




'''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   a_							(   __snake_case							:    List[str]     , __snake_case							:    Union[str, Any]=100     , __snake_case							:    int=" "     )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							=text.split(__snake_case     )
       return [character.join(text[i : i + n]     ).strip() for i in range(0     , len(__snake_case     )     , __snake_case     )]





def   a_							(   __snake_case							:    Dict     )							->		int:



       """simple docstring"""
       lowerCamelCase_,   lowerCamelCase_							=[], []
       for title, text in zip(documents['''title''']     , documents['''text''']     ):
              if text is not None:
                     for passage in split_text(__snake_case     ):
                            titles.append(title if title is not None else ''''''     )
                            texts.append(__snake_case     )
       return {"title": titles, "text": texts}





def   a_							(   __snake_case							:    Union[str, Any]     , __snake_case							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    Tuple     , __snake_case							:    Dict     , __snake_case							:    Any     )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							=load_dataset(
           '''csv'''     , data_files=[csv_path]     , split='''train'''     , delimiter='''\t'''     , column_names=['''title''', '''text''']     )
       lowerCamelCase_							=kb_dataset.map(
           __snake_case     , batched=__snake_case     , num_proc=1     )  # if you want you can load already splitted csv.
       lowerCamelCase_							=[kb_dataset.shard(__snake_case     , __snake_case     , contiguous=__snake_case     ) for i in range(__snake_case     )]
       lowerCamelCase_							=kb_list[process_num]

       lowerCamelCase_							='''data_''' + str(__snake_case     )
       lowerCamelCase_							=os.path.join(__snake_case     , __snake_case     )

       lowerCamelCase_							=DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''     )
       lowerCamelCase_							=ctx_encoder.to(device=__snake_case     )

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

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

       lowerCamelCase_							=data_shrad.map(
           partial(__snake_case     , ctx_encoder=__snake_case     , ctx_tokenizer=__snake_case     , device=__snake_case     )     , batched=__snake_case     , batch_size=16     , features=__snake_case     , )
       dataset.save_to_disk(__snake_case     )





def   a_							(   __snake_case							:    Optional[Any]     , __snake_case							:    Union[str, Any]     )							->		Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=[]

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

       lowerCamelCase_							=concatenate_datasets(__snake_case     )
       faiss.omp_set_num_threads(96     )

       lowerCamelCase_							=faiss.IndexHNSWFlat(768     , 128     , faiss.METRIC_INNER_PRODUCT     )
       concat.add_faiss_index('''embeddings'''     , custom_index=__snake_case     )
       concat.get_index('''embeddings'''     ).save(
           __snake_case     )  # since we load the index in to memory,we can directly update the index in the disk










'''simple docstring'''




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=(HeunDiscreteScheduler,)
       lowercase			:					int   		=10






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_0_0_1,
                  '''beta_end''': 0.0_2,
                  '''beta_schedule''': '''linear''',
              }

              config.update(**lowerCAmelCase						)
              return config






       def   lowercase__  (							self						):



              """simple docstring"""







              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],	[0.0_0_0_2, 0.0_0_2, 0.0_2]						):
                     self.check_over_configs(beta_start=lowerCAmelCase,	beta_end=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for schedule in ["linear", "scaled_linear", "exp"]:
                     self.check_over_configs(beta_schedule=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for clip_sample_range in [1.0, 2.0, 3.0]:
                     self.check_over_configs(clip_sample_range=lowerCAmelCase,	clip_sample=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for prediction_type in ["epsilon", "v_prediction", "sample"]:
                     self.check_over_configs(prediction_type=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config(prediction_type='''v_prediction'''						)
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 4.6_934e-07						) < 1e-2
                     assert abs(result_mean.item() - 6.1_112e-10						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 4.693_428_650_170_972e-07						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if str(lowerCAmelCase						).startswith('''cpu'''						):
                     # The following sum varies between 148 and 156 on mps. Why?
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              elif str(lowerCAmelCase						).startswith('''mps'''						):
                     # Larger tolerance on mps
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-2
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase,	use_karras_sigmas=lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1e-2
              assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07						) < 1e-2




