


from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TFMobileBertForMaskedLM,
						    TFMobileBertForMultipleChoice,
						    TFMobileBertForNextSentencePrediction,
						    TFMobileBertForPreTraining,
						    TFMobileBertForQuestionAnswering,
						    TFMobileBertForSequenceClassification,
						    TFMobileBertForTokenClassification,
						    TFMobileBertModel,
						)







@require_tf
class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Tuple   				=      (
					    (
					        TFMobileBertModel,
					        TFMobileBertForMaskedLM,
					        TFMobileBertForNextSentencePrediction,
					        TFMobileBertForPreTraining,
					        TFMobileBertForQuestionAnswering,
					        TFMobileBertForSequenceClassification,
					        TFMobileBertForTokenClassification,
					        TFMobileBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCAmelCase__ :   List[str]   				=      (
					    {
					        '''feature-extraction''': TFMobileBertModel,
					        '''fill-mask''': TFMobileBertForMaskedLM,
					        '''question-answering''': TFMobileBertForQuestionAnswering,
					        '''text-classification''': TFMobileBertForSequenceClassification,
					        '''token-classification''': TFMobileBertForTokenClassification,
					        '''zero-shot''': TFMobileBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCAmelCase__ :   List[Any]   				=      False
					UpperCAmelCase__ :   List[Any]   				=      False
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Union[str, Any]      ,__snake_case       :int      ,__snake_case       :List[str]=False				) ->   int:
												a__					=					super()._prepare_for_class(__snake_case      ,__snake_case      ,return_labels=__snake_case				)

												if return_labels:
																			if model_class in get_values(__snake_case				):
																										a__					=					tf.zeros(self.model_tester.batch_size      ,dtype=tf.intaa				)

												return inputs_dict







					class 	snake_case_       (lowerCamelCase_				):
										def __init__(				self       :Any      ,__snake_case       :Optional[Any]      ,__snake_case       :Union[str, Any]=13      ,__snake_case       :Optional[Any]=7      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Optional[Any]=True      ,__snake_case       :str=True      ,__snake_case       :int=True      ,__snake_case       :Optional[Any]=99      ,__snake_case       :Any=32      ,__snake_case       :List[Any]=32      ,__snake_case       :Union[str, Any]=2      ,__snake_case       :Optional[Any]=4      ,__snake_case       :Tuple=37      ,__snake_case       :Union[str, Any]="gelu"      ,__snake_case       :Any=0.1      ,__snake_case       :str=0.1      ,__snake_case       :List[Any]=5_12      ,__snake_case       :List[Any]=16      ,__snake_case       :str=2      ,__snake_case       :int=0.02      ,__snake_case       :List[Any]=3      ,__snake_case       :int=4      ,__snake_case       :str=None      ,) ->   Optional[int]:
																	a__					=					parent
																	a__					=					batch_size
																	a__					=					seq_length
																	a__					=					is_training
																	a__					=					use_input_mask
																	a__					=					use_token_type_ids
																	a__					=					use_labels
																	a__					=					vocab_size
																	a__					=					hidden_size
																	a__					=					num_hidden_layers
																	a__					=					num_attention_heads
																	a__					=					intermediate_size
																	a__					=					hidden_act
																	a__					=					hidden_dropout_prob
																	a__					=					attention_probs_dropout_prob
																	a__					=					max_position_embeddings
																	a__					=					type_vocab_size
																	a__					=					type_sequence_label_size
																	a__					=					initializer_range
																	a__					=					num_labels
																	a__					=					num_choices
																	a__					=					scope
																	a__					=					embedding_size
										def 			lowerCamelCase__(				self       :List[str]				) ->   Dict:
																	a__					=					ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size				)

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

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

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

																	a__					=					MobileBertConfig(
																	    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      ,embedding_size=self.embedding_size      ,)

																	return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
										def 			lowerCamelCase__(				self       :str      ,__snake_case       :Tuple      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[str]      ,__snake_case       :Any      ,__snake_case       :Dict      ,__snake_case       :Optional[int]      ,__snake_case       :Optional[Any]				) ->   List[str]:
																	a__					=					TFMobileBertModel(config=__snake_case				)
																	a__					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	a__					=					model(__snake_case				)

																	a__					=					[input_ids, input_mask]
																	a__					=					model(__snake_case				)

