

"""simple docstring"""

from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


a__			:						Any							=			{
    """id""": """5733be284776f41900661182""",
    """title""": """University_of_Notre_Dame""",
    """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
    """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
    """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
}





@pytest.mark.parametrize(
    '''task_cls'''			,						[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,						)
def 					__UpperCamelCase (     a__:			Optional[Any]      )					->      Optional[Any]:




					"""simple docstring"""



					__lowercase        =      task_cls()
					__lowercase        =      asdict(a__      )
					__lowercase        =      task_template_from_dict(a__      )
					assert task == reloaded


class       _snake_case  :







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      LanguageModeling(text_column='''input_text'''				)
											assert {"input_text": "text"} == task.column_mapping







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      Features({'''text''': Value('''string'''				)}				)
											__lowercase        =      {'''text_column''': '''input_text'''}
											__lowercase        =      LanguageModeling.from_dict(UpperCAmelCase_				)
											assert "language-modeling" == task.task
											assert input_schema == task.input_schema



class       _snake_case  (    UpperCamelCase							):







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      sorted(['''pos''', '''neg''']				)







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      TextClassification(text_column='''input_text'''  ,				label_column='''input_label'''				)
											self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}  ,				task.column_mapping				)







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      Features({'''text''': Value('''string'''				)}				)
											# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
											__lowercase        =      Features({'''labels''': ClassLabel}				)
											__lowercase        =      {'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
											__lowercase        =      TextClassification.from_dict(UpperCAmelCase_				)
											self.assertEqual('''text-classification'''  ,				task.task				)
											self.assertEqual(UpperCAmelCase_  ,				task.input_schema				)
											self.assertEqual(UpperCAmelCase_  ,				task.label_schema				)







						def 							_UpperCamelCase     (self				)      ->    Any:
											__lowercase        =      TextClassification(text_column='''input_text'''  ,				label_column='''input_label'''				)
											self.assertEqual(task.label_schema['''labels''']  ,				UpperCAmelCase_				)
											__lowercase        =      task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
											self.assertEqual(task.label_schema['''labels''']  ,				ClassLabel(names=self.labels				)				)



class       _snake_case  (    UpperCamelCase							):







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      QuestionAnsweringExtractive(
											    context_column='''input_context'''  ,				question_column='''input_question'''  ,				answers_column='''input_answers'''				)
											self.assertDictEqual(
											    {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}  ,				task.column_mapping				)







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      Features({'''question''': Value('''string'''				), '''context''': Value('''string'''				)}				)
											__lowercase        =      Features(
											    {
											        '''answers''': Sequence(
											            {
											                '''text''': Value('''string'''				),
											                '''answer_start''': Value('''int32'''				),
											            }				)
											    }				)
											__lowercase        =      {
											    '''context_column''': '''input_input_context''',
											    '''question_column''': '''input_question''',
											    '''answers_column''': '''input_answers''',
											}
											__lowercase        =      QuestionAnsweringExtractive.from_dict(UpperCAmelCase_				)
											self.assertEqual('''question-answering-extractive'''  ,				task.task				)
											self.assertEqual(UpperCAmelCase_  ,				task.input_schema				)
											self.assertEqual(UpperCAmelCase_  ,				task.label_schema				)



class       _snake_case  (    UpperCamelCase							):







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											__lowercase        =      Summarization(text_column='''input_text'''  ,				summary_column='''input_summary'''				)
											self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}  ,				task.column_mapping				)







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      Features({'''text''': Value('''string'''				)}				)
											__lowercase        =      Features({'''summary''': Value('''string'''				)}				)
											__lowercase        =      {'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
											__lowercase        =      Summarization.from_dict(UpperCAmelCase_				)
											self.assertEqual('''summarization'''  ,				task.task				)
											self.assertEqual(UpperCAmelCase_  ,				task.input_schema				)
											self.assertEqual(UpperCAmelCase_  ,				task.label_schema				)



class       _snake_case  (    UpperCamelCase							):







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      AutomaticSpeechRecognition(audio_column='''input_audio'''  ,				transcription_column='''input_transcription'''				)
											self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}  ,				task.column_mapping				)







						def 							_UpperCamelCase     (self				)      ->    Optional[int]:
											__lowercase        =      Features({'''audio''': Audio()}				)
											__lowercase        =      Features({'''transcription''': Value('''string'''				)}				)
											__lowercase        =      {
											    '''audio_column''': '''input_audio''',
											    '''transcription_column''': '''input_transcription''',
											}
											__lowercase        =      AutomaticSpeechRecognition.from_dict(UpperCAmelCase_				)
											self.assertEqual('''automatic-speech-recognition'''  ,				task.task				)
											self.assertEqual(UpperCAmelCase_  ,				task.input_schema				)
											self.assertEqual(UpperCAmelCase_  ,				task.label_schema				)



class       _snake_case  (    UpperCamelCase							):







