



"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


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

__SCREAMING_SNAKE_CASE     :  Optional[int]										=					{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}


class 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					lowercase__      :				Dict        =       'layoutlm'

					def __init__(			self ,				lowerCamelCase__=3_0_5_2_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__=5_1_2 ,				lowerCamelCase__=2 ,				lowerCamelCase__=0.0_2 ,				lowerCamelCase__=1e-12 ,				lowerCamelCase__=0 ,				lowerCamelCase__="absolute" ,				lowerCamelCase__=True ,				lowerCamelCase__=1_0_2_4 ,				**lowerCamelCase__ ,				):
									super().__init__(pad_token_id=lowerCamelCase__ ,				**lowerCamelCase__      )
									_lowerCamelCase					     = vocab_size
									_lowerCamelCase					     = hidden_size
									_lowerCamelCase					     = num_hidden_layers
									_lowerCamelCase					     = num_attention_heads
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = intermediate_size
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = max_position_embeddings
									_lowerCamelCase					     = type_vocab_size
									_lowerCamelCase					     = initializer_range
									_lowerCamelCase					     = layer_norm_eps
									_lowerCamelCase					     = position_embedding_type
									_lowerCamelCase					     = use_cache
									_lowerCamelCase					     = max_ad_position_embeddings


class 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__ = "default" ,				lowerCamelCase__ = None ,				):
									super().__init__(lowerCamelCase__ ,				task=lowerCamelCase__ ,				patching_specs=lowerCamelCase__      )
									_lowerCamelCase					     = config.max_ad_position_embeddings - 1

					@property
					def 	snake_case__					(			self      ):
									return OrderedDict(
									    [
									        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
									        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
									        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
									        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
									    ]      )





					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ = -1 ,				lowerCamelCase__ = -1 ,				lowerCamelCase__ = False ,				lowerCamelCase__ = None ,				):
									_lowerCamelCase					     = super().generate_dummy_inputs(
									    lowerCamelCase__ ,				batch_size=lowerCamelCase__ ,				seq_length=lowerCamelCase__ ,				is_pair=lowerCamelCase__ ,				framework=lowerCamelCase__      )

									# Generate a dummy bbox
									_lowerCamelCase					     = [4_8, 8_4, 7_3, 1_2_8]

									if not framework == TensorType.PYTORCH:
													raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''      )

									if not is_torch_available():
													raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''      )
									import torch

									_lowerCamelCase     ,   _lowerCamelCase					     = input_dict['''input_ids'''].shape
									_lowerCamelCase					     = torch.tensor([*[box] * seq_length]      ).tile(lowerCamelCase__ ,				1 ,				1      )
									return input_dict






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)

