

"""simple docstring"""






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase__					:	Optional[int]				    =	logging.get_logger(__name__)
def    UpperCamelCase       (      _lowerCAmelCase  :    int,	_lowerCAmelCase  :    Optional[Any],	_lowerCAmelCase  :    Tuple			)					->      Optional[Any]:
	_UpperCAmelCase     :				Optional[Any]          =       WavaVecaForSequenceClassification.from_pretrained(__UpperCAmelCase,	config=__UpperCAmelCase			)
	_UpperCAmelCase     :				Optional[Any]          =       downstream_dict['projector.weight']
	_UpperCAmelCase     :				Tuple          =       downstream_dict['projector.bias']
	_UpperCAmelCase     :				Optional[int]          =       downstream_dict['model.post_net.linear.weight']
	_UpperCAmelCase     :				List[Any]          =       downstream_dict['model.post_net.linear.bias']
	return model
def    UpperCamelCase       (      _lowerCAmelCase  :    List[Any],	_lowerCAmelCase  :    Union[str, Any],	_lowerCAmelCase  :    Tuple			)					->      Dict:
	_UpperCAmelCase     :				Optional[Any]          =       WavaVecaForAudioFrameClassification.from_pretrained(__UpperCAmelCase,	config=__UpperCAmelCase			)
	_UpperCAmelCase     :				Any          =       downstream_dict['model.linear.weight']
	_UpperCAmelCase     :				int          =       downstream_dict['model.linear.bias']
	return model
def    UpperCamelCase       (      _lowerCAmelCase  :    List[str],	_lowerCAmelCase  :    int,	_lowerCAmelCase  :    Any			)					->      Union[str, Any]:
	_UpperCAmelCase     :				int          =       WavaVecaForXVector.from_pretrained(__UpperCAmelCase,	config=__UpperCAmelCase			)
	_UpperCAmelCase     :				Dict          =       downstream_dict['connector.weight']
	_UpperCAmelCase     :				Tuple          =       downstream_dict['connector.bias']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
		_UpperCAmelCase     :				Union[str, Any]          =       downstream_dict[
		    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
		]
		_UpperCAmelCase     :				Optional[Any]          =       downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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

	_UpperCAmelCase     :				List[Any]          =       checkpoint['Downstream']

	_UpperCAmelCase     :				List[str]          =       WavaVecaConfig.from_pretrained(__UpperCAmelCase			)
	_UpperCAmelCase     :				Any          =       WavaVecaFeatureExtractor.from_pretrained(
	    __UpperCAmelCase,	return_attention_mask=__UpperCAmelCase,	do_normalize=__UpperCAmelCase			)

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

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

	hf_feature_extractor.save_pretrained(__UpperCAmelCase			)
	hf_model.save_pretrained(__UpperCAmelCase			)


if __name__ == "__main__":
						lowerCamelCase__					:	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.''')
						lowerCamelCase__					:	Union[str, Any]				    =	parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










'''simple docstring'''

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				FunnelTokenizer
							UpperCAmelCase			:		Tuple        =				FunnelTokenizerFast
							UpperCAmelCase			:		List[str]        =				True
							UpperCAmelCase			:		Tuple        =				True


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										super().setUp()

										snake_case:    Optional[Any]      							=[
										    '<unk>',
										    '<cls>',
										    '<sep>',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										snake_case:    Any      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)


							def    UpperCamelCase     (				self   :						Tuple      ,						**a_   :						str			)			->					Any:
										return FunnelTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						**a_   :						int			)			->					str:
										return FunnelTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]			)			->					Union[str, Any]:
										snake_case:    List[str]      							='UNwant\u00E9d,running'
										snake_case:    Dict      							='unwanted, running'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[Any]:
										snake_case:    Union[str, Any]      							=self.tokenizer_class(self.vocab_file			)

										snake_case:    Dict      							=tokenizer.tokenize('UNwant\u00E9d,running'			)
										self.assertListEqual(a_      ,						['un', '##want', '##ed', ',', 'runn', '##ing']			)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						[7, 4, 5, 1_0, 8, 9]			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    Optional[Any]      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													snake_case:    str      							=tokenizer('UNwant\u00E9d,running'			)
													snake_case:    Union[str, Any]      							=len(inputs['input_ids']			) - 1
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len			)

													snake_case:    List[str]      							=tokenizer('UNwant\u00E9d,running'      ,						'UNwant\u00E9d,running'			)
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len + [1] * sentence_len			)




