


"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case												=							{
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case												=							["DebertaV2TokenizerFast"]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case												=							[
       "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDebertaV2ForMaskedLM",
       "TFDebertaV2ForQuestionAnswering",
       "TFDebertaV2ForSequenceClassification",
       "TFDebertaV2ForTokenClassification",
       "TFDebertaV2Model",
       "TFDebertaV2PreTrainedModel",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case												=							[
       "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DebertaV2ForMaskedLM",
       "DebertaV2ForMultipleChoice",
       "DebertaV2ForQuestionAnswering",
       "DebertaV2ForSequenceClassification",
       "DebertaV2ForTokenClassification",
       "DebertaV2Model",
       "DebertaV2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_deberta_va import (
       DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DebertaVaConfig,
       DebertaVaOnnxConfig,
   )
   from .tokenization_deberta_va import DebertaVaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta_va import (
          TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
          TFDebertaVaPreTrainedModel,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta_va import (
          DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
          DebertaVaPreTrainedModel,
      )

else:
   import sys

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





"""simple docstring"""

import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)




def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""projector.weight"""]
					__snake_case        =						downstream_dict["""projector.bias"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.weight"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""model.linear.weight"""]
					__snake_case        =						downstream_dict["""model.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForXVector.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""connector.weight"""]
					__snake_case        =						downstream_dict["""connector.bias"""]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
										__snake_case        =						downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										__snake_case        =						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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




@torch.no_grad()
def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						torch.load(lowerCAmelCase					,       map_location="""cpu"""		)

					__snake_case        =						checkpoint["""Downstream"""]

					__snake_case        =						WavLMConfig.from_pretrained(lowerCAmelCase		)
					__snake_case        =						WavaVecaFeatureExtractor.from_pretrained(
					    lowerCAmelCase					,       return_attention_mask=lowerCAmelCase					,       do_normalize=lowerCAmelCase		)

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

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

					hf_feature_extractor.save_pretrained(lowerCAmelCase		)
					hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
		UpperCamelCase__			:     str			       =				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__			:     Tuple			       =				parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


