


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__       :	Optional[int]     				=logging.get_logger(__name__)

lowerCAmelCase__       :	Any     				={
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""hubert"""






							def __init__(	self       ,		lowerCAmelCase__=3_2       ,		lowerCAmelCase__=7_6_8       ,		lowerCAmelCase__=1_2       ,		lowerCAmelCase__=1_2       ,		lowerCAmelCase__=3_0_7_2       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.02       ,		lowerCAmelCase__=1E-5       ,		lowerCAmelCase__="group"       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)       ,		lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)       ,		lowerCAmelCase__=(1_0, 3, 3, 3, 3, 2, 2)       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=1_2_8       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.05       ,		lowerCAmelCase__=1_0       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=1_0       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__="sum"       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=2_5_6       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=2       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       ,		pad_token_id=lowerCAmelCase__       ,		bos_token_id=lowerCAmelCase__       ,		eos_token_id=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= hidden_size
										SCREAMING_SNAKE_CASE_						:			List[str]       						= feat_extract_norm
										SCREAMING_SNAKE_CASE_						:			str       						= feat_extract_activation
										SCREAMING_SNAKE_CASE_						:			Any       						= list(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= list(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= list(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= conv_bias
										SCREAMING_SNAKE_CASE_						:			str       						= num_conv_pos_embeddings
										SCREAMING_SNAKE_CASE_						:			Dict       						= num_conv_pos_embedding_groups
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= len(self.conv_dim    )
										SCREAMING_SNAKE_CASE_						:			Any       						= num_hidden_layers
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= intermediate_size
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Any       						= num_attention_heads
										SCREAMING_SNAKE_CASE_						:			Any       						= hidden_dropout
										SCREAMING_SNAKE_CASE_						:			List[str]       						= attention_dropout
										SCREAMING_SNAKE_CASE_						:			Tuple       						= activation_dropout
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= feat_proj_layer_norm
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= feat_proj_dropout
										SCREAMING_SNAKE_CASE_						:			int       						= final_dropout
										SCREAMING_SNAKE_CASE_						:			int       						= layerdrop
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			Tuple       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= vocab_size
										SCREAMING_SNAKE_CASE_						:			List[str]       						= do_stable_layer_norm
										SCREAMING_SNAKE_CASE_						:			Any       						= use_weighted_layer_sum
										SCREAMING_SNAKE_CASE_						:			str       						= classifier_proj_size

										if (
										    (len(self.conv_stride    ) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
										    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
										):
													raise ValueError(
													    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
													    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
													    F''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
													    F''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										SCREAMING_SNAKE_CASE_						:			int       						= apply_spec_augment
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= mask_time_prob
										SCREAMING_SNAKE_CASE_						:			str       						= mask_time_length
										SCREAMING_SNAKE_CASE_						:			Tuple       						= mask_time_min_masks
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= mask_feature_prob
										SCREAMING_SNAKE_CASE_						:			List[str]       						= mask_feature_length
										SCREAMING_SNAKE_CASE_						:			Tuple       						= mask_feature_min_masks

										# ctc loss
										SCREAMING_SNAKE_CASE_						:			List[str]       						= ctc_loss_reduction
										SCREAMING_SNAKE_CASE_						:			List[str]       						= ctc_zero_infinity






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return functools.reduce(operator.mul       ,		self.conv_stride       ,		1    )











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


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

