
import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__        =						logging.get_logger(__name__)
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Dict:
			lowerCAmelCase__ : int   =						WavLMForSequenceClassification.from_pretrained(_UpperCAmelCase			,				config=_UpperCAmelCase	)
			lowerCAmelCase__ : Dict   =						downstream_dict['projector.weight']
			lowerCAmelCase__ : List[Any]   =						downstream_dict['projector.bias']
			lowerCAmelCase__ : Any   =						downstream_dict['model.post_net.linear.weight']
			lowerCAmelCase__ : int   =						downstream_dict['model.post_net.linear.bias']
			return model
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						int:
			lowerCAmelCase__ : List[Any]   =						WavLMForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,				config=_UpperCAmelCase	)
			lowerCAmelCase__ : Optional[int]   =						downstream_dict['model.linear.weight']
			lowerCAmelCase__ : Any   =						downstream_dict['model.linear.bias']
			return model
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Optional[Any]:
			lowerCAmelCase__ : Union[str, Any]   =						WavLMForXVector.from_pretrained(_UpperCAmelCase			,				config=_UpperCAmelCase	)
			lowerCAmelCase__ : List[Any]   =						downstream_dict['connector.weight']
			lowerCAmelCase__ : Tuple   =						downstream_dict['connector.bias']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
						lowerCAmelCase__ : Tuple   =						downstream_dict[
						    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
						]
						lowerCAmelCase__ : Optional[int]   =						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

			lowerCAmelCase__ : Optional[int]   =						downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
			lowerCAmelCase__ : Union[str, Any]   =						downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
			lowerCAmelCase__ : Dict   =						downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
			lowerCAmelCase__ : str   =						downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
			lowerCAmelCase__ : Any   =						downstream_dict['objective.W']
			return model
@torch.no_grad()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Union[str, Any]:
			lowerCAmelCase__ : Union[str, Any]   =						torch.load(_UpperCAmelCase			,				map_location='cpu'	)

			lowerCAmelCase__ : str   =						checkpoint['Downstream']

			lowerCAmelCase__ : Dict   =						WavLMConfig.from_pretrained(_UpperCAmelCase	)
			lowerCAmelCase__ : Optional[int]   =						WavaVecaFeatureExtractor.from_pretrained(
			    _UpperCAmelCase			,				return_attention_mask=_UpperCAmelCase			,				do_normalize=_UpperCAmelCase	)

			lowerCAmelCase__ : List[Any]   =						hf_config.architectures[0]
			if arch.endswith('ForSequenceClassification'	):
						lowerCAmelCase__ : Dict   =						convert_classification(_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase	)
			elif arch.endswith('ForAudioFrameClassification'	):
						lowerCAmelCase__ : Optional[Any]   =						convert_diarization(_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase	)
			elif arch.endswith('ForXVector'	):
						lowerCAmelCase__ : Optional[int]   =						convert_xvector(_UpperCAmelCase			,				_UpperCAmelCase			,				_UpperCAmelCase	)
			else:
						raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''	)

			if hf_config.use_weighted_layer_sum:
						lowerCAmelCase__ : Optional[Any]   =						checkpoint['Featurizer']['weights']

			hf_feature_extractor.save_pretrained(_UpperCAmelCase	)
			hf_model.save_pretrained(_UpperCAmelCase	)


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






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

UpperCAmelCase__    :      Optional[Any]       					={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[self.cls_token_id]
								if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





