





"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  '''perceiver'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_56		,SCREAMING_SNAKE_CASE__=12_80		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=26		,SCREAMING_SNAKE_CASE__=8		,SCREAMING_SNAKE_CASE__=8		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="kv"		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=2_62		,SCREAMING_SNAKE_CASE__=20_48		,SCREAMING_SNAKE_CASE__=56		,SCREAMING_SNAKE_CASE__=[3_68, 4_96]		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=19_20		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=[1, 16, 2_24, 2_24]		,**SCREAMING_SNAKE_CASE__		,)      ->				Tuple:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_latents
             __SCREAMING_SNAKE_CASE       :int		    =  d_latents
             __SCREAMING_SNAKE_CASE       :Any		    =  d_model
             __SCREAMING_SNAKE_CASE       :Any		    =  num_blocks
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  num_self_attends_per_block
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_self_attention_heads
             __SCREAMING_SNAKE_CASE       :str		    =  num_cross_attention_heads
             __SCREAMING_SNAKE_CASE       :Any		    =  qk_channels
             __SCREAMING_SNAKE_CASE       :int		    =  v_channels
             __SCREAMING_SNAKE_CASE       :str		    =  cross_attention_shape_for_attention
             __SCREAMING_SNAKE_CASE       :Dict		    =  self_attention_widening_factor
             __SCREAMING_SNAKE_CASE       :Tuple		    =  cross_attention_widening_factor
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :int		    =  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  initializer_range
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  layer_norm_eps
             __SCREAMING_SNAKE_CASE       :int		    =  use_query_residual
             # masked language modeling attributes
             __SCREAMING_SNAKE_CASE       :Any		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Dict		    =  max_position_embeddings
             # image classification attributes
             __SCREAMING_SNAKE_CASE       :Tuple		    =  image_size
             # flow attributes
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  train_size
             # multimodal autoencoding attributes
             __SCREAMING_SNAKE_CASE       :Any		    =  num_frames
             __SCREAMING_SNAKE_CASE       :int		    =  audio_samples_per_frame
             __SCREAMING_SNAKE_CASE       :str		    =  samples_per_patch
             __SCREAMING_SNAKE_CASE       :Dict		    =  output_shape






class  _SCREAMING_SNAKE_CASE(       A  ):




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''inputs''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)




       @property
       def    _UpperCamelCase					(						self							)      ->				float:





             """simple docstring"""






             return 1E-4




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 3		,SCREAMING_SNAKE_CASE__ = 40		,SCREAMING_SNAKE_CASE__ = 40		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             if isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                   __SCREAMING_SNAKE_CASE       :str		    =  compute_effective_axis_dimension(
                       SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_batch		,num_token_to_add=0							)
                   # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  compute_effective_axis_dimension(
                       SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_sequence		,num_token_to_add=SCREAMING_SNAKE_CASE__							)
                   # Generate dummy inputs according to compute batch and sequence
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [''' '''.join(['''a''']							) * seq_length] * batch_size
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  dict(preprocessor(SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  inputs.pop('''input_ids'''							)
                   return inputs
             elif isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) and preprocessor.model_input_names[0] == "pixel_values":
                   # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_batch							)
                   __SCREAMING_SNAKE_CASE       :str		    =  self._generate_dummy_images(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  dict(preprocessor(images=SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE       :str		    =  inputs.pop('''pixel_values'''							)
                   return inputs
             else:
                   raise ValueError(
                       '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''							)






"""simple docstring"""
import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[int]			,       a_					:		Any							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForSequenceClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict['''projector.weight''']
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  downstream_dict['''projector.bias''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.post_net.linear.weight''']
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.post_net.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		List[Any]			,       a_					:		List[Any]							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForAudioFrameClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.linear.weight''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[Any]			,       a_					:		int							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  UniSpeechSatForXVector.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''connector.weight''']
      __SCREAMING_SNAKE_CASE       :Tuple		    =  downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict[
                f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      __SCREAMING_SNAKE_CASE       :Any		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      __SCREAMING_SNAKE_CASE       :Dict		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict['''objective.W''']
      return model



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Union[str, Any]			,       a_					:		Any			,       a_					:		Union[str, Any]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :str		    =  torch.load(a_			,       map_location='''cpu'''							)

      __SCREAMING_SNAKE_CASE       :str		    =  checkpoint['''Downstream''']

      __SCREAMING_SNAKE_CASE       :str		    =  UniSpeechSatConfig.from_pretrained(a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  WavaVecaFeatureExtractor.from_pretrained(
          a_			,       return_attention_mask=a_			,       do_normalize=a_							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hf_config.architectures[0]
      if arch.endswith('''ForSequenceClassification'''							):
            __SCREAMING_SNAKE_CASE       :str		    =  convert_classification(a_			,       a_			,       a_							)
      elif arch.endswith('''ForAudioFrameClassification'''							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  convert_diarization(a_			,       a_			,       a_							)
      elif arch.endswith('''ForXVector'''							):
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  convert_xvector(a_			,       a_			,       a_							)
      else:
            raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''							)

      if hf_config.use_weighted_layer_sum:
            __SCREAMING_SNAKE_CASE       :Dict		    =  checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(a_							)
      hf_model.save_pretrained(a_							)


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)