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      sorted(['''pos''', '''neg''']				)







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      AudioClassification(audio_column='''input_audio'''  ,				label_column='''input_label'''				)
											self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}  ,				task.column_mapping				)







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      Features({'''audio''': Audio()}				)
											__lowercase        =      Features({'''labels''': ClassLabel}				)
											__lowercase        =      {
											    '''audio_column''': '''input_image''',
											    '''label_column''': '''input_label''',
											}
											__lowercase        =      AudioClassification.from_dict(UpperCAmelCase_				)
											self.assertEqual('''audio-classification'''  ,				task.task				)
											self.assertEqual(UpperCAmelCase_  ,				task.input_schema				)
											self.assertEqual(UpperCAmelCase_  ,				task.label_schema				)







						def 							_UpperCamelCase     (self				)      ->    Any:
											__lowercase        =      AudioClassification(audio_column='''input_audio'''  ,				label_column='''input_label'''				)
											self.assertEqual(task.label_schema['''labels''']  ,				UpperCAmelCase_				)
											__lowercase        =      task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
											self.assertEqual(task.label_schema['''labels''']  ,				ClassLabel(names=self.labels				)				)



class       _snake_case  (    UpperCamelCase							):







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      sorted(['''pos''', '''neg''']				)







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      ImageClassification(image_column='''input_image'''  ,				label_column='''input_label'''				)
											self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}  ,				task.column_mapping				)







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      Features({'''image''': Image()}				)
											__lowercase        =      Features({'''labels''': ClassLabel}				)
											__lowercase        =      {
											    '''image_column''': '''input_image''',
											    '''label_column''': '''input_label''',
											}
											__lowercase        =      ImageClassification.from_dict(UpperCAmelCase_				)
											self.assertEqual('''image-classification'''  ,				task.task				)
											self.assertEqual(UpperCAmelCase_  ,				task.input_schema				)
											self.assertEqual(UpperCAmelCase_  ,				task.label_schema				)







						def 							_UpperCamelCase     (self				)      ->    Tuple:
											__lowercase        =      ImageClassification(image_column='''input_image'''  ,				label_column='''input_label'''				)
											self.assertEqual(task.label_schema['''labels''']  ,				UpperCAmelCase_				)
											__lowercase        =      task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
											self.assertEqual(task.label_schema['''labels''']  ,				ClassLabel(names=self.labels				)				)



class       _snake_case  (    UpperCamelCase							):







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      DatasetInfo(task_templates=QuestionAnsweringExtractive()				)
											__lowercase        =      Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}  ,				info=UpperCAmelCase_				)
											assert isinstance(dataset.info.task_templates  ,				UpperCAmelCase_				)
											assert len(dataset.info.task_templates				) == 1

											def keep_task(UpperCAmelCase_				):
																return x

											def dont_keep_task(UpperCAmelCase_				):
																__lowercase        =      deepcopy(UpperCAmelCase_				)
																__lowercase        =      0
																return out

											__lowercase        =      dataset.map(UpperCAmelCase_				)
											assert mapped_dataset.info.task_templates == dataset.info.task_templates
											# reload from cache
											__lowercase        =      dataset.map(UpperCAmelCase_				)
											assert mapped_dataset.info.task_templates == dataset.info.task_templates

											__lowercase        =      dataset.map(UpperCAmelCase_				)
											assert mapped_dataset.info.task_templates == []
											# reload from cache
											__lowercase        =      dataset.map(UpperCAmelCase_				)
											assert mapped_dataset.info.task_templates == []







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      Features({'''text''': Value('''string'''				), '''label''': ClassLabel(names=('''pos''', '''neg''')				)}				)
											__lowercase        =      TextClassification(text_column='''text'''  ,				label_column='''label'''				)
											__lowercase        =      DatasetInfo(features=UpperCAmelCase_  ,				task_templates=UpperCAmelCase_				)
											__lowercase        =      Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}  ,				info=UpperCAmelCase_				)

											def process(UpperCAmelCase_				):
																return example

											__lowercase        =      dataset.remove_columns('''label'''				)
											__lowercase        =      modified_dataset.map(UpperCAmelCase_				)
											assert mapped_dataset.info.task_templates == []





"""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
							)


