






"""simple docstring"""

import argparse

import torch

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


logging.set_verbosity_info()
lowercase__          = logging.get_logger(__name__)

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    Optional[Any]:

		"""simple docstring"""







		lowerCAmelCase_   :							str									=			WavaVecaForSequenceClassification.from_pretrained(__UpperCamelCase      ,       config=__UpperCamelCase       )
		lowerCAmelCase_   :							str									=			downstream_dict["projector.weight"]
		lowerCAmelCase_   :							Optional[int]									=			downstream_dict["projector.bias"]
		lowerCAmelCase_   :							int									=			downstream_dict["model.post_net.linear.weight"]
		lowerCAmelCase_   :							Optional[Any]									=			downstream_dict["model.post_net.linear.bias"]
		return model

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    List[Any]:

		"""simple docstring"""







		lowerCAmelCase_   :							Optional[Any]									=			WavaVecaForAudioFrameClassification.from_pretrained(__UpperCamelCase      ,       config=__UpperCamelCase       )
		lowerCAmelCase_   :							str									=			downstream_dict["model.linear.weight"]
		lowerCAmelCase_   :							List[Any]									=			downstream_dict["model.linear.bias"]
		return model

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    str:

		"""simple docstring"""







		lowerCAmelCase_   :							int									=			WavaVecaForXVector.from_pretrained(__UpperCamelCase      ,       config=__UpperCamelCase       )
		lowerCAmelCase_   :							Any									=			downstream_dict["connector.weight"]
		lowerCAmelCase_   :							Union[str, Any]									=			downstream_dict["connector.bias"]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
				lowerCAmelCase_   :							Union[str, Any]									=			downstream_dict[
				    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
				]
				lowerCAmelCase_   :							Union[str, Any]									=			downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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

@torch.no_grad()
def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    List[str]:

		"""simple docstring"""







		lowerCAmelCase_   :							int									=			torch.load(__UpperCamelCase      ,       map_location="cpu"       )

		lowerCAmelCase_   :							Tuple									=			checkpoint["Downstream"]

		lowerCAmelCase_   :							Any									=			WavaVecaConfig.from_pretrained(__UpperCamelCase       )
		lowerCAmelCase_   :							Optional[Any]									=			WavaVecaFeatureExtractor.from_pretrained(
		    __UpperCamelCase      ,       return_attention_mask=__UpperCamelCase      ,       do_normalize=__UpperCamelCase       )

		lowerCAmelCase_   :							Optional[int]									=			hf_config.architectures[0]
		if arch.endswith("ForSequenceClassification"       ):
				lowerCAmelCase_   :							Any									=			convert_classification(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )
		elif arch.endswith("ForAudioFrameClassification"       ):
				lowerCAmelCase_   :							Any									=			convert_diarization(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )
		elif arch.endswith("ForXVector"       ):
				lowerCAmelCase_   :							Dict									=			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_   :							int									=			checkpoint["Featurizer"]["weights"]

		hf_feature_extractor.save_pretrained(__UpperCamelCase       )
		hf_model.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
					lowercase__          = 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.""")
					lowercase__          = parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








"""simple docstring"""

import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    DebertaForMaskedLM,
					    DebertaForQuestionAnswering,
					    DebertaForSequenceClassification,
					    DebertaForTokenClassification,
					    DebertaModel,
					)
					from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		def __init__(   self							:	Optional[Any]						,			a_							:	int						,			a_							:	Optional[int]=13						,			a_							:	Optional[Any]=7						,			a_							:	Tuple=True						,			a_							:	Optional[int]=True						,			a_							:	List[str]=True						,			a_							:	Union[str, Any]=True						,			a_							:	List[Any]=99						,			a_							:	List[Any]=32						,			a_							:	Dict=5						,			a_							:	Tuple=4						,			a_							:	Any=37						,			a_							:	int="gelu"						,			a_							:	Any=0.1						,			a_							:	Union[str, Any]=0.1						,			a_							:	Dict=5_12						,			a_							:	Union[str, Any]=16						,			a_							:	Optional[int]=2						,			a_							:	Dict=0.02						,			a_							:	List[str]=False						,			a_							:	str=True						,			a_							:	Any="None"						,			a_							:	Dict=3						,			a_							:	List[str]=4						,			a_							:	Optional[Any]=None						,			):
				lowerCAmelCase_   :							str									=			parent
				lowerCAmelCase_   :							Optional[Any]									=			batch_size
				lowerCAmelCase_   :							Any									=			seq_length
				lowerCAmelCase_   :							int									=			is_training
				lowerCAmelCase_   :							List[Any]									=			use_input_mask
				lowerCAmelCase_   :							str									=			use_token_type_ids
				lowerCAmelCase_   :							Dict									=			use_labels
				lowerCAmelCase_   :							Optional[Any]									=			vocab_size
				lowerCAmelCase_   :							List[str]									=			hidden_size
				lowerCAmelCase_   :							Optional[Any]									=			num_hidden_layers
				lowerCAmelCase_   :							Optional[int]									=			num_attention_heads
				lowerCAmelCase_   :							Optional[Any]									=			intermediate_size
				lowerCAmelCase_   :							List[Any]									=			hidden_act
				lowerCAmelCase_   :							List[str]									=			hidden_dropout_prob
				lowerCAmelCase_   :							Tuple									=			attention_probs_dropout_prob
				lowerCAmelCase_   :							int									=			max_position_embeddings
				lowerCAmelCase_   :							Any									=			type_vocab_size
				lowerCAmelCase_   :							Dict									=			type_sequence_label_size
				lowerCAmelCase_   :							Optional[int]									=			initializer_range
				lowerCAmelCase_   :							Optional[Any]									=			num_labels
				lowerCAmelCase_   :							List[Any]									=			num_choices
				lowerCAmelCase_   :							Optional[Any]									=			relative_attention
				lowerCAmelCase_   :							Optional[int]									=			position_biased_input
				lowerCAmelCase_   :							Union[str, Any]									=			pos_att_type
				lowerCAmelCase_   :							Tuple									=			scope





