


import argparse

import torch

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


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






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[Any]								=      UniSpeechSatForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE      , config=_SCREAMING_SNAKE_CASE   )
		_UpperCAmelCase		:       Optional[Any]								=      downstream_dict["projector.weight"]
		_UpperCAmelCase		:       Tuple								=      downstream_dict["projector.bias"]
		_UpperCAmelCase		:       Dict								=      downstream_dict["model.post_net.linear.weight"]
		_UpperCAmelCase		:       Dict								=      downstream_dict["model.post_net.linear.bias"]
		return model






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Union[str, Any]								=      UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE      , config=_SCREAMING_SNAKE_CASE   )
		_UpperCAmelCase		:       Union[str, Any]								=      downstream_dict["model.linear.weight"]
		_UpperCAmelCase		:       Optional[Any]								=      downstream_dict["model.linear.bias"]
		return model






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       List[Any]								=      UniSpeechSatForXVector.from_pretrained(_SCREAMING_SNAKE_CASE      , config=_SCREAMING_SNAKE_CASE   )
		_UpperCAmelCase		:       str								=      downstream_dict["connector.weight"]
		_UpperCAmelCase		:       Tuple								=      downstream_dict["connector.bias"]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
				_UpperCAmelCase		:       Tuple								=      downstream_dict[
				    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
				]
				_UpperCAmelCase		:       List[str]								=      downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

		_UpperCAmelCase		:       str								=      downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
		_UpperCAmelCase		:       int								=      downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
		_UpperCAmelCase		:       List[Any]								=      downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
		_UpperCAmelCase		:       Tuple								=      downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
		_UpperCAmelCase		:       List[Any]								=      downstream_dict["objective.W"]
		return model






@torch.no_grad()
def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       List[str]								=      torch.load(_SCREAMING_SNAKE_CASE      , map_location="cpu"   )

		_UpperCAmelCase		:       Union[str, Any]								=      checkpoint["Downstream"]

		_UpperCAmelCase		:       Tuple								=      UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
		_UpperCAmelCase		:       List[Any]								=      WavaVecaFeatureExtractor.from_pretrained(
		    _SCREAMING_SNAKE_CASE      , return_attention_mask=_SCREAMING_SNAKE_CASE      , do_normalize=_SCREAMING_SNAKE_CASE   )

		_UpperCAmelCase		:       Tuple								=      hf_config.architectures[0]
		if arch.endswith("ForSequenceClassification"   ):
				_UpperCAmelCase		:       int								=      convert_classification(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE   )
		elif arch.endswith("ForAudioFrameClassification"   ):
				_UpperCAmelCase		:       Optional[Any]								=      convert_diarization(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE   )
		elif arch.endswith("ForXVector"   ):
				_UpperCAmelCase		:       Tuple								=      convert_xvector(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE   )
		else:
				raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""   )

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

		hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )
		hf_model.save_pretrained(_SCREAMING_SNAKE_CASE   )


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)







'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__    	=     {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MraForMaskedLM',
						    'MraForMultipleChoice',
						    'MraForQuestionAnswering',
						    'MraForSequenceClassification',
						    'MraForTokenClassification',
						    'MraLayer',
						    'MraModel',
						    'MraPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure)



