


import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Any							,	SCREAMING_SNAKE_CASE_ :		Optional[Any]							,	SCREAMING_SNAKE_CASE_ :		Dict						):





			"""simple docstring"""




			UpperCamelCase								=       WavaVecaForSequenceClassification.from_pretrained(UpperCAmelCase__							,	config=UpperCAmelCase__						)
			UpperCamelCase								=       downstream_dict["""projector.weight"""]
			UpperCamelCase								=       downstream_dict["""projector.bias"""]
			UpperCamelCase								=       downstream_dict["""model.post_net.linear.weight"""]
			UpperCamelCase								=       downstream_dict["""model.post_net.linear.bias"""]
			return model






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		List[Any]							,	SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		Optional[int]						):





			"""simple docstring"""




			UpperCamelCase								=       WavaVecaForAudioFrameClassification.from_pretrained(UpperCAmelCase__							,	config=UpperCAmelCase__						)
			UpperCamelCase								=       downstream_dict["""model.linear.weight"""]
			UpperCamelCase								=       downstream_dict["""model.linear.bias"""]
			return model






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Any							,	SCREAMING_SNAKE_CASE_ :		Optional[Any]							,	SCREAMING_SNAKE_CASE_ :		str						):





			"""simple docstring"""




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

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






@torch.no_grad()
def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		int							,	SCREAMING_SNAKE_CASE_ :		Any							,	SCREAMING_SNAKE_CASE_ :		int							,	SCREAMING_SNAKE_CASE_ :		str						):





			"""simple docstring"""




			UpperCamelCase								=       torch.load(UpperCAmelCase__							,	map_location="""cpu"""						)

			UpperCamelCase								=       checkpoint["""Downstream"""]

			UpperCamelCase								=       WavaVecaConfig.from_pretrained(UpperCAmelCase__						)
			UpperCamelCase								=       WavaVecaFeatureExtractor.from_pretrained(
			    UpperCAmelCase__							,	return_attention_mask=UpperCAmelCase__							,	do_normalize=UpperCAmelCase__						)

			UpperCamelCase								=       hf_config.architectures[0]
			if arch.endswith("""ForSequenceClassification"""						):
						UpperCamelCase								=       convert_classification(UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__						)
			elif arch.endswith("""ForAudioFrameClassification"""						):
						UpperCamelCase								=       convert_diarization(UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__						)
			elif arch.endswith("""ForXVector"""						):
						UpperCamelCase								=       convert_xvector(UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__						)
			else:
						raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'						)

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

			hf_feature_extractor.save_pretrained(UpperCAmelCase__						)
			hf_model.save_pretrained(UpperCAmelCase__						)


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







'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
