




import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					int  ,			A__       :					Any	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForSequenceClassification.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__       :					Optional[Any]  ,			A__       :					Optional[Any]  ,			A__       :					Union[str, Any]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Tuple  ,			A__       :					Dict	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForXVector.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__       :					List[str]  ,			A__       :					Dict  ,			A__       :					List[str]  ,			A__       :					Tuple	):





       '''simple docstring'''



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

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

       __lowerCamelCase   =						WavaVecaConfig.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)







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

