

import argparse

import torch

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


logging.set_verbosity_info()
_UpperCAmelCase	:     Tuple		       = logging.get_logger(__name__)





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__     ,							config=UpperCamelCase__   )
							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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__     ,							config=UpperCamelCase__   )
							snake_case_           =					downstream_dict['model.linear.weight']
							snake_case_           =					downstream_dict['model.linear.bias']
							return model





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					UniSpeechSatForXVector.from_pretrained(UpperCamelCase__     ,							config=UpperCamelCase__   )
							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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					torch.load(UpperCamelCase__     ,							map_location='cpu'   )

							snake_case_           =					checkpoint['Downstream']

							snake_case_           =					UniSpeechSatConfig.from_pretrained(UpperCamelCase__   )
							snake_case_           =					WavaVecaFeatureExtractor.from_pretrained(
							    UpperCamelCase__     ,							return_attention_mask=UpperCamelCase__     ,							do_normalize=UpperCamelCase__   )

							snake_case_           =					hf_config.architectures[0]
							if arch.endswith('ForSequenceClassification'   ):
														snake_case_           =					convert_classification(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
							elif arch.endswith('ForAudioFrameClassification'   ):
														snake_case_           =					convert_diarization(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
							elif arch.endswith('ForXVector'   ):
														snake_case_           =					convert_xvector(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
							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(UpperCamelCase__   )
							hf_model.save_pretrained(UpperCamelCase__   )


if __name__ == "__main__":
					_UpperCAmelCase	:     List[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.""")
					_UpperCAmelCase	:     Optional[Any]		       = parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




