






"""simple docstring"""
import argparse

import torch

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


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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Any	    =   UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__   ,   config=UpperCamelCase__					)
	A_		:						Union[str, Any]	    =   downstream_dict["""projector.weight"""]
	A_		:						Optional[int]	    =   downstream_dict["""projector.bias"""]
	A_		:						List[str]	    =   downstream_dict["""model.post_net.linear.weight"""]
	A_		:						Optional[int]	    =   downstream_dict["""model.post_net.linear.bias"""]
	return model






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Optional[Any]	    =   UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__   ,   config=UpperCamelCase__					)
	A_		:						Dict	    =   downstream_dict["""model.linear.weight"""]
	A_		:						Any	    =   downstream_dict["""model.linear.bias"""]
	return model






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

	A_		:						Dict	    =   downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	A_		:						List[Any]	    =   downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	A_		:						Dict	    =   downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	A_		:						Any	    =   downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	A_		:						List[str]	    =   downstream_dict["""objective.W"""]
	return model






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   torch.load(UpperCamelCase__   ,   map_location="""cpu"""					)

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

	A_		:						Optional[Any]	    =   UniSpeechSatConfig.from_pretrained(UpperCamelCase__					)
	A_		:						Any	    =   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCamelCase__   ,   return_attention_mask=UpperCamelCase__   ,   do_normalize=UpperCamelCase__					)

	A_		:						Tuple	    =   hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""					):
		A_		:						List[Any]	    =   convert_classification(UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__					)
	elif arch.endswith("""ForAudioFrameClassification"""					):
		A_		:						List[Any]	    =   convert_diarization(UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__					)
	elif arch.endswith("""ForXVector"""					):
		A_		:						Tuple	    =   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_		:						List[str]	    =   checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(UpperCamelCase__					)
	hf_model.save_pretrained(UpperCamelCase__					)


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









"""simple docstring"""
import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_lowerCAmelCase         =							"."


if __name__ == "__main__":
							_lowerCAmelCase         =							os.path.join(REPO_PATH, "utils/documentation_tests.txt")
							_lowerCAmelCase         =							[]
							_lowerCAmelCase         =							[]
							with open(doctest_file_path) as fp:
														for line in fp:
																					_lowerCAmelCase         =							line.strip()
																					_lowerCAmelCase         =							os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														_lowerCAmelCase         =							"\n".join(non_existent_paths)
														raise ValueError(F'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
							if all_paths != sorted(all_paths):
														raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")

