


"""simple docstring"""







import argparse

import torch

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


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase_  ,						config=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["projector.weight"]
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["projector.bias"]
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["model.post_net.linear.weight"]
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["model.post_net.linear.bias"]
					return model




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase_  ,						config=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["model.linear.weight"]
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["model.linear.bias"]
					return model




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				UniSpeechSatForXVector.from_pretrained(lowerCAmelCase_  ,						config=lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["connector.weight"]
					__SCREAMING_SNAKE_CASE						   =				downstream_dict["connector.bias"]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
										__SCREAMING_SNAKE_CASE						   =				downstream_dict[
										    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
										]
										__SCREAMING_SNAKE_CASE						   =				downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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




@torch.no_grad()
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				torch.load(lowerCAmelCase_  ,						map_location="cpu"   )

					__SCREAMING_SNAKE_CASE						   =				checkpoint["Downstream"]

					__SCREAMING_SNAKE_CASE						   =				UniSpeechSatConfig.from_pretrained(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				WavaVecaFeatureExtractor.from_pretrained(
					    lowerCAmelCase_  ,						return_attention_mask=lowerCAmelCase_  ,						do_normalize=lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				hf_config.architectures[0]
					if arch.endswith("ForSequenceClassification"   ):
										__SCREAMING_SNAKE_CASE						   =				convert_classification(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif arch.endswith("ForAudioFrameClassification"   ):
										__SCREAMING_SNAKE_CASE						   =				convert_diarization(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif arch.endswith("ForXVector"   ):
										__SCREAMING_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:
										__SCREAMING_SNAKE_CASE						   =				checkpoint["Featurizer"]["weights"]

					hf_feature_extractor.save_pretrained(lowerCAmelCase_   )
					hf_model.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
		a__				:							Optional[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.''')
		a__				:							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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
