				guidance_scale=snake_case__			,				negative_prompt=snake_case__			,				num_images_per_prompt=snake_case__			,				eta=snake_case__			,				generator=snake_case__			,				latents=snake_case__			,				output_type=snake_case__			,				return_dict=snake_case__			,				callback=snake_case__			,				callback_steps=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


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




def 						UpperCamelCase	(			a			)				->     Optional[MinHash]:






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




def 						UpperCamelCase	(			a			)				->     Set[str]:






     '''simple docstring'''
     return {t for t in NON_ALPHA.split(a			) if len(t.strip()			) > 0}






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Any			,				*,
     a__		:   float = 0.85			,				):
      __magic_name__						       =    duplication_jaccard_threshold
      __magic_name__						       =    NUM_PERM
      __magic_name__						       =    MinHashLSH(threshold=self._duplication_jaccard_threshold			,				num_perm=self._num_perm      )

      __magic_name__						       =    defaultdict(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple			,				a__		:   MinHash      ):
      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    []
      for base, duplicates in self._duplicate_clusters.items():
           __magic_name__						       =    [base] + list(a__      )
           # reformat the cluster to be a list of dict
           __magic_name__						       =    [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
           duplicate_clusters.append(a__      )
      return duplicate_clusters



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple      ):
      __magic_name__						       =    self.get_duplicate_clusters()
      with open(a__			,				'''w'''      ) as f:
           json.dump(a__			,				a__      )




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    element
     __magic_name__						       =    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	(			a			)				->     List[Any]:






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




def 						UpperCamelCase	(			a		,						a			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    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 						UpperCamelCase	(			a		,						a			)				->     float:






     '''simple docstring'''
     __magic_name__						       =    get_tokens(a			)
     __magic_name__						       =    get_tokens(a			)
     return len(tokensa & tokensa			) / len(tokensa | tokensa			)


_lowerCAmelCase         =						None




def 						UpperCamelCase	(			a		,						a			)				->     Dict:






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




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Optional[Any]:






     '''simple docstring'''
     global _shared_dataset
     __magic_name__						       =    dataset
     __magic_name__						       =    []
     __magic_name__						       =    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	(			a		,						a = 0.85			)				->     Tuple[Type[Dataset], List[List[Dict]]]:






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