																	a__					=					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       :int      ,__snake_case       :Dict      ,__snake_case       :Tuple      ,__snake_case       :List[Any]      ,__snake_case       :Union[str, Any]      ,__snake_case       :Tuple      ,__snake_case       :List[Any]      ,__snake_case       :Optional[Any]				) ->   Dict:
																	a__					=					TFMobileBertForMaskedLM(config=__snake_case				)
																	a__					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	a__					=					model(__snake_case				)
																	self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)				)
										def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :Optional[Any]      ,__snake_case       :Any      ,__snake_case       :List[Any]      ,__snake_case       :str      ,__snake_case       :Tuple      ,__snake_case       :List[Any]      ,__snake_case       :Union[str, Any]				) ->   Any:
																	a__					=					TFMobileBertForNextSentencePrediction(config=__snake_case				)
																	a__					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	a__					=					model(__snake_case				)
																	self.parent.assertEqual(result.logits.shape      ,(self.batch_size, 2)				)
										def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :Any      ,__snake_case       :Optional[int]      ,__snake_case       :Union[str, Any]      ,__snake_case       :str      ,__snake_case       :Union[str, Any]      ,__snake_case       :int      ,__snake_case       :Optional[Any]				) ->   Optional[int]:
																	a__					=					TFMobileBertForPreTraining(config=__snake_case				)
																	a__					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	a__					=					model(__snake_case				)
																	self.parent.assertEqual(
																	    result.prediction_logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)				)
																	self.parent.assertEqual(result.seq_relationship_logits.shape      ,(self.batch_size, 2)				)
										def 			lowerCamelCase__(				self       :Dict      ,__snake_case       :Tuple      ,__snake_case       :Tuple      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[str]      ,__snake_case       :List[str]      ,__snake_case       :Dict      ,__snake_case       :int				) ->   Any:
																	a__					=					self.num_labels
																	a__					=					TFMobileBertForSequenceClassification(config=__snake_case				)
																	a__					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	a__					=					model(__snake_case				)
																	self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)				)
										def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :List[str]      ,__snake_case       :Optional[Any]      ,__snake_case       :Union[str, Any]      ,__snake_case       :Tuple      ,__snake_case       :int      ,__snake_case       :Any      ,__snake_case       :Any				) ->   List[Any]:
																	a__					=					self.num_choices
																	a__					=					TFMobileBertForMultipleChoice(config=__snake_case				)
																	a__					=					tf.tile(tf.expand_dims(__snake_case      ,1				)      ,(1, self.num_choices, 1)				)
																	a__					=					tf.tile(tf.expand_dims(__snake_case      ,1				)      ,(1, self.num_choices, 1)				)
																	a__					=					tf.tile(tf.expand_dims(__snake_case      ,1				)      ,(1, self.num_choices, 1)				)
																	a__					=					{
																	    'input_ids': multiple_choice_inputs_ids,
																	    'attention_mask': multiple_choice_input_mask,
																	    'token_type_ids': multiple_choice_token_type_ids,
																	}
																	a__					=					model(__snake_case				)
																	self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)				)
										def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[str]      ,__snake_case       :Dict      ,__snake_case       :Any      ,__snake_case       :List[str]      ,__snake_case       :List[str]      ,__snake_case       :List[Any]				) ->   Optional[int]:
																	a__					=					self.num_labels
																	a__					=					TFMobileBertForTokenClassification(config=__snake_case				)
																	a__					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	a__					=					model(__snake_case				)
																	self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)				)
										def 			lowerCamelCase__(				self       :str      ,__snake_case       :List[str]      ,__snake_case       :Union[str, Any]      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[Any]      ,__snake_case       :List[Any]      ,__snake_case       :Dict      ,__snake_case       :str				) ->   Optional[int]:
																	a__					=					TFMobileBertForQuestionAnswering(config=__snake_case				)
																	a__					=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
																	a__					=					model(__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       :Any				) ->   Tuple:
																	a__					=					self.prepare_config_and_inputs()
																	(
																	    (
																	    a__
																	)				, (
																	    a__
																	)				, (
																	    a__
																	)				, (
																	    a__
																	)				, (
																	    a__
																	)				, (
																	    a__
																	)				, (
																	    a__
																	)				, 
																	)					=					config_and_inputs
																	a__					=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
																	return config, inputs_dict





					def 			lowerCamelCase__(				self       :int				) ->   str:
												a__					=					TFMobileBertModelTest.TFMobileBertModelTester(self				)
												a__					=					ConfigTester(self      ,config_class=__snake_case      ,hidden_size=37				)
					def 			lowerCamelCase__(				self       :str				) ->   Any:
												self.config_tester.run_common_tests()
					def 			lowerCamelCase__(				self       :Tuple				) ->   List[Any]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_model(*__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Tuple:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_masked_lm(*__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Dict:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__snake_case				)
					def 			lowerCamelCase__(				self       :str				) ->   Union[str, Any]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   str:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_pretraining(*__snake_case				)
					def 			lowerCamelCase__(				self       :Any				) ->   List[str]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_question_answering(*__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   List[Any]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__snake_case				)
					def 			lowerCamelCase__(				self       :Any				) ->   Union[str, Any]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_mobilebert_for_token_classification(*__snake_case				)



