


from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
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 (
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		)


class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    str	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    List[Any]=13	,							lowerCamelCase_     :    Optional[Any]=7	,							lowerCamelCase_     :    int=True	,							lowerCamelCase_     :    Optional[int]=True	,							lowerCamelCase_     :    Tuple=True	,							lowerCamelCase_     :    Tuple=True	,							lowerCamelCase_     :    Any=99	,							lowerCamelCase_     :    int=32	,							lowerCamelCase_     :    Dict=2	,							lowerCamelCase_     :    int=4	,							lowerCamelCase_     :    str=37	,							lowerCamelCase_     :    int="gelu"	,							lowerCamelCase_     :    Union[str, Any]=0.1	,							lowerCamelCase_     :    List[str]=0.1	,							lowerCamelCase_     :    int=512	,							lowerCamelCase_     :    List[str]=16	,							lowerCamelCase_     :    int=2	,							lowerCamelCase_     :    List[str]=0.02	,							lowerCamelCase_     :    List[str]=3	,							lowerCamelCase_     :    str=4	,							lowerCamelCase_     :    Any=None	,							):
								_lowerCAmelCase  						=parent
								_lowerCAmelCase  						=13
								_lowerCAmelCase  						=7
								_lowerCAmelCase  						=True
								_lowerCAmelCase  						=True
								_lowerCAmelCase  						=True
								_lowerCAmelCase  						=True
								_lowerCAmelCase  						=99
								_lowerCAmelCase  						=32
								_lowerCAmelCase  						=2
								_lowerCAmelCase  						=4
								_lowerCAmelCase  						=37
								_lowerCAmelCase  						="""gelu"""
								_lowerCAmelCase  						=0.1
								_lowerCAmelCase  						=0.1
								_lowerCAmelCase  						=512
								_lowerCAmelCase  						=16
								_lowerCAmelCase  						=False
								_lowerCAmelCase  						=-1
								_lowerCAmelCase  						=True
								_lowerCAmelCase  						=2
								_lowerCAmelCase  						=0.02
								_lowerCAmelCase  						=3
								_lowerCAmelCase  						=4
								_lowerCAmelCase  						=None






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								_lowerCAmelCase  						=ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size					)

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

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

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

								_lowerCAmelCase  						=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	,							relative_attention=self.relative_attention	,							max_relative_positions=self.max_relative_positions	,							position_biased_input=self.position_biased_input	,							initializer_range=self.initializer_range	,							return_dict=UpperCAmelCase_	,							)

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






				def        lowerCAmelCase__			(				self     :    Dict	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Dict					):
								_lowerCAmelCase  						=TFDebertaModel(config=UpperCAmelCase_					)
								_lowerCAmelCase  						={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

								_lowerCAmelCase  						=[input_ids, input_mask]
								_lowerCAmelCase  						=model(UpperCAmelCase_					)

								_lowerCAmelCase  						=model(UpperCAmelCase_					)

								self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)					)






				def        lowerCAmelCase__			(				self     :    List[Any]	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    Tuple					):
								_lowerCAmelCase  						=TFDebertaForMaskedLM(config=UpperCAmelCase_					)
								_lowerCAmelCase  						={
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								_lowerCAmelCase  						=model(UpperCAmelCase_					)
								self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)					)






				def        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    int	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    Optional[int]					):
								_lowerCAmelCase  						=self.num_labels
								_lowerCAmelCase  						=TFDebertaForSequenceClassification(config=UpperCAmelCase_					)
								_lowerCAmelCase  						={
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}

								_lowerCAmelCase  						=model(UpperCAmelCase_					)
								self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)					)






				def        lowerCAmelCase__			(				self     :    int	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Dict					):
								_lowerCAmelCase  						=self.num_labels
								_lowerCAmelCase  						=TFDebertaForTokenClassification(config=UpperCAmelCase_					)
								_lowerCAmelCase  						={
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								_lowerCAmelCase  						=model(UpperCAmelCase_					)
								self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.num_labels)					)






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Union[str, Any]					):
								_lowerCAmelCase  						=TFDebertaForQuestionAnswering(config=UpperCAmelCase_					)
								_lowerCAmelCase  						={
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}

								_lowerCAmelCase  						=model(UpperCAmelCase_					)
								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     :    Tuple					):
								_lowerCAmelCase  						=self.prepare_config_and_inputs()
								(
								    (
								    _lowerCAmelCase			
								)						,							(
								    _lowerCAmelCase			
								)						,							(
								    _lowerCAmelCase			
								)						,							(
								    _lowerCAmelCase			
								)						,							(
								    _lowerCAmelCase			
								)						,							(
								    _lowerCAmelCase			
								)						,							(
								    _lowerCAmelCase			
								)						,										
								)  						=config_and_inputs
								_lowerCAmelCase  						={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
								return config, inputs_dict


@require_tf
class        __lowerCamelCase      (				lowerCamelCase_				,		lowerCamelCase_				,		unittest.TestCase ):


