






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase										=  {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase										=  ['''GPT2TokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase										=  [
       '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GPT2DoubleHeadsModel''',
       '''GPT2ForQuestionAnswering''',
       '''GPT2ForSequenceClassification''',
       '''GPT2ForTokenClassification''',
       '''GPT2LMHeadModel''',
       '''GPT2Model''',
       '''GPT2PreTrainedModel''',
       '''load_tf_weights_in_gpt2''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase										=  [
       '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFGPT2DoubleHeadsModel''',
       '''TFGPT2ForSequenceClassification''',
       '''TFGPT2LMHeadModel''',
       '''TFGPT2MainLayer''',
       '''TFGPT2Model''',
       '''TFGPT2PreTrainedModel''',
   ]

try:
   if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase										=  ['''TFGPT2Tokenizer''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase										=  ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
   from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
   from .tokenization_gpta import GPTaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_fast import GPTaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpta import (
          GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTaDoubleHeadsModel,
          GPTaForQuestionAnswering,
          GPTaForSequenceClassification,
          GPTaForTokenClassification,
          GPTaLMHeadModel,
          GPTaModel,
          GPTaPreTrainedModel,
          load_tf_weights_in_gpta,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gpta import (
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFGPTaDoubleHeadsModel,
          TFGPTaForSequenceClassification,
          TFGPTaLMHeadModel,
          TFGPTaMainLayer,
          TFGPTaModel,
          TFGPTaPreTrainedModel,
      )

   try:
      if not is_keras_nlp_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_tf import TFGPTaTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
   import sys

   lowerCamelCase										=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowerCamelCase										=  ['''UtteranceLevel''']
@torch.no_grad()
def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
    if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
        raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''      )

    _lowercase     =	checkpoint['Downstream']

    _lowercase     =	HubertConfig.from_pretrained(_lowercase      )
    _lowercase     =	HubertForSequenceClassification.from_pretrained(_lowercase,      config=_lowercase      )
    _lowercase     =	WavaVecaFeatureExtractor.from_pretrained(
        _lowercase,      return_attention_mask=_lowercase,      do_normalize=_lowercase      )

    if hf_congfig.use_weighted_layer_sum:
        _lowercase     =	checkpoint['Featurizer']['weights']

    _lowercase     =	downstream_dict['projector.weight']
    _lowercase     =	downstream_dict['projector.bias']
    _lowercase     =	downstream_dict['model.post_net.linear.weight']
    _lowercase     =	downstream_dict['model.post_net.linear.bias']

    hf_feature_extractor.save_pretrained(_lowercase      )
    hf_model.save_pretrained(_lowercase      )


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)