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							List[Any]									=			ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size	)

				lowerCAmelCase_   :							str									=			None
				if self.use_input_mask:
						lowerCAmelCase_   :							Union[str, Any]									=			ids_tensor([self.batch_size, self.seq_length]						,			vocab_size=2	)

				lowerCAmelCase_   :							int									=			None
				if self.use_token_type_ids:
						lowerCAmelCase_   :							Union[str, Any]									=			ids_tensor([self.batch_size, self.seq_length]						,			self.type_vocab_size	)

				lowerCAmelCase_   :							Optional[int]									=			None
				lowerCAmelCase_   :							Optional[int]									=			None
				lowerCAmelCase_   :							Dict									=			None
				if self.use_labels:
						lowerCAmelCase_   :							Optional[Any]									=			ids_tensor([self.batch_size]						,			self.type_sequence_label_size	)
						lowerCAmelCase_   :							Dict									=			ids_tensor([self.batch_size, self.seq_length]						,			self.num_labels	)
						lowerCAmelCase_   :							Union[str, Any]									=			ids_tensor([self.batch_size]						,			self.num_choices	)

				lowerCAmelCase_   :							List[str]									=			self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





		def    lowerCamelCase       (   self							:	int	):
				return DebertaConfig(
				    vocab_size=self.vocab_size						,			hidden_size=self.hidden_size						,			num_hidden_layers=self.num_hidden_layers						,			num_attention_heads=self.num_attention_heads						,			intermediate_size=self.intermediate_size						,			hidden_act=self.hidden_act						,			hidden_dropout_prob=self.hidden_dropout_prob						,			attention_probs_dropout_prob=self.attention_probs_dropout_prob						,			max_position_embeddings=self.max_position_embeddings						,			type_vocab_size=self.type_vocab_size						,			initializer_range=self.initializer_range						,			relative_attention=self.relative_attention						,			position_biased_input=self.position_biased_input						,			pos_att_type=self.pos_att_type						,			)





		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							Union[str, Any]									=			self.get_config()
				lowerCAmelCase_   :							Tuple									=			3_00
				return config





		def    lowerCamelCase       (   self							:	List[Any]						,			a_							:	Optional[Any]	):
				self.parent.assertListEqual(list(result.loss.size()	)						,			[]	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	List[str]						,			a_							:	Union[str, Any]						,			a_							:	Dict						,			a_							:	str						,			a_							:	int						,			a_							:	Any						,			a_							:	Tuple	):
				lowerCAmelCase_   :							Union[str, Any]									=			DebertaModel(config=a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							str									=			model(a_						,			attention_mask=a_						,			token_type_ids=a_	)[0]
				lowerCAmelCase_   :							List[str]									=			model(a_						,			token_type_ids=a_	)[0]
				lowerCAmelCase_   :							Optional[Any]									=			model(a_	)[0]

				self.parent.assertListEqual(list(sequence_output.size()	)						,			[self.batch_size, self.seq_length, self.hidden_size]	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	Optional[int]						,			a_							:	int						,			a_							:	List[str]						,			a_							:	int						,			a_							:	Tuple						,			a_							:	int						,			a_							:	List[Any]	):
				lowerCAmelCase_   :							List[Any]									=			DebertaForMaskedLM(config=a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							Dict									=			model(a_						,			attention_mask=a_						,			token_type_ids=a_						,			labels=a_	)

				self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)	)





