






"""simple docstring"""







from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
  import tensorflow as tf

  from ..tf_utils import stable_softmax


if is_torch_available():
  import torch


_UpperCamelCase					      =  logging.get_logger(__name__)







@add_end_docstrings(
    snake_case   ,					R'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''   ,					)
class  lowerCamelCase__ (	snake_case			):






    def     _UpperCamelCase							(			self     ,A ):
          if self.framework == "tf":
                UpperCAmelCase          =	tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
          elif self.framework == "pt":
                UpperCAmelCase          =	torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,as_tuple=A )
          else:
                raise ValueError("""Unsupported framework""" )
          return masked_index






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.get_masked_index(A )
          UpperCAmelCase          =	np.prod(masked_index.shape )
          if numel < 1:
                raise PipelineException(
                    """fill-mask"""     ,self.model.base_model_prefix     ,F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''     ,)






    def     _UpperCamelCase							(			self     ,A ):
          if isinstance(A     ,A ):
                for model_input in model_inputs:
                      self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0] )
          else:
                for input_ids in model_inputs["input_ids"]:
                      self._ensure_exactly_one_mask_token(A )






    def     _UpperCamelCase							(			self     ,A     ,A=None     ,**A ):
          if return_tensors is None:
                UpperCAmelCase          =	self.framework
          UpperCAmelCase          =	self.tokenizer(A     ,return_tensors=A )
          self.ensure_exactly_one_mask_token(A )
          return model_inputs






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.model(**A )
          UpperCAmelCase          =	model_inputs["""input_ids"""]
          return model_outputs






    def     _UpperCamelCase							(			self     ,A     ,A=5     ,A=None ):
          # Cap top_k if there are targets
          if target_ids is not None and target_ids.shape[0] < top_k:
                UpperCAmelCase          =	target_ids.shape[0]
          UpperCAmelCase          =	model_outputs["""input_ids"""][0]
          UpperCAmelCase          =	model_outputs["""logits"""]

          if self.framework == "tf":
                UpperCAmelCase          =	tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]

                UpperCAmelCase          =	outputs.numpy()

                UpperCAmelCase          =	outputs[0, masked_index, :]
                UpperCAmelCase          =	stable_softmax(A     ,axis=-1 )
                if target_ids is not None:
                      UpperCAmelCase          =	tf.gather_nd(tf.squeeze(A     ,0 )     ,target_ids.reshape(-1     ,1 ) )
                      UpperCAmelCase          =	tf.expand_dims(A     ,0 )

                UpperCAmelCase          =	tf.math.top_k(A     ,k=A )
                UpperCAmelCase      , UpperCAmelCase          =	topk.values.numpy(), topk.indices.numpy()
          else:
                UpperCAmelCase          =	torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,as_tuple=A ).squeeze(-1 )
                # Fill mask pipeline supports only one ${mask_token} per sample

                UpperCAmelCase          =	outputs[0, masked_index, :]
                UpperCAmelCase          =	logits.softmax(dim=-1 )
                if target_ids is not None:
                      UpperCAmelCase          =	probs[..., target_ids]

                UpperCAmelCase      , UpperCAmelCase          =	probs.topk(A )

          UpperCAmelCase          =	[]
          UpperCAmelCase          =	values.shape[0] == 1
          for i, (_values, _predictions) in enumerate(zip(values.tolist()     ,predictions.tolist() ) ):
                UpperCAmelCase          =	[]
                for v, p in zip(_values     ,_predictions ):
                      # Copy is important since we're going to modify this array in place
                      UpperCAmelCase          =	input_ids.numpy().copy()
                      if target_ids is not None:
                            UpperCAmelCase          =	target_ids[p].tolist()

                      UpperCAmelCase          =	p
                      # Filter padding out:
                      UpperCAmelCase          =	tokens[np.where(tokens != self.tokenizer.pad_token_id )]
                      # Originally we skip special tokens to give readable output.
                      # For multi masks though, the other [MASK] would be removed otherwise
                      # making the output look odd, so we add them back
                      UpperCAmelCase          =	self.tokenizer.decode(A     ,skip_special_tokens=A )
                      UpperCAmelCase          =	{"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p] ), """sequence""": sequence}
                      row.append(A )
                result.append(A )
          if single_mask:
                return result[0]
          return result






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if isinstance(A     ,A ):
                UpperCAmelCase          =	[targets]
          try:
                UpperCAmelCase          =	self.tokenizer.get_vocab()
          except Exception:
                UpperCAmelCase          =	{}
          UpperCAmelCase          =	[]
          for target in targets:
                UpperCAmelCase          =	vocab.get(A     ,A )
                if id_ is None:
                      UpperCAmelCase          =	self.tokenizer(
                          A     ,add_special_tokens=A     ,return_attention_mask=A     ,return_token_type_ids=A     ,max_length=1     ,truncation=A     ,)["""input_ids"""]
                      if len(A ) == 0:
                            logger.warning(
                                F'''The specified target token `{target}` does not exist in the model vocabulary. '''
                                """We cannot replace it with anything meaningful, ignoring it""" )
                            continue
                      UpperCAmelCase          =	input_ids[0]
                      # XXX: If users encounter this pass
                      # it becomes pretty slow, so let's make sure
                      # The warning enables them to fix the input to
                      # get faster performance.
                      logger.warning(
                          F'''The specified target token `{target}` does not exist in the model vocabulary. '''
                          F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.''' )
                target_ids.append(id_ )
          UpperCAmelCase          =	list(set(A ) )
          if len(A ) == 0:
                raise ValueError("""At least one target must be provided when passed.""" )
          UpperCAmelCase          =	np.array(A )
          return target_ids






    def     _UpperCamelCase							(			self     ,A=None     ,A=None ):
          UpperCAmelCase          =	{}

          if targets is not None:
                UpperCAmelCase          =	self.get_target_ids(A     ,A )
                UpperCAmelCase          =	target_ids

          if top_k is not None:
                UpperCAmelCase          =	top_k

          if self.tokenizer.mask_token_id is None:
                raise PipelineException(
                    """fill-mask"""     ,self.model.base_model_prefix     ,"""The tokenizer does not define a `mask_token`.""" )
          return {}, {}, postprocess_params






    def __call__(			self     ,A     ,*A     ,**A ):
          UpperCAmelCase          =	super().__call__(A     ,**A )
          if isinstance(A     ,A ) and len(A ) == 1:
                return outputs[0]
          return outputs











"""simple docstring"""







import argparse

import torch

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


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

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForSequenceClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""projector.weight"""]
      UpperCAmelCase          =	downstream_dict["""projector.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForXVector.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""connector.weight"""]
      UpperCAmelCase          =	downstream_dict["""connector.bias"""]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            UpperCAmelCase          =	downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCAmelCase          =	downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	torch.load(_snake_case				,	map_location="""cpu"""							)

      UpperCAmelCase          =	checkpoint["""Downstream"""]

      UpperCAmelCase          =	WavaVecaConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	WavaVecaFeatureExtractor.from_pretrained(
          _snake_case				,	return_attention_mask=_snake_case				,	do_normalize=_snake_case							)

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

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

      hf_feature_extractor.save_pretrained(_snake_case							)
      hf_model.save_pretrained(_snake_case							)


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)



