





import argparse

import torch

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


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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""projector.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""projector.bias"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.post_net.linear.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.post_net.linear.bias"""]
		return model






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Dict       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.linear.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.linear.bias"""]
		return model






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForXVector.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""connector.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""connector.bias"""]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
				SCREAMING_SNAKE_CASE__								=   downstream_dict[
				    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
				]
				SCREAMING_SNAKE_CASE__								=   downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   torch.load(UpperCamelCase__       , map_location="""cpu"""     )

		SCREAMING_SNAKE_CASE__								=   checkpoint["""Downstream"""]

		SCREAMING_SNAKE_CASE__								=   WavaVecaConfig.from_pretrained(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   WavaVecaFeatureExtractor.from_pretrained(
		    UpperCamelCase__       , return_attention_mask=UpperCamelCase__       , do_normalize=UpperCamelCase__     )

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

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

		hf_feature_extractor.save_pretrained(UpperCamelCase__     )
		hf_model.save_pretrained(UpperCamelCase__     )


if __name__ == "__main__":
	_lowerCamelCase 					=      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.')
	_lowerCamelCase 					=      parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		torch.FloatTensor


class     _lowerCamelCase       (     snake_case_     ,		snake_case_					):




 '''simple docstring'''


 @register_to_config
 def __init__(   self   ,   __lowercase = 16   ,   __lowercase = 88   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 32   ,   __lowercase = None   ,   __lowercase = False   ,   __lowercase = None   ,   __lowercase = "geglu"   ,   __lowercase = True   ,   __lowercase = True   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Optional[int]	  =      num_attention_heads
  __A	:		Dict	  =      attention_head_dim
  __A	:		Optional[int]	  =      num_attention_heads * attention_head_dim

  __A	:		Optional[Any]	  =      in_channels

  __A	:		Optional[int]	  =      torch.nn.GroupNorm(num_groups=__lowercase   ,   num_channels=__lowercase   ,   eps=1E-6   ,   affine=__lowercase    )
  __A	:		Optional[Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )

  # 3. Define transformers blocks
  __A	:		List[Any]	  =      nn.ModuleList(
      [
          BasicTransformerBlock(
              __lowercase   ,   __lowercase   ,   __lowercase   ,   dropout=__lowercase   ,   cross_attention_dim=__lowercase   ,   activation_fn=__lowercase   ,   attention_bias=__lowercase   ,   double_self_attention=__lowercase   ,   norm_elementwise_affine=__lowercase   ,   )
          for d in range(__lowercase    )
      ]    )

  __A	:		Union[str, Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=1   ,   __lowercase=None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A  ,__A  ,__A  ,__A	:		int	  =      hidden_states.shape
  __A	:		Union[str, Any]	  =      batch_frames // num_frames

  __A	:		Union[str, Any]	  =      hidden_states

  __A	:		List[str]	  =      hidden_states[None, :].reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   2   ,   1   ,   3   ,   4    )

  __A	:		Dict	  =      self.norm(__lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   3   ,   4   ,   2   ,   1    ).reshape(batch_size * height * width   ,   __lowercase   ,   __lowercase    )

  __A	:		Optional[Any]	  =      self.proj_in(__lowercase    )

  # 2. Blocks
  for block in self.transformer_blocks:
   __A	:		List[Any]	  =      block(
       __lowercase   ,   encoder_hidden_states=__lowercase   ,   timestep=__lowercase   ,   cross_attention_kwargs=__lowercase   ,   class_labels=__lowercase   ,   )

  # 3. Output
  __A	:		Optional[int]	  =      self.proj_out(__lowercase    )
  __A	:		int	  =      (
      hidden_states[None, None, :]
      .reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
      .permute(0   ,   3   ,   4   ,   1   ,   2    )
      .contiguous()
  )
  __A	:		List[str]	  =      hidden_states.reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )

  __A	:		str	  =      hidden_states + residual

  if not return_dict:
   return (output,)

  return TransformerTemporalModelOutput(sample=__lowercase    )






