import argparse

import torch

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


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE:    str     						=							logging.get_logger(__name__)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   WavLMForSequenceClassification.from_pretrained(lowerCamelCase_				,						config=lowerCamelCase_	)
			_snake_case														=   downstream_dict["projector.weight"]
			_snake_case														=   downstream_dict["projector.bias"]
			_snake_case														=   downstream_dict["model.post_net.linear.weight"]
			_snake_case														=   downstream_dict["model.post_net.linear.bias"]
			return model






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   WavLMForAudioFrameClassification.from_pretrained(lowerCamelCase_				,						config=lowerCamelCase_	)
			_snake_case														=   downstream_dict["model.linear.weight"]
			_snake_case														=   downstream_dict["model.linear.bias"]
			return model






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   WavLMForXVector.from_pretrained(lowerCamelCase_				,						config=lowerCamelCase_	)
			_snake_case														=   downstream_dict["connector.weight"]
			_snake_case														=   downstream_dict["connector.bias"]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
						_snake_case														=   downstream_dict[
						    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
						]
						_snake_case														=   downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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






@torch.no_grad()
def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   torch.load(lowerCamelCase_				,						map_location="cpu"	)

			_snake_case														=   checkpoint["Downstream"]

			_snake_case														=   WavLMConfig.from_pretrained(lowerCamelCase_	)
			_snake_case														=   WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_				,						return_attention_mask=lowerCamelCase_				,						do_normalize=lowerCamelCase_	)

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

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

			hf_feature_extractor.save_pretrained(lowerCamelCase_	)
			hf_model.save_pretrained(lowerCamelCase_	)


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    List[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.''')
			_SCREAMING_SNAKE_CASE:    int     						=							parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE:    Optional[int]     						=							sys.argv[1]
_SCREAMING_SNAKE_CASE:    int     						=							sys.argv[2]
_SCREAMING_SNAKE_CASE:    str     						=							int(sys.argv[3])

_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							0

_SCREAMING_SNAKE_CASE:    Any     						=							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
			for line in f_p:
						_SCREAMING_SNAKE_CASE:    Any     						=							line.rstrip()

						if not line:
									print(line)
									_SCREAMING_SNAKE_CASE:    str     						=							0
									continue

						_SCREAMING_SNAKE_CASE:    Optional[int]     						=							line.split()[0]

						_SCREAMING_SNAKE_CASE:    Tuple     						=							len(tokenizer.tokenize(token))

						# Token contains strange control characters like \x96 or \x95
						# Just filter out the complete line
						if current_subwords_len == 0:
									continue

						if (subword_len_counter + current_subwords_len) > max_len:
									print('''''')
									print(line)
									_SCREAMING_SNAKE_CASE:    Any     						=							current_subwords_len
									continue

						subword_len_counter += current_subwords_len

						print(line)

