


'''simple docstring'''
import argparse

import torch

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


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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		UniSpeechSatForSequenceClassification.from_pretrained(UpperCAmelCase_						,  config=UpperCAmelCase_ )
							A_			    =		downstream_dict['projector.weight']
							A_			    =		downstream_dict['projector.bias']
							A_			    =		downstream_dict['model.post_net.linear.weight']
							A_			    =		downstream_dict['model.post_net.linear.bias']
							return model







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCAmelCase_						,  config=UpperCAmelCase_ )
							A_			    =		downstream_dict['model.linear.weight']
							A_			    =		downstream_dict['model.linear.bias']
							return model







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		UniSpeechSatForXVector.from_pretrained(UpperCAmelCase_						,  config=UpperCAmelCase_ )
							A_			    =		downstream_dict['connector.weight']
							A_			    =		downstream_dict['connector.bias']
							for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
														A_			    =		downstream_dict[
														    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
														]
														A_			    =		downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

							A_			    =		downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
							A_			    =		downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
							A_			    =		downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
							A_			    =		downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
							A_			    =		downstream_dict['objective.W']
							return model







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		torch.load(UpperCAmelCase_						,  map_location="cpu" )

							A_			    =		checkpoint['Downstream']

							A_			    =		UniSpeechSatConfig.from_pretrained(UpperCAmelCase_ )
							A_			    =		WavaVecaFeatureExtractor.from_pretrained(
							    UpperCAmelCase_						,  return_attention_mask=UpperCAmelCase_						,  do_normalize=UpperCAmelCase_ )

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

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

							hf_feature_extractor.save_pretrained(UpperCAmelCase_ )
							hf_model.save_pretrained(UpperCAmelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				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.''')
							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









'''simple docstring'''



from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

A__						:   int				=    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A__						:   Dict				=    TaTokenizerFast

A__						:   Dict				=    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """MT5EncoderModel""",
          """MT5ForConditionalGeneration""",
          """MT5ForQuestionAnswering""",
          """MT5Model""",
          """MT5PreTrainedModel""",
          """MT5Stack""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      A__						:   Union[str, Any]				=    _LazyModule(
          __name__,
          globals()["""__file__"""],
          _import_structure,
          extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
          module_spec=__spec__,
      )






