





import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase__    :      List[Any]       					=logging.get_logger(__name__)





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Dict:
				lowerCamelCase									=WavLMForSequenceClassification.from_pretrained(_UpperCAmelCase      ,   config=_UpperCAmelCase					)
				lowerCamelCase									=downstream_dict["""projector.weight"""]
				lowerCamelCase									=downstream_dict["""projector.bias"""]
				lowerCamelCase									=downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase									=downstream_dict["""model.post_net.linear.bias"""]
				return model





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	int:
				lowerCamelCase									=WavLMForAudioFrameClassification.from_pretrained(_UpperCAmelCase      ,   config=_UpperCAmelCase					)
				lowerCamelCase									=downstream_dict["""model.linear.weight"""]
				lowerCamelCase									=downstream_dict["""model.linear.bias"""]
				return model





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Optional[Any]:
				lowerCamelCase									=WavLMForXVector.from_pretrained(_UpperCAmelCase      ,   config=_UpperCAmelCase					)
				lowerCamelCase									=downstream_dict["""connector.weight"""]
				lowerCamelCase									=downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
								lowerCamelCase									=downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase									=downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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





@torch.no_grad()
def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Union[str, Any]:
				lowerCamelCase									=torch.load(_UpperCAmelCase      ,   map_location="""cpu"""					)

				lowerCamelCase									=checkpoint["""Downstream"""]

				lowerCamelCase									=WavLMConfig.from_pretrained(_UpperCAmelCase					)
				lowerCamelCase									=WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase      ,   return_attention_mask=_UpperCAmelCase      ,   do_normalize=_UpperCAmelCase					)

				lowerCamelCase									=hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""					):
								lowerCamelCase									=convert_classification(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
				elif arch.endswith("""ForAudioFrameClassification"""					):
								lowerCamelCase									=convert_diarization(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
				elif arch.endswith("""ForXVector"""					):
								lowerCamelCase									=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									=checkpoint["""Featurizer"""]["""weights"""]

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


if __name__ == "__main__":
							UpperCAmelCase__    :      Union[str, 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.''')
							UpperCAmelCase__    :      str       					=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_				)