		def    lowerCamelCase       (   self							:	str						,			a_							:	List[str]						,			a_							:	Tuple						,			a_							:	Any						,			a_							:	str						,			a_							:	List[Any]						,			a_							:	Tuple						,			a_							:	Union[str, Any]	):
				lowerCAmelCase_   :							str									=			self.num_labels
				lowerCAmelCase_   :							List[str]									=			DebertaForSequenceClassification(a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							str									=			model(a_						,			attention_mask=a_						,			token_type_ids=a_						,			labels=a_	)
				self.parent.assertListEqual(list(result.logits.size()	)						,			[self.batch_size, self.num_labels]	)
				self.check_loss_output(a_	)





		def    lowerCamelCase       (   self							:	Any						,			a_							:	Union[str, Any]						,			a_							:	Any						,			a_							:	str						,			a_							:	int						,			a_							:	Dict						,			a_							:	int						,			a_							:	Tuple	):
				lowerCAmelCase_   :							int									=			self.num_labels
				lowerCAmelCase_   :							Any									=			DebertaForTokenClassification(config=a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							int									=			model(a_						,			attention_mask=a_						,			token_type_ids=a_						,			labels=a_	)
				self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.num_labels)	)





		def    lowerCamelCase       (   self							:	Dict						,			a_							:	Dict						,			a_							:	Optional[int]						,			a_							:	Union[str, Any]						,			a_							:	Any						,			a_							:	List[Any]						,			a_							:	int						,			a_							:	str	):
				lowerCAmelCase_   :							Optional[int]									=			DebertaForQuestionAnswering(config=a_	)
				model.to(a_	)
				model.eval()
				lowerCAmelCase_   :							Tuple									=			model(
				    a_						,			attention_mask=a_						,			token_type_ids=a_						,			start_positions=a_						,			end_positions=a_						,			)
				self.parent.assertEqual(result.start_logits.shape						,			(self.batch_size, self.seq_length)	)
				self.parent.assertEqual(result.end_logits.shape						,			(self.batch_size, self.seq_length)	)





		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							Tuple									=			self.prepare_config_and_inputs()
				(
				    (
				    lowerCAmelCase_
				)				,	(
				    lowerCAmelCase_
				)				,	(
				    lowerCAmelCase_
				)				,	(
				    lowerCAmelCase_
				)				,	(
				    lowerCAmelCase_
				)				,	(
				    lowerCAmelCase_
				)				,	(
				    lowerCAmelCase_
				)				,	
				)   :							List[Any]									=			config_and_inputs
				lowerCAmelCase_   :							str									=			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict







@require_torch
class 		__lowerCamelCase				( A__ ,							A__ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				int            =							(
		    (
		        DebertaModel,
		        DebertaForMaskedLM,
		        DebertaForSequenceClassification,
		        DebertaForTokenClassification,
		        DebertaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		a_	:				Dict            =							(
		    {
		        """feature-extraction""": DebertaModel,
		        """fill-mask""": DebertaForMaskedLM,
		        """question-answering""": DebertaForQuestionAnswering,
		        """text-classification""": DebertaForSequenceClassification,
		        """token-classification""": DebertaForTokenClassification,
		        """zero-shot""": DebertaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)

		a_	:				List[Any]            =							True
		a_	:				Dict            =							False
		a_	:				int            =							False
		a_	:				str            =							False
		a_	:				List[Any]            =							False





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							Union[str, Any]									=			DebertaModelTester(self	)
				lowerCAmelCase_   :							List[Any]									=			ConfigTester(self						,			config_class=a_						,			hidden_size=37	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				self.config_tester.run_common_tests()





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Tuple									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_model(*a_	)





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							List[str]									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_sequence_classification(*a_	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Optional[int]									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_masked_lm(*a_	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Dict									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_question_answering(*a_	)





		def    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							Optional[int]									=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_token_classification(*a_	)





		@slow
		def    lowerCamelCase       (   self							:	Optional[Any]	):
				for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowerCAmelCase_   :							Union[str, Any]									=			DebertaModel.from_pretrained(a_	)
						self.assertIsNotNone(a_	)







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






		@unittest.skip(reason="Model not available yet"	)
		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				pass





		@slow
		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							int									=			DebertaModel.from_pretrained("microsoft/deberta-base"	)

				lowerCAmelCase_   :							str									=			torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	)
				lowerCAmelCase_   :							Dict									=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
				with torch.no_grad():
						lowerCAmelCase_   :							Dict									=			model(a_						,			attention_mask=a_	)[0]
				# compare the actual values for a slice.
				lowerCAmelCase_   :							Optional[Any]									=			torch.tensor(
				    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]	)
				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,			a_						,			atol=1e-4	)						,			f'''{output[:, 1:4, 1:4]}'''	)
