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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=1_00		,       __lowerCAmelCase=" "    )->	Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase     :			List[Any]     	=text.split(__lowerCAmelCase    )
      return [character.join(text[i : i + n]    ).strip() for i in range(0		,       len(__lowerCAmelCase    )		,       __lowerCAmelCase    )]





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Any:






      '''simple docstring'''





      UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=[], []
      for title, text in zip(documents['''title''']		,       documents['''text''']    ):
            if text is not None:
                  for passage in split_text(__lowerCAmelCase    ):
                        titles.append(title if title is not None else ''''''    )
                        texts.append(__lowerCAmelCase    )
      return {"title": titles, "text": texts}





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	str:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	=load_dataset(
          '''csv'''		,       data_files=[csv_path]		,       split='''train'''		,       delimiter='''\t'''		,       column_names=['''title''', '''text''']    )
      UpperCAmelCase     :			List[str]     	=kb_dataset.map(
          __lowerCAmelCase		,       batched=__lowerCAmelCase		,       num_proc=1    )  # if you want you can load already splitted csv.
      UpperCAmelCase     :			Tuple     	=[kb_dataset.shard(__lowerCAmelCase		,       __lowerCAmelCase		,       contiguous=__lowerCAmelCase    ) for i in range(__lowerCAmelCase    )]
      UpperCAmelCase     :			Optional[int]     	=kb_list[process_num]

      UpperCAmelCase     :			int     	='''data_''' + str(__lowerCAmelCase    )
      UpperCAmelCase     :			Tuple     	=os.path.join(__lowerCAmelCase		,       __lowerCAmelCase    )

      UpperCAmelCase     :			Any     	=DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''    )
      UpperCAmelCase     :			List[Any]     	=ctx_encoder.to(device=__lowerCAmelCase    )

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

      UpperCAmelCase     :			int     	=Features(
          {'''text''': Value('''string'''    ), '''title''': Value('''string'''    ), '''embeddings''': Sequence(Value('''float32'''    )    )}    )  # optional, save as float32 instead of float64 to save space

      UpperCAmelCase     :			str     	=data_shrad.map(
          partial(__lowerCAmelCase		,       ctx_encoder=__lowerCAmelCase		,       ctx_tokenizer=__lowerCAmelCase		,       device=__lowerCAmelCase    )		,       batched=__lowerCAmelCase		,       batch_size=16		,       features=__lowerCAmelCase		,       )
      dataset.save_to_disk(__lowerCAmelCase    )





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	List[str]:






      '''simple docstring'''





      UpperCAmelCase     :			Union[str, Any]     	=[]

      for shard_address in glob(str(__lowerCAmelCase    ) + '''/*/'''    ):
            data_shard_list.append(load_from_disk(__lowerCAmelCase    )    )

      UpperCAmelCase     :			Tuple     	=concatenate_datasets(__lowerCAmelCase    )
      faiss.omp_set_num_threads(96    )

      UpperCAmelCase     :			Optional[int]     	=faiss.IndexHNSWFlat(7_68		,       1_28		,       faiss.METRIC_INNER_PRODUCT    )
      concat.add_faiss_index('''embeddings'''		,       custom_index=__lowerCAmelCase    )
      concat.get_index('''embeddings'''    ).save(
          __lowerCAmelCase    )  # since we load the index in to memory,we can directly update the index in the disk

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Dict 	=   (VQDiffusionScheduler,)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	={
           '''num_vec_classes''': 4097,
           '''num_train_timesteps''': 100,
       }

       config.update(**snake_case__    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=4
       UpperCAmelCase     :			Optional[int]     	=8
       UpperCAmelCase     :			Optional[Any]     	=8

       UpperCAmelCase     :			str     	=torch.randint(0		, snake_case__		, (batch_size, height * width)    )

       return sample
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       assert False
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       def model(snake_case__		, snake_case__		, *snake_case__    ):
             UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=sample.shape
             UpperCAmelCase     :			Tuple     	=torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)    )
             UpperCAmelCase     :			List[str]     	=F.log_softmax(logits.double()		, dim=1    ).float()
             return return_value

       return model
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       for num_vec_classes in [5, 100, 1000, 4000]:
             self.check_over_configs(num_vec_classes=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       pass
