




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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=1_00						,			UpperCamelCase__=" "   )     ->     Optional[int]:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Optional[int]:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  load_dataset(
				    """csv"""						,			data_files=[csv_path]						,			split="""train"""						,			delimiter="""\t"""						,			column_names=["""title""", """text"""]   )
				__lowerCAmelCase					=  kb_dataset.map(
				    UpperCamelCase__						,			batched=UpperCamelCase__						,			num_proc=1   )  # if you want you can load already splitted csv.
				__lowerCAmelCase					=  [kb_dataset.shard(UpperCamelCase__						,			UpperCamelCase__						,			contiguous=UpperCamelCase__   ) for i in range(UpperCamelCase__   )]
				__lowerCAmelCase					=  kb_list[process_num]

				__lowerCAmelCase					=  """data_""" + str(UpperCamelCase__   )
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			UpperCamelCase__   )

				__lowerCAmelCase					=  DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""   )
				__lowerCAmelCase					=  ctx_encoder.to(device=UpperCamelCase__   )

				def embed(
				    UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   ) -> dict:
								__lowerCAmelCase					=  ctx_tokenizer(
								    documents["""title"""]						,			documents["""text"""]						,			truncation=UpperCamelCase__						,			padding="""longest"""						,			return_tensors="""pt"""   )["""input_ids"""]
								__lowerCAmelCase					=  ctx_encoder(input_ids.to(device=UpperCamelCase__   )						,			return_dict=UpperCamelCase__   ).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(UpperCamelCase__						,			ctx_encoder=UpperCamelCase__						,			ctx_tokenizer=UpperCamelCase__						,			device=UpperCamelCase__   )						,			batched=UpperCamelCase__						,			batch_size=16						,			features=UpperCamelCase__						,			)
				dataset.save_to_disk(UpperCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[int]:







				'''simple docstring'''


				__lowerCAmelCase					=  []

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

				__lowerCAmelCase					=  concatenate_datasets(UpperCamelCase__   )
				faiss.omp_set_num_threads(96   )

				__lowerCAmelCase					=  faiss.IndexHNSWFlat(7_68						,			1_28						,			faiss.METRIC_INNER_PRODUCT   )
				concat.add_faiss_index("""embeddings"""						,			custom_index=UpperCamelCase__   )
				concat.get_index("""embeddings"""   ).save(
				    UpperCamelCase__   )  # since we load the index in to memory,we can directly update the index in the disk













"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




