


'''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							:   Tuple  						=	re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
lowerCamelCase							:   Tuple  						=	10
lowerCamelCase							:   List[Any]  						=	256

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      (A       )       ->       Union[str, Any]:






       """simple docstring"""







       return {t for t in NON_ALPHA.split(A       ) if len(t.strip()       ) > 0}
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    List[str]   ,						*,
          UpperCamelCase    :    float = 0.85   ,						):

             '''simple docstring'''







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

             lowercase__      			= defaultdict(_lowerCAmelCase )



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    MinHash ):

             '''simple docstring'''







             lowercase__      			= 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       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= []
             for base, duplicates in self._duplicate_clusters.items():
                    lowercase__      			= [base] + list(_lowerCAmelCase )
                    # 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(_lowerCAmelCase )
             return duplicate_clusters






      def       UpperCamelCase__		(self    :    Dict   ,						UpperCamelCase    :    str ):

             '''simple docstring'''







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






def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       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 _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       with mp.Pool() as pool:
              for data in pool.imap_unordered(
                  _compute_min_hash  , ThreadedIterator(A  , max_queue_size=10_000       )  , chunksize=100  , ):
                     if data is not None:
                            yield data

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       int:






       """simple docstring"""







       lowercase__      			= 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 _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Dict:






       """simple docstring"""







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


lowerCamelCase							:   Dict  						=	None

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Dict:






       """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(A  , A       ) >= jaccard_threshold:
                            elementa["copies"] += 1
                            break
              else:
                     lowercase__      			= 1
                     extremes.append(A       )
       return extremes

def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Optional[Any]:






       """simple docstring"""







       global _shared_dataset
       lowercase__      			= dataset
       lowercase__      			= []
       lowercase__      			= 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 _SCREAMING_SNAKE_CASE      (A  , A = 0.85       )       ->       int:






       """simple docstring"""







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










from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_UpperCAmelCase		:  Dict	=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase=8      ):
  lowercase			:List[str]							=  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowercase			:List[Any]							=  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class       __lowerCAmelCase				(							lowerCAmelCase):






       def __init__(      self:    Tuple				,    _lowerCAmelCase:    UNetaDConditionModel				,    _lowerCAmelCase:    DDPMScheduler				,    _lowerCAmelCase:    VQModel				,    ):
         super().__init__()

         self.register_modules(
             unet=_lowerCAmelCase				,    scheduler=_lowerCAmelCase				,    movq=_lowerCAmelCase				,    )
         lowercase			:List[Any]							=  2 ** (len(self.movq.config.block_out_channels ) - 1)






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Any				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Optional[int] ):
         if latents is None:
           lowercase			:int							=  randn_tensor(_lowerCAmelCase				,    generator=_lowerCAmelCase				,    device=_lowerCAmelCase				,    dtype=_lowerCAmelCase )
         else:
           if latents.shape != shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
           lowercase			:Optional[Any]							=  latents.to(_lowerCAmelCase )

         lowercase			:int							=  latents * scheduler.init_noise_sigma
         return latents






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str]=0 ):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError("Please install accelerate via `pip install accelerate`" )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         lowercase			:List[str]							=  [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(_lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict=0 ):
         if is_accelerate_available() and is_accelerate_version(">="				,    "0.17.0.dev0" ):
           from accelerate import cpu_offload_with_hook
         else:
           raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         if self.device.type != "cpu":
           self.to("cpu"				,    silence_dtype_warnings=_lowerCAmelCase )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         lowercase			:List[Any]							=  None
         for cpu_offloaded_model in [self.unet, self.movq]:
           lowercase		,			lowercase			:Dict							=  cpu_offload_with_hook(_lowerCAmelCase				,    _lowerCAmelCase				,    prev_module_hook=_lowerCAmelCase )

