"""simple docstring"""






import argparse

import torch

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


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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Optional[int]:
				SCREAMING_SNAKE_CASE__		  =				UniSpeechSatForSequenceClassification.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["projector.weight"]
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["projector.bias"]
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["model.post_net.linear.weight"]
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["model.post_net.linear.bias"]
				return model




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				UniSpeechSatForAudioFrameClassification.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["model.linear.weight"]
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["model.linear.bias"]
				return model




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   str:
				SCREAMING_SNAKE_CASE__		  =				UniSpeechSatForXVector.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["connector.weight"]
				SCREAMING_SNAKE_CASE__		  =				downstream_dict["connector.bias"]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
								SCREAMING_SNAKE_CASE__		  =				downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								SCREAMING_SNAKE_CASE__		  =				downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase  ,					map_location="cpu"       )

				SCREAMING_SNAKE_CASE__		  =				checkpoint["Downstream"]

				SCREAMING_SNAKE_CASE__		  =				UniSpeechSatConfig.from_pretrained(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				WavaVecaFeatureExtractor.from_pretrained(
				    __UpperCAmelCase  ,					return_attention_mask=__UpperCAmelCase  ,					do_normalize=__UpperCAmelCase       )

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

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

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


if __name__ == "__main__":
						_A   					=    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   					=    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 AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    AlbertForMaskedLM,
						    AlbertForMultipleChoice,
						    AlbertForPreTraining,
						    AlbertForQuestionAnswering,
						    AlbertForSequenceClassification,
						    AlbertForTokenClassification,
						    AlbertModel,
						)
						from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				str   , _snake_case			:				List[str]   , _snake_case			:				List[Any]=13   , _snake_case			:				List[str]=7   , _snake_case			:				Dict=True   , _snake_case			:				str=True   , _snake_case			:				Optional[Any]=True   , _snake_case			:				Tuple=True   , _snake_case			:				List[Any]=99   , _snake_case			:				Dict=16   , _snake_case			:				Tuple=36   , _snake_case			:				Optional[int]=6   , _snake_case			:				Optional[int]=6   , _snake_case			:				Tuple=6   , _snake_case			:				Optional[int]=37   , _snake_case			:				Dict="gelu"   , _snake_case			:				str=0.1   , _snake_case			:				Tuple=0.1   , _snake_case			:				List[str]=512   , _snake_case			:				Any=16   , _snake_case			:				Optional[int]=2   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Union[str, Any]=3   , _snake_case			:				int=4   , _snake_case			:				Optional[int]=None   , )							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_input_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				embedding_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_hidden_groups
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				num_choices
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_input_mask:
													SCREAMING_SNAKE_CASE__		  =				random_attention_mask([self.batch_size, self.seq_length]		)

									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.type_vocab_size		)

									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_labels:
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.type_sequence_label_size		)
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.num_labels		)
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.num_choices		)

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

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



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									return AlbertConfig(
									    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   , num_hidden_groups=self.num_hidden_groups   , )



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Union[str, Any]   , _snake_case			:				str   , _snake_case			:				int		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)		)
									self.parent.assertEqual(result.pooler_output.shape   , (self.batch_size, self.hidden_size)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				str		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForPreTraining(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , sentence_order_label=_snake_case   , )
									self.parent.assertEqual(result.prediction_logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)
									self.parent.assertEqual(result.sop_logits.shape   , (self.batch_size, config.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Any   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				Tuple   , _snake_case			:				List[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AlbertForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[Any]   , _snake_case			:				Tuple		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForQuestionAnswering(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , start_positions=_snake_case   , end_positions=_snake_case   , )
									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			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				str   , _snake_case			:				Optional[int]   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[Any]   , _snake_case			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForSequenceClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				Any   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[str]   , _snake_case			:				int		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.num_choices
									SCREAMING_SNAKE_CASE__		  =				AlbertForMultipleChoice(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				input_ids.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				token_type_ids.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				input_mask.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , )
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       
									)		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		(
					    (
					        AlbertModel,
					        AlbertForPreTraining,
					        AlbertForMaskedLM,
					        AlbertForMultipleChoice,
					        AlbertForSequenceClassification,
					        AlbertForTokenClassification,
					        AlbertForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					a   	=		(
					    {
					        "feature-extraction": AlbertModel,
					        "fill-mask": AlbertForMaskedLM,
					        "question-answering": AlbertForQuestionAnswering,
					        "text-classification": AlbertForSequenceClassification,
					        "token-classification": AlbertForTokenClassification,
					        "zero-shot": AlbertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				str=False		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				super()._prepare_for_class(_snake_case   , _snake_case   , return_labels=_snake_case		)

									if return_labels:
													if model_class in get_values(_snake_case		):
																	SCREAMING_SNAKE_CASE__		  =				torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)   , dtype=torch.long   , device=_snake_case		)
																	SCREAMING_SNAKE_CASE__		  =				torch.zeros(
																	    self.model_tester.batch_size   , dtype=torch.long   , device=_snake_case		)
									return inputs_dict



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE__		  =				type
													self.model_tester.create_and_check_model(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       str:
									for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained("albert-base-v2"		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 11, 768)		)
									self.assertEqual(output.shape   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(
									    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

									self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   , _snake_case   , atol=1e-4		)		)