					@slow
					def 			lowerCamelCase__(				self       :str				) ->   Optional[int]:
												# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												for model_name in ["google/mobilebert-uncased"]:
																			a__					=					TFMobileBertModel.from_pretrained(__snake_case				)
																			self.assertIsNotNone(__snake_case				)







@require_tf
class 	snake_case_       (unittest.TestCase				):
					@slow
					def 			lowerCamelCase__(				self       :Tuple				) ->   str:
												a__					=					TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'				)
												a__					=					tf.constant([[0, 1, 2, 3, 4, 5]]				)
												a__					=					model(__snake_case				)[0]

												a__					=					[1, 6, 3_05_22]
												self.assertEqual(output.shape      ,__snake_case				)

												a__					=					tf.constant(
												    [
												        [
												            [-4.5_91_95_47, -9.24_82_95, -9.64_52_56],
												            [-6.7_30_61_75, -6.44_02_84, -6.6_05_28_37],
												            [-7.2_74_35_06, -6.7_84_79_15, -6.02_46_73],
												        ]
												    ]				)
												tf.debugging.assert_near(output[:, :3, :3]      ,__snake_case      ,atol=1E-4				)








import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

snake_case							:	Optional[Any]					      =				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
snake_case							:	Dict					      =				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict       ):
							for attribute in key.split('.'       ):
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       )

							if weight_type is not None:
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       ).shape
							else:
														a__					=					hf_pointer.shape

							assert hf_shape == value.shape, (
							    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
							    F' {value.shape} for {full_name}'
							)

							if weight_type == "weight":
														a__					=					value
							elif weight_type == "weight_g":
														a__					=					value
							elif weight_type == "weight_v":
														a__					=					value
							elif weight_type == "bias":
														a__					=					value
							else:
														a__					=					value

							logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'       )






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[Any]       ):
							a__					=					[]
							a__					=					fairseq_model.state_dict()

							a__					=					hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														a__					=					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   hf_model.config.feat_extract_norm == 'group' ,   )
																					a__					=					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
																																			a__					=					True
																																			if "*" in mapped_key:
																																										a__					=					name.split(__lowerCAmelCase       )[0].split('.'       )[-2]
																																										a__					=					mapped_key.replace('*' ,   __lowerCAmelCase       )
																																			if "weight_g" in name:
																																										a__					=					'weight_g'
																																			elif "weight_v" in name:
																																										a__					=					'weight_v'
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										a__					=					'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										a__					=					'weight'
																																			else:
																																										a__					=					None

																																			set_recursively(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
																												continue
														if not is_used:
																					unused_weights.append(__lowerCAmelCase       )

							logger.warning(F'Unused weights: {unused_weights}'       )






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Optional[int]       ):
							a__					=					full_name.split('conv_layers.'       )[-1]
							a__					=					name.split('.'       )
							a__					=					int(items[0]       )
							a__					=					int(items[1]       )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																					)
																					a__					=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																					)
																					a__					=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'       )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																					    " found."
																					)
																					a__					=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																					)
																					a__					=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
							else:
														unused_weights.append(__lowerCAmelCase       )






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[Any]=None       ):
							# load the pre-trained checkpoints
							a__					=					torch.load(__lowerCAmelCase       )
							a__					=					WavLMConfigOrig(checkpoint['cfg']       )
							a__					=					WavLMOrig(__lowerCAmelCase       )
							model.load_state_dict(checkpoint['model']       )
							model.eval()

							if config_path is not None:
														a__					=					WavLMConfig.from_pretrained(__lowerCAmelCase       )
							else:
														a__					=					WavLMConfig()

							a__					=					WavLMModel(__lowerCAmelCase       )

							recursively_load_weights(__lowerCAmelCase ,   __lowerCAmelCase       )

							hf_wavlm.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	Union[str, Any]					      =				argparse.ArgumentParser()
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
						parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						snake_case							:	Union[str, Any]					      =				parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