         # We'll offload the last model manually.
         lowercase			:Dict							=  hook






       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         if not hasattr(self.unet				,    "_hf_hook" ):
           return self.device
         for module in self.unet.modules():
           if (
               hasattr(_lowerCAmelCase				,    "_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()
       @replace_example_docstring(_lowerCAmelCase )
       def __call__(      self:    str				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 1_00				,    _lowerCAmelCase:    float = 4.0				,    _lowerCAmelCase:    int = 1				,    _lowerCAmelCase:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,    _lowerCAmelCase:    Optional[torch.FloatTensor] = None				,    _lowerCAmelCase:    Optional[str] = "pil"				,    _lowerCAmelCase:    bool = True				,    ):
         lowercase			:str							=  self._execution_device

         lowercase			:List[str]							=  guidance_scale > 1.0

         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:Any							=  torch.cat(_lowerCAmelCase				,    dim=0 )
         lowercase			:Any							=  image_embeds.shape[0] * num_images_per_prompt
         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:List[Any]							=  torch.cat(_lowerCAmelCase				,    dim=0 )

         if do_classifier_free_guidance:
           lowercase			:int							=  image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )
           lowercase			:Any							=  negative_image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )

         lowercase			:Optional[int]							=  torch.cat([negative_image_embeds, image_embeds]				,    dim=0 ).to(dtype=self.unet.dtype				,    device=_lowerCAmelCase )

         self.scheduler.set_timesteps(_lowerCAmelCase				,    device=_lowerCAmelCase )
         lowercase			:Optional[Any]							=  self.scheduler.timesteps

         lowercase			:Tuple							=  self.unet.config.in_channels

         lowercase		,			lowercase			:Optional[int]							=  downscale_height_and_width(_lowerCAmelCase				,    _lowerCAmelCase				,    self.movq_scale_factor )

         # create initial latent
         lowercase			:List[Any]							=  self.prepare_latents(
             (batch_size, num_channels_latents, height, width)				,    image_embeds.dtype				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    self.scheduler				,    )

         for i, t in enumerate(self.progress_bar(_lowerCAmelCase ) ):
           # expand the latents if we are doing classifier free guidance
           lowercase			:Tuple							=  torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

           lowercase			:List[str]							=  {"image_embeds": image_embeds}
           lowercase			:List[Any]							=  self.unet(
               sample=_lowerCAmelCase				,    timestep=_lowerCAmelCase				,    encoder_hidden_states=_lowerCAmelCase				,    added_cond_kwargs=_lowerCAmelCase				,    return_dict=_lowerCAmelCase				,    )[0]

           if do_classifier_free_guidance:
             lowercase		,			lowercase			:List[Any]							=  noise_pred.split(latents.shape[1]				,    dim=1 )
             lowercase		,			lowercase			:Any							=  noise_pred.chunk(2 )
             lowercase		,			lowercase			:Dict							=  variance_pred.chunk(2 )
             lowercase			:str							=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             lowercase			:Any							=  torch.cat([noise_pred, variance_pred_text]				,    dim=1 )

           if not (
               hasattr(self.scheduler.config				,    "variance_type" )
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
             lowercase		,			lowercase			:str							=  noise_pred.split(latents.shape[1]				,    dim=1 )

           # compute the previous noisy sample x_t -> x_t-1
           lowercase			:Tuple							=  self.scheduler.step(
               _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    generator=_lowerCAmelCase				,    )[0]
         # post-processing
         lowercase			:Dict							=  self.movq.decode(_lowerCAmelCase				,    force_not_quantize=_lowerCAmelCase )["sample"]

         if output_type not in ["pt", "np", "pil"]:
           raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )

         if output_type in ["np", "pil"]:
           lowercase			:Any							=  image * 0.5 + 0.5
           lowercase			:Tuple							=  image.clamp(0				,    1 )
           lowercase			:List[str]							=  image.cpu().permute(0				,    2				,    3				,    1 ).float().numpy()

         if output_type == "pil":
           lowercase			:List[Any]							=  self.numpy_to_pil(_lowerCAmelCase )

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase )



