

"""simple docstring"""

import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__=1_0_0				,				lowercase__=" "      ) ->	List[str]:
 lowerCAmelCase__      :							Dict    =							text.split(lowercase__      )
 return [character.join(text[i : i + n]      ).strip() for i in range(0				,				len(lowercase__      )				,				lowercase__      )]



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	List[Any]:
 lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							[], []
 for title, text in zip(documents["title"]				,				documents["text"]      ):
  if text is not None:
   for passage in split_text(lowercase__      ):
    titles.append(title if title is not None else ""      )
    texts.append(lowercase__      )
 return {"title": titles, "text": texts}



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      ) ->	Optional[int]:
 lowerCAmelCase__      :							List[str]    =							load_dataset(
     "csv"				,				data_files=[csv_path]				,				split="train"				,				delimiter="\t"				,				column_names=["title", "text"]      )
 lowerCAmelCase__      :							List[str]    =							kb_dataset.map(
     lowercase__				,				batched=lowercase__				,				num_proc=1      )  # if you want you can load already splitted csv.
 lowerCAmelCase__      :							List[Any]    =							[kb_dataset.shard(lowercase__				,				lowercase__				,				contiguous=lowercase__      ) for i in range(lowercase__      )]
 lowerCAmelCase__      :							Tuple    =							kb_list[process_num]

 lowerCAmelCase__      :							int    =							"data_" + str(lowercase__      )
 lowerCAmelCase__      :							Optional[int]    =							os.path.join(lowercase__				,				lowercase__      )

 lowerCAmelCase__      :							Tuple    =							DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"      )
 lowerCAmelCase__      :							List[str]    =							ctx_encoder.to(device=lowercase__      )

 def embed(
     lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      ) -> dict:
  lowerCAmelCase__      :							Optional[Any]    =							ctx_tokenizer(
      documents["title"]				,				documents["text"]				,				truncation=lowercase__				,				padding="longest"				,				return_tensors="pt"      )["input_ids"]
  lowerCAmelCase__      :							Any    =							ctx_encoder(input_ids.to(device=lowercase__      )				,				return_dict=lowercase__      ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

 lowerCAmelCase__      :							Optional[int]    =							Features(
     {"text": Value("string"      ), "title": Value("string"      ), "embeddings": Sequence(Value("float32"      )      )}      )  # optional, save as float32 instead of float64 to save space

 lowerCAmelCase__      :							str    =							data_shrad.map(
     partial(lowercase__				,				ctx_encoder=lowercase__				,				ctx_tokenizer=lowercase__				,				device=lowercase__      )				,				batched=lowercase__				,				batch_size=1_6				,				features=lowercase__				,				)
 dataset.save_to_disk(lowercase__      )






def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	str:
 lowerCAmelCase__      :							int    =							[]

 for shard_address in glob(str(lowercase__      ) + "/*/"      ):
  data_shard_list.append(load_from_disk(lowercase__      )      )

 lowerCAmelCase__      :							Optional[int]    =							concatenate_datasets(lowercase__      )
 faiss.omp_set_num_threads(9_6      )

 lowerCAmelCase__      :							Optional[int]    =							faiss.IndexHNSWFlat(7_6_8				,				1_2_8				,				faiss.METRIC_INNER_PRODUCT      )
 concat.add_faiss_index("embeddings"				,				custom_index=lowercase__      )
 concat.get_index("embeddings"      ).save(
     lowercase__      )  # since we load the index in to memory,we can directly update the index in the disk









"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Optional[int]          =	(VQDiffusionScheduler,)






       def  SCREAMING_SNAKE_CASE_  (      self ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							{
            "num_vec_classes": 4_097,
            "num_train_timesteps": 100,
        }

        config.update(**snake_case      )
        return config






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							4
        lowerCAmelCase__      :							Optional[Any]    =							8
        lowerCAmelCase__      :							Union[str, Any]    =							8

        lowerCAmelCase__      :							Optional[Any]    =							torch.randint(0 ,				snake_case ,				(batch_size, height * width)      )

        return sample






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        assert False






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        def model(snake_case ,				snake_case ,				*snake_case      ):
         lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							sample.shape
         lowerCAmelCase__      :							Dict    =							torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
         lowerCAmelCase__      :							int    =							F.log_softmax(logits.double() ,				dim=1      ).float()
         return return_value

        return model






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for timesteps in [2, 5, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for num_vec_classes in [5, 100, 1_000, 4_000]:
         self.check_over_configs(num_vec_classes=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for t in [0, 50, 99]:
         self.check_over_forward(time_step=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






