




"""simple docstring"""

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCamelCase__			:     Tuple			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[Any]			       =				{
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}
class      A__   (   lowercase_  ):
	A_				:					Any      =   """deberta"""




	def __init__( self	:						List[Any]   ,     lowercase	:						str=50_265   ,     lowercase	:						Union[str, Any]=768   ,     lowercase	:						List[Any]=12   ,     lowercase	:						List[str]=12   ,     lowercase	:						Tuple=3_072   ,     lowercase	:						Tuple="gelu"   ,     lowercase	:						Union[str, Any]=0.1   ,     lowercase	:						Tuple=0.1   ,     lowercase	:						str=512   ,     lowercase	:						int=0   ,     lowercase	:						Union[str, Any]=0.02   ,     lowercase	:						Any=1e-7   ,     lowercase	:						Any=False   ,     lowercase	:						List[str]=-1   ,     lowercase	:						int=0   ,     lowercase	:						Any=True   ,     lowercase	:						Union[str, Any]=None   ,     lowercase	:						int=0   ,     lowercase	:						Tuple="gelu"   ,     **lowercase	:						Optional[int]   ,     )       ->			Tuple:
						super().__init__(**lowercase						)

						__snake_case        =						hidden_size
						__snake_case        =						num_hidden_layers
						__snake_case        =						num_attention_heads
						__snake_case        =						intermediate_size
						__snake_case        =						hidden_act
						__snake_case        =						hidden_dropout_prob
						__snake_case        =						attention_probs_dropout_prob
						__snake_case        =						max_position_embeddings
						__snake_case        =						type_vocab_size
						__snake_case        =						initializer_range
						__snake_case        =						relative_attention
						__snake_case        =						max_relative_positions
						__snake_case        =						pad_token_id
						__snake_case        =						position_biased_input

						# Backwards compatibility
						if type(lowercase						) == str:
											__snake_case        =						[x.strip() for x in pos_att_type.lower().split("""|"""						)]

						__snake_case        =						pos_att_type
						__snake_case        =						vocab_size
						__snake_case        =						layer_norm_eps

						__snake_case        =						kwargs.get("""pooler_hidden_size"""   ,     lowercase						)
						__snake_case        =						pooler_dropout
						__snake_case        =						pooler_hidden_act

class      A__   (   lowercase_  ):




	@property
	def 				a_		( self	:						List[Any]						)       ->			Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__snake_case        =						{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
											__snake_case        =						{0: """batch""", 1: """sequence"""}
						if self._config.type_vocab_size > 0:
											return OrderedDict(
											    [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]						)
						else:
											return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]						)




	@property
	def 				a_		( self	:						str						)       ->			int:
						return 12




	def 				a_		( self	:						Tuple   ,     lowercase	:						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,     lowercase	:						int = -1   ,     lowercase	:						int = -1   ,     lowercase	:						int = -1   ,     lowercase	:						bool = False   ,     lowercase	:						Optional["TensorType"] = None   ,     lowercase	:						int = 3   ,     lowercase	:						int = 40   ,     lowercase	:						int = 40   ,     lowercase	:						"PreTrainedTokenizerBase" = None   ,     )       ->			Mapping[str, Any]:
						__snake_case        =						super().generate_dummy_inputs(preprocessor=lowercase   ,     framework=lowercase						)
						if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
						return dummy_inputs








"""simple docstring"""

import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)




def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""projector.weight"""]
					__snake_case        =						downstream_dict["""projector.bias"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.weight"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""model.linear.weight"""]
					__snake_case        =						downstream_dict["""model.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForXVector.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""connector.weight"""]
					__snake_case        =						downstream_dict["""connector.bias"""]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
										__snake_case        =						downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										__snake_case        =						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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




@torch.no_grad()
def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						torch.load(lowerCAmelCase					,       map_location="""cpu"""		)

					__snake_case        =						checkpoint["""Downstream"""]

					__snake_case        =						WavLMConfig.from_pretrained(lowerCAmelCase		)
					__snake_case        =						WavaVecaFeatureExtractor.from_pretrained(
					    lowerCAmelCase					,       return_attention_mask=lowerCAmelCase					,       do_normalize=lowerCAmelCase		)

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

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

					hf_feature_extractor.save_pretrained(lowerCAmelCase		)
					hf_model.save_pretrained(lowerCAmelCase		)


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


