"""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_		(   lowerCamelCase   ,     lowerCamelCase=1_0_0   ,     lowerCamelCase=" "			):
				UpperCAmelCase__			 =				text.split(_lowercase			)
				return [character.join(text[i : i + n]			).strip() for i in range(0   ,     len(_lowercase			)   ,     _lowercase			)]



def     a_		(   lowerCamelCase			):
				UpperCAmelCase__			 =				[], []
				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     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				load_dataset(
				    'csv'   ,     data_files=[csv_path]   ,     split='train'   ,     delimiter='\t'   ,     column_names=['title', 'text']			)
				UpperCAmelCase__			 =				kb_dataset.map(
				    _lowercase   ,     batched=_lowercase   ,     num_proc=1			)  # if you want you can load already splitted csv.
				UpperCAmelCase__			 =				[kb_dataset.shard(_lowercase   ,     _lowercase   ,     contiguous=_lowercase			) for i in range(_lowercase			)]
				UpperCAmelCase__			 =				kb_list[process_num]

				UpperCAmelCase__			 =				"""data_""" + str(_lowercase			)
				UpperCAmelCase__			 =				os.path.join(_lowercase   ,     _lowercase			)

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

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

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

				UpperCAmelCase__			 =				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     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				[]

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

				UpperCAmelCase__			 =				concatenate_datasets(_lowercase			)
				faiss.omp_set_num_threads(9_6			)

				UpperCAmelCase__			 =				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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :     Union[str, Any]       =       logging.get_logger(__name__)

a  :     int       =       {
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					'hubert'





	def __init__(    self						,						A=32						,						A=768						,						A=12						,						A=12						,						A=3072						,						A="gelu"						,						A=0.1						,						A=0.1						,						A=0.1						,						A=True						,						A=0.0						,						A=0.1						,						A=0.1						,						A=0.0_2						,						A=1e-5						,						A="group"						,						A="gelu"						,						A=(512, 512, 512, 512, 512, 512, 512)						,						A=(5, 2, 2, 2, 2, 2, 2)						,						A=(10, 3, 3, 3, 3, 2, 2)						,						A=False						,						A=128						,						A=16						,						A=False						,						A=True						,						A=0.0_5						,						A=10						,						A=2						,						A=0.0						,						A=10						,						A=0						,						A="sum"						,						A=False						,						A=False						,						A=256						,						A=0						,						A=1						,						A=2						,						**A						,						)     ->     List[str]:
								super().__init__(**A						,						pad_token_id=A						,						bos_token_id=A						,						eos_token_id=A       )
								UpperCAmelCase	:						List[Any]					=						hidden_size
								UpperCAmelCase	:						List[Any]					=						feat_extract_norm
								UpperCAmelCase	:						Optional[Any]					=						feat_extract_activation
								UpperCAmelCase	:						Optional[int]					=						list(A       )
								UpperCAmelCase	:						int					=						list(A       )
								UpperCAmelCase	:						Any					=						list(A       )
								UpperCAmelCase	:						Any					=						conv_bias
								UpperCAmelCase	:						Dict					=						num_conv_pos_embeddings
								UpperCAmelCase	:						Tuple					=						num_conv_pos_embedding_groups
								UpperCAmelCase	:						str					=						len(self.conv_dim       )
								UpperCAmelCase	:						Optional[Any]					=						num_hidden_layers
								UpperCAmelCase	:						int					=						intermediate_size
								UpperCAmelCase	:						Any					=						hidden_act
								UpperCAmelCase	:						Tuple					=						num_attention_heads
								UpperCAmelCase	:						int					=						hidden_dropout
								UpperCAmelCase	:						Any					=						attention_dropout
								UpperCAmelCase	:						Any					=						activation_dropout
								UpperCAmelCase	:						str					=						feat_proj_layer_norm
								UpperCAmelCase	:						int					=						feat_proj_dropout
								UpperCAmelCase	:						List[Any]					=						final_dropout
								UpperCAmelCase	:						int					=						layerdrop
								UpperCAmelCase	:						Dict					=						layer_norm_eps
								UpperCAmelCase	:						Union[str, Any]					=						initializer_range
								UpperCAmelCase	:						Any					=						vocab_size
								UpperCAmelCase	:						Optional[int]					=						do_stable_layer_norm
								UpperCAmelCase	:						Optional[int]					=						use_weighted_layer_sum
								UpperCAmelCase	:						Dict					=						classifier_proj_size

								if (
								    (len(self.conv_stride       ) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
								    or (len(self.conv_dim       ) != self.num_feat_extract_layers)
								):
															raise ValueError(
															    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
															    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
															    f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
															    f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								UpperCAmelCase	:						Union[str, Any]					=						apply_spec_augment
								UpperCAmelCase	:						Tuple					=						mask_time_prob
								UpperCAmelCase	:						Optional[Any]					=						mask_time_length
								UpperCAmelCase	:						List[Any]					=						mask_time_min_masks
								UpperCAmelCase	:						Any					=						mask_feature_prob
								UpperCAmelCase	:						Any					=						mask_feature_length
								UpperCAmelCase	:						Tuple					=						mask_feature_min_masks

								# ctc loss
								UpperCAmelCase	:						int					=						ctc_loss_reduction
								UpperCAmelCase	:						Any					=						ctc_zero_infinity






	@property
	def 					_lowercase(    self       )     ->     Union[str, Any]:
								return functools.reduce(operator.mul						,						self.conv_stride						,						1       )

