


'''simple docstring'''






import argparse

import torch

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


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





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Any ):







		'''simple docstring'''
		snake_case_		:							Dict			       =						UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_				,      config=lowerCamelCase_ )
		snake_case_		:							str			       =						downstream_dict["""projector.weight"""]
		snake_case_		:							List[Any]			       =						downstream_dict["""projector.bias"""]
		snake_case_		:							str			       =						downstream_dict["""model.post_net.linear.weight"""]
		snake_case_		:							int			       =						downstream_dict["""model.post_net.linear.bias"""]
		return model





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Optional[Any] ):







		'''simple docstring'''
		snake_case_		:							Any			       =						UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_				,      config=lowerCamelCase_ )
		snake_case_		:							Dict			       =						downstream_dict["""model.linear.weight"""]
		snake_case_		:							int			       =						downstream_dict["""model.linear.bias"""]
		return model





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Any ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						UniSpeechSatForXVector.from_pretrained(lowerCamelCase_				,      config=lowerCamelCase_ )
		snake_case_		:							Any			       =						downstream_dict["""connector.weight"""]
		snake_case_		:							Union[str, Any]			       =						downstream_dict["""connector.bias"""]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
				snake_case_		:							int			       =						downstream_dict[
				    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
				]
				snake_case_		:							Optional[Any]			       =						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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





@torch.no_grad()
def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		snake_case_		:							Any			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )

		snake_case_		:							str			       =						checkpoint["""Downstream"""]

		snake_case_		:							Dict			       =						UniSpeechSatConfig.from_pretrained(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						WavaVecaFeatureExtractor.from_pretrained(
		    lowerCamelCase_				,      return_attention_mask=lowerCamelCase_				,      do_normalize=lowerCamelCase_ )

		snake_case_		:							str			       =						hf_config.architectures[0]
		if arch.endswith("""ForSequenceClassification""" ):
				snake_case_		:							str			       =						convert_classification(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )
		elif arch.endswith("""ForAudioFrameClassification""" ):
				snake_case_		:							List[str]			       =						convert_diarization(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ )
		elif arch.endswith("""ForXVector""" ):
				snake_case_		:							List[Any]			       =						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_		:							Union[str, Any]			       =						checkpoint["""Featurizer"""]["""weights"""]

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


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



'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch
@require_torch
@require_optimum
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoTokenizer.from_pretrained(_UpperCamelCase			)
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[str]			       =						tokenizer("""This is me"""  ,return_tensors="""pt"""			)

									snake_case_		:							List[str]			       =						model.to_bettertransformer()

									self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									snake_case_		:							List[str]			       =						model.generate(**_UpperCamelCase			)

									snake_case_		:							Dict			       =						model.reverse_bettertransformer()

									self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

											self.assertFalse(
											    any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

											snake_case_		:							Optional[Any]			       =						model_reloaded.generate(**_UpperCamelCase			)
											self.assertTrue(torch.allclose(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						"""hf-internal-testing/tiny-random-t5"""
									snake_case_		:							str			       =						AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						model.to_bettertransformer()

									with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(_UpperCamelCase			):
													model.save_pretrained(_UpperCamelCase			)

											snake_case_		:							Any			       =						model.reverse_bettertransformer()
											model.save_pretrained(_UpperCamelCase			)