




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


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







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







def 				lowerCAmelCase_	(     snake_case_     ):
      return {t for t in NON_ALPHA.split(snake_case_     ) if len(t.strip()     ) > 0}







class 						lowercase							:

     def __init__(  self		,     *,
         _a = 0.85		,     )						->							List[Any]:
           _A							:     Any			=						duplication_jaccard_threshold
           _A							:     int			=						NUM_PERM
           _A							:     List[str]			=						MinHashLSH(threshold=self._duplication_jaccard_threshold		,     num_perm=self._num_perm				)

           _A							:     str			=						defaultdict(_a				)

     def 					a__	(  self		,     _a		,     _a				)						->							None:
           _A							:     Any			=						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 					a__	(  self				)						->							List[List[Dict]]:
           _A							:     List[Any]			=						[]
           for base, duplicates in self._duplicate_clusters.items():
                 _A							:     Dict			=						[base] + list(_a				)
                 # reformat the cluster to be a list of dict
                 _A							:     Tuple			=						[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
                 duplicate_clusters.append(_a				)
           return duplicate_clusters



     def 					a__	(  self		,     _a				)						->							None:
           _A							:     str			=						self.get_duplicate_clusters()
           with open(_a		,     """w"""				) as f:
                 json.dump(_a		,     _a				)







def 				lowerCAmelCase_	(     snake_case_     ):
      _A			,   _A							:     Tuple			=						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_	(     snake_case_     ):
      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash,ThreadedIterator(snake_case_,max_queue_size=10000     ),chunksize=100,):
                  if data is not None:
                        yield data







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     int			=						DuplicationIndex(duplication_jaccard_threshold=snake_case_     )

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

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







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Any			=						get_tokens(snake_case_     )
      _A							:     Dict			=						get_tokens(snake_case_     )
      return len(tokensa & tokensa     ) / len(tokensa | tokensa     )


_snake_case										=       None







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







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







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

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

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

      return ds_filter, duplicate_clusters







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

_snake_case										=       "\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"







@dataclass
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						42







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     )						->							List[Any]:
           super().__init__()

           self.register_modules(
               prior=_a		,     image_encoder=_a		,     image_processor=_a		,     scheduler=_a		,     renderer=_a		,     )

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							str:
           if latents is None:
                 _A							:     str			=						randn_tensor(_a		,     generator=_a		,     device=_a		,     dtype=_a				)
           else:
                 if latents.shape != shape:
                       raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
                 _A							:     Union[str, Any]			=						latents.to(_a				)

           _A							:     int			=						latents * scheduler.init_noise_sigma
           return latents

     def 					a__	(  self		,     _a=0				)						->							Optional[Any]:
           if is_accelerate_available():
                 from accelerate import cpu_offload
           else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

           _A							:     str			=						torch.device(F'''cuda:{gpu_id}'''				)

           _A							:     Any			=						[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(_a		,     _a				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           if self.device != torch.device("""meta"""				) or not hasattr(self.image_encoder		,     """_hf_hook"""				):
                 return self.device
           for module in self.image_encoder.modules():
                 if (
                     hasattr(_a		,     """_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

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     )						->							Tuple:
           if isinstance(_a		,     _a				) and isinstance(image[0]		,     torch.Tensor				):
                 _A							:     int			=						torch.cat(_a		,     axis=0				) if image[0].ndim == 4 else torch.stack(_a		,     axis=0				)

           if not isinstance(_a		,     torch.Tensor				):
                 _A							:     Dict			=						self.image_processor(_a		,     return_tensors="""pt"""				).pixel_values[0].unsqueeze(0				)

           _A							:     int			=						image.to(dtype=self.image_encoder.dtype		,     device=_a				)

           _A							:     List[Any]			=						self.image_encoder(_a				)["""last_hidden_state"""]
           _A							:     List[Any]			=						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           _A							:     Dict			=						image_embeds.repeat_interleave(_a		,     dim=0				)

           if do_classifier_free_guidance:
                 _A							:     str			=						torch.zeros_like(_a				)

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 _A							:     List[str]			=						torch.cat([negative_image_embeds, image_embeds]				)

           return image_embeds



     @torch.no_grad()
     @replace_example_docstring(_a				)
     def __call__(  self		,     _a		,     _a = 1		,     _a = 25		,     _a = None		,     _a = None		,     _a = 4.0		,     _a = 64		,     _a = "pil"		,     _a = True		,     )						->							Union[str, Any]:

           if isinstance(_a		,     PIL.Image.Image				):
                 _A							:     List[Any]			=						1
           elif isinstance(_a		,     torch.Tensor				):
                 _A							:     Any			=						image.shape[0]
           elif isinstance(_a		,     _a				) and isinstance(image[0]		,     (torch.Tensor, PIL.Image.Image)				):
                 _A							:     Union[str, Any]			=						len(_a				)
           else:
                 raise ValueError(
                     F'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_a				)}'''				)

           _A							:     Optional[int]			=						self._execution_device

           _A							:     Tuple			=						batch_size * num_images_per_prompt

           _A							:     List[Any]			=						guidance_scale > 1.0
           _A							:     Optional[Any]			=						self._encode_image(_a		,     _a		,     _a		,     _a				)

           # prior

           self.scheduler.set_timesteps(_a		,     device=_a				)
           _A							:     Optional[int]			=						self.scheduler.timesteps

           _A							:     List[str]			=						self.prior.config.num_embeddings
           _A							:     int			=						self.prior.config.embedding_dim

           _A							:     Optional[Any]			=						self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)		,     image_embeds.dtype		,     _a		,     _a		,     _a		,     self.scheduler		,     )

           # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
           _A							:     List[Any]			=						latents.reshape(latents.shape[0]		,     _a		,     _a				)

           for i, t in enumerate(self.progress_bar(_a				)				):
                 # expand the latents if we are doing classifier free guidance
                 _A							:     List[str]			=						torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                 _A							:     int			=						self.scheduler.scale_model_input(_a		,     _a				)

                 _A							:     Tuple			=						self.prior(
                     _a		,     timestep=_a		,     proj_embedding=_a		,     ).predicted_image_embedding

                 # remove the variance
                 _A			,   _A							:     Optional[Any]			=						noise_pred.split(
                     scaled_model_input.shape[2]		,     dim=2				)  # batch_size, num_embeddings, embedding_dim

                 if do_classifier_free_guidance is not None:
                       _A			,   _A							:     Dict			=						noise_pred.chunk(2				)
                       _A							:     Tuple			=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                 _A							:     int			=						self.scheduler.step(
                     _a		,     timestep=_a		,     sample=_a		,     ).prev_sample

           if output_type == "latent":
                 return ShapEPipelineOutput(images=_a				)

           _A							:     List[str]			=						[]
           for i, latent in enumerate(_a				):
                 print()
                 _A							:     List[str]			=						self.renderer.decode(
                     latent[None, :]		,     _a		,     size=_a		,     ray_batch_size=4096		,     n_coarse_samples=64		,     n_fine_samples=128		,     )

                 images.append(_a				)

           _A							:     List[Any]			=						torch.stack(_a				)

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

           _A							:     List[str]			=						images.cpu().numpy()

           if output_type == "pil":
                 _A							:     List[Any]			=						[self.numpy_to_pil(_a				) for image in images]

           # Offload last model to CPU
           if hasattr(self		,     """final_offload_hook"""				) and self.final_offload_hook is not None:
                 self.final_offload_hook.offload()

           if not return_dict:
                 return (images,)

           return ShapEPipelineOutput(images=_a				)

