
"""simple docstring"""





import argparse

import torch

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


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




def 	UpperCAmelCase     (		A	:						int     ,				A	:						str     ,				A	:						Tuple       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		WavaVecaForSequenceClassification.from_pretrained(A     ,				config=A       )
		_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 	UpperCAmelCase     (		A	:						str     ,				A	:						int     ,				A	:						Optional[Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		WavaVecaForAudioFrameClassification.from_pretrained(A     ,				config=A       )
		_UpperCAmelCase							 =		downstream_dict['model.linear.weight']
		_UpperCAmelCase							 =		downstream_dict['model.linear.bias']
		return model




def 	UpperCAmelCase     (		A	:						int     ,				A	:						Optional[Any]     ,				A	:						Dict       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		WavaVecaForXVector.from_pretrained(A     ,				config=A       )
		_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 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						Optional[Any]     ,				A	:						str     ,				A	:						Dict       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		torch.load(A     ,				map_location='cpu'       )

		_UpperCAmelCase							 =		checkpoint['Downstream']

		_UpperCAmelCase							 =		WavaVecaConfig.from_pretrained(A       )
		_UpperCAmelCase							 =		WavaVecaFeatureExtractor.from_pretrained(
		    A     ,				return_attention_mask=A     ,				do_normalize=A       )

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


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







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 			lowercase__			(      A,					A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''swin'''

							_UpperCAmelCase      				=					{
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}






							def __init__(  self	,       snake_case=224	,       snake_case=4	,       snake_case=3	,       snake_case=96	,       snake_case=[2, 2, 6, 2]	,       snake_case=[3, 6, 12, 24]	,       snake_case=7	,       snake_case=4.0	,       snake_case=True	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.1	,       snake_case="gelu"	,       snake_case=False	,       snake_case=0.02	,       snake_case=1E-5	,       snake_case=32	,       snake_case=None	,       snake_case=None	,       **snake_case	,       )			->							List[Any]:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		depths
									_UpperCAmelCase							 =		len(snake_case						)
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		window_size
									_UpperCAmelCase							 =		mlp_ratio
									_UpperCAmelCase							 =		qkv_bias
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		use_absolute_embeddings
									_UpperCAmelCase							 =		layer_norm_eps
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		encoder_stride
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									_UpperCAmelCase							 =		int(embed_dim * 2 ** (len(snake_case						) - 1)						)
									_UpperCAmelCase							 =		['stem'] + [f'stage{idx}' for idx in range(1	,       len(snake_case						) + 1						)]
									_UpperCAmelCase ,					_UpperCAmelCase							 =		get_aligned_output_features_output_indices(
									    out_features=snake_case	,       out_indices=snake_case	,       stage_names=self.stage_names						)







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 1E-4


