




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__			(		A__			:   Optional[Any]    ,     A__			:   Optional[int]=100    ,     A__			:   Union[str, Any]=" "      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Union[str, Any]      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   List[str]    ,     A__			:   List[str]    ,     A__			:   Union[str, Any]    ,     A__			:   List[Any]      ):





       '''simple docstring'''



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

       __lowerCamelCase  =		"""data_""" + str(A__      )
       __lowerCamelCase  =		os.path.join(A__    ,     A__      )

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

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




def    lowerCamelCase__			(		A__			:   Dict    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		[]

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

       __lowerCamelCase  =		concatenate_datasets(A__      )
       faiss.omp_set_num_threads(96      )

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







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_   							=      logging.get_logger(__name__)




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForAudioFrameClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""model.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForXVector.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""connector.weight"""]
       __lowerCamelCase  =		downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
              __lowerCamelCase  =		downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase  =		downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase  =		downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

       __lowerCamelCase  =		hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""      ):
              __lowerCamelCase  =		convert_classification(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForAudioFrameClassification"""      ):
              __lowerCamelCase  =		convert_diarization(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForXVector"""      ):
              __lowerCamelCase  =		convert_xvector(A__    ,     A__    ,     A__      )
       else:
              raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'      )

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase  =		checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser()
       parser.add_argument(
           '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
       )
       parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
       parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

