



import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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





def 						__lowerCamelCase       (      __magic_name__	:  List[Any]     , __magic_name__	:  List[str]     , __magic_name__	:  List[str]						):
							a__:       Union[str, Any]											=WavLMForSequenceClassification.from_pretrained(__magic_name__     , config=__magic_name__						)
							a__:       List[Any]											=downstream_dict["projector.weight"]
							a__:       Optional[Any]											=downstream_dict["projector.bias"]
							a__:       int											=downstream_dict["model.post_net.linear.weight"]
							a__:       Tuple											=downstream_dict["model.post_net.linear.bias"]
							return model





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]     , __magic_name__	:  str     , __magic_name__	:  List[str]						):
							a__:       int											=WavLMForAudioFrameClassification.from_pretrained(__magic_name__     , config=__magic_name__						)
							a__:       Any											=downstream_dict["model.linear.weight"]
							a__:       Optional[int]											=downstream_dict["model.linear.bias"]
							return model





def 						__lowerCamelCase       (      __magic_name__	:  Optional[int]     , __magic_name__	:  Dict     , __magic_name__	:  Any						):
							a__:       int											=WavLMForXVector.from_pretrained(__magic_name__     , config=__magic_name__						)
							a__:       Any											=downstream_dict["connector.weight"]
							a__:       Union[str, Any]											=downstream_dict["connector.bias"]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
														a__:       Union[str, Any]											=downstream_dict[
														    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
														]
														a__:       Union[str, Any]											=downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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





@torch.no_grad()
def 						__lowerCamelCase       (      __magic_name__	:  Any     , __magic_name__	:  Any     , __magic_name__	:  int     , __magic_name__	:  Any						):
							a__:       Any											=torch.load(__magic_name__     , map_location="cpu"						)

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

							a__:       List[Any]											=WavLMConfig.from_pretrained(__magic_name__						)
							a__:       List[str]											=WavaVecaFeatureExtractor.from_pretrained(
							    __magic_name__     , return_attention_mask=__magic_name__     , do_normalize=__magic_name__						)

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

							if hf_config.use_weighted_layer_sum:
														a__:       Optional[Any]											=checkpoint["Featurizer"]["weights"]

							hf_feature_extractor.save_pretrained(__magic_name__						)
							hf_model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__UpperCAmelCase						  =     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						  =     parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase						  =     logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer


		class 	lowerCamelCase__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
							    F"tpu_num_cores: {training_args.tpu_num_cores}"     , )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														a__:       Union[str, Any]											=Trainer(
														    model=DummyModel()     , args=__magic_name__     , data_collator=DummyDataCollator()     , eval_dataset=__magic_name__     , compute_metrics=__magic_name__     , )
														a__:       str											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

														a__:       Optional[int]											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





