"""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(lowerCamelCase	)
				return [character.join(text[i : i + n]	).strip() for i in range(0 ,      len(lowerCamelCase	) ,      lowerCamelCase	)]



def   a_						( lowerCamelCase	):
				UpperCAmelCase__				,     UpperCAmelCase__					=							[], []
				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   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(
				    lowerCamelCase ,      batched=lowerCamelCase ,      num_proc=1	)  # if you want you can load already splitted csv.
				UpperCAmelCase__					=							[kb_dataset.shard(lowerCamelCase ,      lowerCamelCase ,      contiguous=lowerCamelCase	) for i in range(lowerCamelCase	)]
				UpperCAmelCase__					=							kb_list[process_num]

				UpperCAmelCase__					=							'data_' + str(lowerCamelCase	)
				UpperCAmelCase__					=							os.path.join(lowerCamelCase ,      lowerCamelCase	)

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

				def embed(
				    lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	) -> dict:
								UpperCAmelCase__					=							ctx_tokenizer(
								    documents['title'] ,      documents['text'] ,      truncation=lowerCamelCase ,      padding='longest' ,      return_tensors='pt'	)['input_ids']
								UpperCAmelCase__					=							ctx_encoder(input_ids.to(device=lowerCamelCase	) ,      return_dict=lowerCamelCase	).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(lowerCamelCase ,      ctx_encoder=lowerCamelCase ,      ctx_tokenizer=lowerCamelCase ,      device=lowerCamelCase	) ,      batched=lowerCamelCase ,      batch_size=1_6 ,      features=lowerCamelCase ,      )
				dataset.save_to_disk(lowerCamelCase	)







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

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

				UpperCAmelCase__					=							concatenate_datasets(lowerCamelCase	)
				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=lowerCamelCase	)
				concat.get_index('embeddings'	).save(
				    lowerCamelCase	)  # since we load the index in to memory,we can directly update the index in the disk




"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase__  :  Any											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"gpt2"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__(    self       :     Any   ,lowerCamelCase__       :     List[Any]=50_257   ,lowerCamelCase__       :     Union[str, Any]=1_024   ,lowerCamelCase__       :     Dict=768   ,lowerCamelCase__       :     List[str]=12   ,lowerCamelCase__       :     Dict=12   ,lowerCamelCase__       :     List[str]=None   ,lowerCamelCase__       :     List[str]="gelu_new"   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Dict=0.1   ,lowerCamelCase__       :     str=0.1   ,lowerCamelCase__       :     Union[str, Any]=1e-5   ,lowerCamelCase__       :     Optional[Any]=0.0_2   ,lowerCamelCase__       :     str="cls_index"   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     List[Any]=None   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Tuple=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]=50_256   ,lowerCamelCase__       :     Union[str, Any]=50_256   ,lowerCamelCase__       :     Union[str, Any]=False   ,lowerCamelCase__       :     List[Any]=False   ,**lowerCamelCase__       :     Tuple   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							n_positions
									UpperCAmelCase__					=							n_embd
									UpperCAmelCase__					=							n_layer
									UpperCAmelCase__					=							n_head
									UpperCAmelCase__					=							n_inner
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							resid_pdrop
									UpperCAmelCase__					=							embd_pdrop
									UpperCAmelCase__					=							attn_pdrop
									UpperCAmelCase__					=							layer_norm_epsilon
									UpperCAmelCase__					=							initializer_range
									UpperCAmelCase__					=							summary_type
									UpperCAmelCase__					=							summary_use_proj
									UpperCAmelCase__					=							summary_activation
									UpperCAmelCase__					=							summary_first_dropout
									UpperCAmelCase__					=							summary_proj_to_labels
									UpperCAmelCase__					=							scale_attn_weights
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							scale_attn_by_inverse_layer_idx
									UpperCAmelCase__					=							reorder_and_upcast_attn

									UpperCAmelCase__					=							bos_token_id
									UpperCAmelCase__					=							eos_token_id

									super().__init__(bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,**lowerCamelCase__      )






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     PretrainedConfig   ,lowerCamelCase__       :     str = "default"   ,lowerCamelCase__       :     List[PatchingSpec] = None   ,lowerCamelCase__       :     bool = False   ,):
									super().__init__(lowerCamelCase__   ,task=lowerCamelCase__   ,patching_specs=lowerCamelCase__   ,use_past=lowerCamelCase__      )
									if not getattr(self._config   ,'pad_token_id'   ,lowerCamelCase__      ):
													# TODO: how to do that better?
													UpperCAmelCase__					=							0







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
									if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__   ,direction='inputs'      )
													UpperCAmelCase__					=							{0: 'batch', 1: 'past_sequence + sequence'}
									else:
													UpperCAmelCase__					=							{0: 'batch', 1: 'sequence'}

									return common_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									return self._config.n_layer







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return self._config.n_head







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     PreTrainedTokenizer   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[TensorType] = None   ,):
									UpperCAmelCase__					=							super(lowerCamelCase__   ,self      ).generate_dummy_inputs(
									    lowerCamelCase__   ,batch_size=lowerCamelCase__   ,seq_length=lowerCamelCase__   ,is_pair=lowerCamelCase__   ,framework=lowerCamelCase__      )

									# We need to order the input in the way they appears in the forward()
									UpperCAmelCase__					=							OrderedDict({'input_ids': common_inputs['input_ids']}      )

									# Need to add the past_keys
									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
													else:
																	import torch

																	UpperCAmelCase__				,     UpperCAmelCase__					=							common_inputs['input_ids'].shape
																	# Not using the same length for past_key_values
																	UpperCAmelCase__					=							seqlen + 2
																	UpperCAmelCase__					=							(
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	UpperCAmelCase__					=							[
																	    (torch.zeros(lowerCamelCase__      ), torch.zeros(lowerCamelCase__      )) for _ in range(self.num_layers      )
																	]

									UpperCAmelCase__					=							common_inputs['attention_mask']
									if self.use_past:
													UpperCAmelCase__					=							ordered_inputs['attention_mask'].dtype
													UpperCAmelCase__					=							torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__   ,lowerCamelCase__   ,dtype=lowerCamelCase__      )]   ,dim=1      )

									return ordered_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 13



