



'''simple docstring'''
import argparse

import torch

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


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



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      str				):


			'''simple docstring'''







			A:      Union[str, Any]														=   WavLMForSequenceClassification.from_pretrained(snake_case__							,							config=snake_case__				)
			A:      Union[str, Any]														=   downstream_dict["""projector.weight"""]
			A:      int														=   downstream_dict["""projector.bias"""]
			A:      Any														=   downstream_dict["""model.post_net.linear.weight"""]
			A:      List[str]														=   downstream_dict["""model.post_net.linear.bias"""]
			return model



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      int							,							lowerCamelCase__   :      Any							,							lowerCamelCase__   :      Tuple				):


			'''simple docstring'''







			A:      int														=   WavLMForAudioFrameClassification.from_pretrained(snake_case__							,							config=snake_case__				)
			A:      Union[str, Any]														=   downstream_dict["""model.linear.weight"""]
			A:      Tuple														=   downstream_dict["""model.linear.bias"""]
			return model



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      str							,							lowerCamelCase__   :      Optional[Any]				):


			'''simple docstring'''







			A:      Optional[int]														=   WavLMForXVector.from_pretrained(snake_case__							,							config=snake_case__				)
			A:      Union[str, Any]														=   downstream_dict["""connector.weight"""]
			A:      Optional[Any]														=   downstream_dict["""connector.bias"""]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
						A:      Optional[int]														=   downstream_dict[
						    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
						]
						A:      Optional[Any]														=   downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

			A:      Union[str, Any]														=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
			A:      str														=   downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
			A:      Optional[int]														=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
			A:      str														=   downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
			A:      Tuple														=   downstream_dict["""objective.W"""]
			return model



@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      Optional[int]							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      List[Any]				):


			'''simple docstring'''







			A:      Optional[int]														=   torch.load(snake_case__							,							map_location="""cpu"""				)

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

			A:      Any														=   WavLMConfig.from_pretrained(snake_case__				)
			A:      Any														=   WavaVecaFeatureExtractor.from_pretrained(
			    snake_case__							,							return_attention_mask=snake_case__							,							do_normalize=snake_case__				)

			A:      Dict														=   hf_config.architectures[0]
			if arch.endswith("""ForSequenceClassification"""				):
						A:      Union[str, Any]														=   convert_classification(snake_case__							,							snake_case__							,							snake_case__				)
			elif arch.endswith("""ForAudioFrameClassification"""				):
						A:      Tuple														=   convert_diarization(snake_case__							,							snake_case__							,							snake_case__				)
			elif arch.endswith("""ForXVector"""				):
						A:      int														=   convert_xvector(snake_case__							,							snake_case__							,							snake_case__				)
			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(snake_case__				)
			hf_model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE :      List[str]             =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.')
					__SCREAMING_SNAKE_CASE :      List[Any]             =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


__SCREAMING_SNAKE_CASE :      int             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_					):
	"""simple docstring"""


	A__    :    List[str]						=				'''convnext'''


	def __init__(		self					,   A=3					,   A=4					,   A=4					,   A=None					,   A=None					,   A="gelu"					,   A=0.02					,   A=1e-12					,   A=1e-6					,   A=0.0					,   A=2_24					,   A=None					,   A=None					,   **A					,   ) ->							Dict:
				super().__init__(**A			)

				A:      List[str]														=   num_channels
				A:      Optional[Any]														=   patch_size
				A:      List[str]														=   num_stages
				A:      Tuple														=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
				A:      List[Any]														=   [3, 3, 9, 3] if depths is None else depths
				A:      int														=   hidden_act
				A:      int														=   initializer_range
				A:      Optional[int]														=   layer_norm_eps
				A:      Tuple														=   layer_scale_init_value
				A:      int														=   drop_path_rate
				A:      Tuple														=   image_size
				A:      Union[str, Any]														=   ["""stem"""] + [f'stage{idx}' for idx in range(1					,   len(self.depths			) + 1			)]
				A							,		A:      List[str]														=   get_aligned_output_features_output_indices(
				    out_features=A					,   out_indices=A					,   stage_names=self.stage_names			)







class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Any						=				version.parse('''1.11'''					)


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





	@property
	def      a__ (		self			) ->							float:
				return 1e-5





