'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   str					     =					UniSpeechSatForSequenceClassification.from_pretrained(__snake_case		,		config=__snake_case		)
	__magic_name__	:   Optional[Any]					     =					downstream_dict["projector.weight"]
	__magic_name__	:   List[str]					     =					downstream_dict["projector.bias"]
	__magic_name__	:   Dict					     =					downstream_dict["model.post_net.linear.weight"]
	__magic_name__	:   int					     =					downstream_dict["model.post_net.linear.bias"]
	return model





def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					UniSpeechSatForAudioFrameClassification.from_pretrained(__snake_case		,		config=__snake_case		)
	__magic_name__	:   Tuple					     =					downstream_dict["model.linear.weight"]
	__magic_name__	:   List[str]					     =					downstream_dict["model.linear.bias"]
	return model





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   Optional[Any]					     =					UniSpeechSatForXVector.from_pretrained(__snake_case		,		config=__snake_case		)
	__magic_name__	:   Tuple					     =					downstream_dict["connector.weight"]
	__magic_name__	:   Union[str, Any]					     =					downstream_dict["connector.bias"]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
		__magic_name__	:   str					     =					downstream_dict[
		    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		__magic_name__	:   Union[str, Any]					     =					downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

	__magic_name__	:   List[Any]					     =					downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
	__magic_name__	:   Tuple					     =					downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
	__magic_name__	:   Optional[int]					     =					downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
	__magic_name__	:   Any					     =					downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
	__magic_name__	:   Any					     =					downstream_dict["objective.W"]
	return model





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Any		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					torch.load(__snake_case		,		map_location='cpu'		)

	__magic_name__	:   str					     =					checkpoint["Downstream"]

	__magic_name__	:   Tuple					     =					UniSpeechSatConfig.from_pretrained(__snake_case		)
	__magic_name__	:   List[str]					     =					WavaVecaFeatureExtractor.from_pretrained(
	    __snake_case		,		return_attention_mask=__snake_case		,		do_normalize=__snake_case		)

	__magic_name__	:   Union[str, Any]					     =					hf_config.architectures[0]
	if arch.endswith('ForSequenceClassification'		):
		__magic_name__	:   Optional[int]					     =					convert_classification(__snake_case		,		__snake_case		,		__snake_case		)
	elif arch.endswith('ForAudioFrameClassification'		):
		__magic_name__	:   Optional[Any]					     =					convert_diarization(__snake_case		,		__snake_case		,		__snake_case		)
	elif arch.endswith('ForXVector'		):
		__magic_name__	:   Any					     =					convert_xvector(__snake_case		,		__snake_case		,		__snake_case		)
	else:
		raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'		)

	if hf_config.use_weighted_layer_sum:
		__magic_name__	:   Tuple					     =					checkpoint["Featurizer"]["weights"]

	hf_feature_extractor.save_pretrained(__snake_case		)
	hf_model.save_pretrained(__snake_case		)


if __name__ == "__main__":
							lowerCAmelCase				:Optional[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				:Optional[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 LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Optional[Any]				   =    LxmertTokenizer
		A_     :      List[Any]				   =    LxmertTokenizerFast
		A_     :      int				   =    True
		A_     :      Any				   =    True







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			super().setUp()

			__magic_name__	:   str					     =					[
			    '[UNK]',
			    '[CLS]',
			    '[SEP]',
			    'want',
			    '##want',
			    '##ed',
			    'wa',
			    'un',
			    'runn',
			    '##ing',
			    ',',
			    'low',
			    'lowest',
			]
			__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						Any  ,					_A		:						str      )  ->			List[Any]:
			__magic_name__	:   Dict					     =					'UNwant\u00E9d,running'
			__magic_name__	:   Dict					     =					'unwanted, running'
			return input_text, output_text







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			__magic_name__	:   Optional[Any]					     =					self.tokenizer_class(self.vocab_file      )

			__magic_name__	:   List[str]					     =					tokenizer.tokenize('UNwant\u00E9d,running'      )
			self.assertListEqual(_A  ,					['un', '##want', '##ed', ',', 'runn', '##ing']      )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )  ,					[7, 4, 5, 10, 8, 9]      )







		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			if not self.test_rust_tokenizer:
				return

			__magic_name__	:   Any					     =					self.get_tokenizer()
			__magic_name__	:   Optional[Any]					     =					self.get_rust_tokenizer()

			__magic_name__	:   Union[str, Any]					     =					'I was born in 92000, and this is falsé.'

			__magic_name__	:   List[Any]					     =					tokenizer.tokenize(_A      )
			__magic_name__	:   Dict					     =					rust_tokenizer.tokenize(_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   int					     =					tokenizer.encode(_A  ,					add_special_tokens=_A      )
			__magic_name__	:   Union[str, Any]					     =					rust_tokenizer.encode(_A  ,					add_special_tokens=_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   List[Any]					     =					self.get_rust_tokenizer()
			__magic_name__	:   str					     =					tokenizer.encode(_A      )
			__magic_name__	:   Optional[int]					     =					rust_tokenizer.encode(_A      )
			self.assertListEqual(_A  ,					_A      )