				"""simple docstring"""







				a_:		Tuple					      =	(
				    (
				        TFDebertaModel,
				        TFDebertaForMaskedLM,
				        TFDebertaForQuestionAnswering,
				        TFDebertaForSequenceClassification,
				        TFDebertaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				a_:		str					      =	(
				    {
				        """feature-extraction""": TFDebertaModel,
				        """fill-mask""": TFDebertaForMaskedLM,
				        """question-answering""": TFDebertaForQuestionAnswering,
				        """text-classification""": TFDebertaForSequenceClassification,
				        """token-classification""": TFDebertaForTokenClassification,
				        """zero-shot""": TFDebertaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				a_:		List[str]					      =	False
				a_:		str					      =	False






				def        lowerCAmelCase__			(				self     :    Dict					):
								_lowerCAmelCase  						=TFDebertaModelTester(self					)
								_lowerCAmelCase  						=ConfigTester(self	,							config_class=UpperCAmelCase_	,							hidden_size=37					)






				def        lowerCAmelCase__			(				self     :    int					):
								self.config_tester.run_common_tests()






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								_lowerCAmelCase  						=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_					)






				def        lowerCAmelCase__			(				self     :    List[str]					):
								_lowerCAmelCase  						=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_					)






				def        lowerCAmelCase__			(				self     :    Any					):
								_lowerCAmelCase  						=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_					)






				def        lowerCAmelCase__			(				self     :    Tuple					):
								_lowerCAmelCase  						=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_					)






				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								_lowerCAmelCase  						=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_					)






				@slow
				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								_lowerCAmelCase  						=TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""					)
								self.assertIsNotNone(UpperCAmelCase_					)


@require_tf
class        __lowerCamelCase      (				unittest.TestCase ):


				"""simple docstring"""







				@unittest.skip(reason="""Model not available yet"""					)
				def        lowerCAmelCase__			(				self     :    int					):
								pass






				@slow
				def        lowerCAmelCase__			(				self     :    Dict					):
								_lowerCAmelCase  						=TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""					)
								_lowerCAmelCase  						=tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
								_lowerCAmelCase  						=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
								_lowerCAmelCase  						=model(UpperCAmelCase_	,							attention_mask=UpperCAmelCase_					)[0]

								_lowerCAmelCase  						=tf.constant(
								    [
								        [
								            [-0.5985_5896, -0.8055_2566, -0.846_2135],
								            [1.448_4025, -0.9348_3794, -0.8059_3085],
								            [0.312_2741, 0.0031_6059, -1.413_1377],
								        ]
								    ]					)
								tf.debugging.assert_near(output[:, 1:4, 1:4]	,							UpperCAmelCase_	,							atol=1e-4					)




"""simple docstring"""

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__			:						int							=			logging.get_logger(__name__)

a__			:						Union[str, Any]							=			{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def 					__UpperCamelCase (     a__:			str			,						a__:			Any			,						a__:			Dict			,						a__:			List[str]			,						a__:			str      )					->      str:




					"""simple docstring"""



					for attribute in key.split('''.'''      ):
										__lowercase        =      getattr(a__			,						a__      )

					if weight_type is not None:
										__lowercase        =      getattr(a__			,						a__      ).shape
					else:
										__lowercase        =      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":
										__lowercase        =      value
					elif weight_type == "weight_g":
										__lowercase        =      value
					elif weight_type == "weight_v":
										__lowercase        =      value
					elif weight_type == "bias":
										__lowercase        =      value
					else:
										__lowercase        =      value

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





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			Optional[int]			,						a__:			Optional[Any]      )					->      Any:




					"""simple docstring"""



					__lowercase        =      []
					__lowercase        =      fairseq_model.state_dict()

					__lowercase        =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__lowercase        =      False
										if "conv_layers" in name:
															load_conv_layer(
															    a__			,						a__			,						a__			,						a__			,						hf_model.config.feat_extract_norm == '''group'''			,						)
															__lowercase        =      True
										else:
															for key, mapped_key in MAPPING.items():
																				__lowercase        =      '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																				if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
																									__lowercase        =      True
																									if "*" in mapped_key:
																														__lowercase        =      name.split(a__      )[0].split('''.'''      )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														__lowercase        =      mapped_key.replace('''*'''			,						a__      )
																									if "weight_g" in name:
																														__lowercase        =      '''weight_g'''
																									elif "weight_v" in name:
																														__lowercase        =      '''weight_v'''
																									elif "weight" in name:
																														__lowercase        =      '''weight'''
																									elif "bias" in name:
																														__lowercase        =      '''bias'''
																									else:
																														__lowercase        =      None
																									set_recursively(a__			,						a__			,						a__			,						a__			,						a__      )
																				continue
										if not is_used:
															unused_weights.append(a__      )

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





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			int			,						a__:			List[Any]			,						a__:			int			,						a__:			List[str]      )					->      Optional[Any]:




					"""simple docstring"""



					__lowercase        =      full_name.split('''conv_layers.'''      )[-1]
					__lowercase        =      name.split('''.'''      )
					__lowercase        =      int(items[0]      )
					__lowercase        =      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.'''
															)
															__lowercase        =      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.'''
															)
															__lowercase        =      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."
															)
															__lowercase        =      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.'''
															)
															__lowercase        =      value
															logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
					else:
										unused_weights.append(a__      )





