

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     lowercase						(       _a ,				_a ,				_a					):



 '''simple docstring'''


 @register_to_config
 def __init__(self       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a       ,       __a = False       ,       ) ->					str:






  """simple docstring"""





  super().__init__()

  UpperCAmelCase__							=				nn.Linear(_UpperCAmelCase       ,       _UpperCAmelCase       ,       bias=_UpperCAmelCase )

  UpperCAmelCase__							=				nn.Embedding(_UpperCAmelCase       ,       _UpperCAmelCase )
  UpperCAmelCase__							=				False

  UpperCAmelCase__							=				nn.Dropout(p=_UpperCAmelCase )

  UpperCAmelCase__							=				TaConfig(
      d_model=_UpperCAmelCase       ,       num_heads=_UpperCAmelCase       ,       d_kv=_UpperCAmelCase       ,       d_ff=_UpperCAmelCase       ,       feed_forward_proj=_UpperCAmelCase       ,       dropout_rate=_UpperCAmelCase       ,       is_decoder=_UpperCAmelCase       ,       is_encoder_decoder=_UpperCAmelCase       ,       )
  UpperCAmelCase__							=				nn.ModuleList()
  for lyr_num in range(_UpperCAmelCase ):
   UpperCAmelCase__							=				TaBlock(_UpperCAmelCase )
   self.encoders.append(_UpperCAmelCase )

  UpperCAmelCase__							=				TaLayerNorm(_UpperCAmelCase )
  UpperCAmelCase__							=				nn.Dropout(p=_UpperCAmelCase )




 def   UpperCamelCase__							(self       ,       __a       ,       __a ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				self.input_proj(_UpperCAmelCase )

  # terminal relative positional encodings
  UpperCAmelCase__							=				encoder_inputs.shape[1]
  UpperCAmelCase__							=				torch.arange(_UpperCAmelCase       ,       device=encoder_inputs.device )

  UpperCAmelCase__							=				encoder_inputs_mask.sum(-1 )
  UpperCAmelCase__							=				torch.roll(input_positions.unsqueeze(0 )       ,       tuple(seq_lens.tolist() )       ,       dims=0 )
  x += self.position_encoding(_UpperCAmelCase )

  UpperCAmelCase__							=				self.dropout_pre(_UpperCAmelCase )

  # inverted the attention mask
  UpperCAmelCase__							=				encoder_inputs.size()
  UpperCAmelCase__							=				self.get_extended_attention_mask(_UpperCAmelCase       ,       _UpperCAmelCase )

  for lyr in self.encoders:
   UpperCAmelCase__							=				lyr(_UpperCAmelCase       ,       _UpperCAmelCase )[0]
  UpperCAmelCase__							=				self.layer_norm(_UpperCAmelCase )

  return self.dropout_post(_UpperCAmelCase ), encoder_inputs_mask









import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


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