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			List[str]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      SEWDConfig()
					if is_finetuned:
										__lowercase        =      model.wav_encoder.wav_model.cfg
					else:
										__lowercase        =      model.cfg

					__lowercase        =      fs_config.conv_bias
					__lowercase        =      eval(fs_config.conv_feature_layers      )
					__lowercase        =      [x[0] for x in conv_layers]
					__lowercase        =      [x[1] for x in conv_layers]
					__lowercase        =      [x[2] for x in conv_layers]
					__lowercase        =      '''gelu'''
					__lowercase        =      '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
					__lowercase        =      0.0
					__lowercase        =      fs_config.activation_fn.name
					__lowercase        =      fs_config.encoder_embed_dim
					__lowercase        =      0.02
					__lowercase        =      fs_config.encoder_ffn_embed_dim
					__lowercase        =      1e-5
					__lowercase        =      fs_config.encoder_layerdrop
					__lowercase        =      fs_config.encoder_attention_heads
					__lowercase        =      fs_config.conv_pos_groups
					__lowercase        =      fs_config.conv_pos
					__lowercase        =      len(a__      )
					__lowercase        =      fs_config.encoder_layers
					__lowercase        =      fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					__lowercase        =      fs_config.max_position_embeddings
					__lowercase        =      fs_config.position_buckets
					__lowercase        =      fs_config.share_att_key
					__lowercase        =      fs_config.relative_attention
					__lowercase        =      fs_config.position_biased_input
					__lowercase        =      tuple(fs_config.pos_att_type.split('''|'''      )      )
					__lowercase        =      fs_config.norm_rel_ebd

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										__lowercase        =      model.cfg
										__lowercase        =      fs_config.final_dropout
										__lowercase        =      fs_config.layerdrop
					__lowercase        =      fs_config.activation_dropout
					__lowercase        =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					__lowercase        =      fs_config.attention_dropout
					__lowercase        =      fs_config.dropout_input
					__lowercase        =      fs_config.dropout
					__lowercase        =      fs_config.mask_channel_length
					__lowercase        =      fs_config.mask_channel_prob
					__lowercase        =      fs_config.mask_length
					__lowercase        =      fs_config.mask_prob

					__lowercase        =      '''Wav2Vec2FeatureExtractor'''
					__lowercase        =      '''Wav2Vec2CTCTokenizer'''

					return config





@torch.no_grad()
def 					__UpperCamelCase (     a__:			str			,						a__:			str			,						a__:			Optional[int]=None			,						a__:			str=None			,						a__:			Union[str, Any]=True      )					->      List[Any]:




					"""simple docstring"""



					if is_finetuned:
										__lowercase    ,			__lowercase    ,			__lowercase        =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]			,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
					else:
										__lowercase    ,			__lowercase    ,			__lowercase        =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

					if config_path is not None:
										__lowercase        =      SEWDConfig.from_pretrained(a__      )
					else:
										__lowercase        =      convert_config(model[0]			,						a__      )
					__lowercase        =      model[0].eval()

					__lowercase        =      True if config.feat_extract_norm == '''layer''' else False
					__lowercase        =      WavaVecaFeatureExtractor(
					    feature_size=1			,						sampling_rate=1_60_00			,						padding_value=0			,						do_normalize=a__			,						return_attention_mask=a__			,						)

					if is_finetuned:
										if dict_path:
															__lowercase        =      Dictionary.load(a__      )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__lowercase        =      target_dict.pad_index
															__lowercase        =      target_dict.bos_index
															__lowercase        =      target_dict.pad_index
															__lowercase        =      target_dict.bos_index
															__lowercase        =      target_dict.eos_index
															__lowercase        =      len(target_dict.symbols      )
															__lowercase        =      os.path.join(a__			,						'''vocab.json'''      )
															if not os.path.isdir(a__      ):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__      )      )
																				return
															os.makedirs(a__			,						exist_ok=a__      )
															with open(a__			,						'''w'''			,						encoding='''utf-8'''      ) as vocab_handle:
																				json.dump(target_dict.indices			,						a__      )
															__lowercase        =      WavaVecaCTCTokenizer(
															    a__			,						unk_token=target_dict.unk_word			,						pad_token=target_dict.pad_word			,						bos_token=target_dict.bos_word			,						eos_token=target_dict.eos_word			,						word_delimiter_token='''|'''			,						do_lower_case=a__			,						)
															__lowercase        =      WavaVecaProcessor(feature_extractor=a__			,						tokenizer=a__      )
															processor.save_pretrained(a__      )

										__lowercase        =      SEWDForCTC(a__      )
					else:
										__lowercase        =      SEWDModel(a__      )
										feature_extractor.save_pretrained(a__      )

					recursively_load_weights(a__			,						a__			,						a__      )

					hf_model.save_pretrained(a__      )


if __name__ == "__main__":
							a__			:						Optional[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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							a__			:						Union[str, Any]							=			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)


