


"""simple docstring"""






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							LEDTokenizer
		A    =							LEDTokenizerFast
		A    =							True
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					super().setUp()
					_UpperCamelCase					:List[str]					     =	[
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''\u0120''',
					    '''\u0120l''',
					    '''\u0120n''',
					    '''\u0120lo''',
					    '''\u0120low''',
					    '''er''',
					    '''\u0120lowest''',
					    '''\u0120newer''',
					    '''\u0120wider''',
					    '''<unk>''',
					]
					_UpperCamelCase					:Any					     =	dict(zip(SCREAMING_SNAKE_CASE__					,				range(len(SCREAMING_SNAKE_CASE__   )   )   )   )
					_UpperCamelCase					:List[str]					     =	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
					_UpperCamelCase					:Optional[Any]					     =	{'''unk_token''': '''<unk>'''}

					_UpperCamelCase					:List[Any]					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']   )
					_UpperCamelCase					:int					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''merges_file''']   )
					with open(self.vocab_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(SCREAMING_SNAKE_CASE__   ) + '''\n'''   )
					with open(self.merges_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								fp.write('''\n'''.join(SCREAMING_SNAKE_CASE__   )   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					kwargs.update(self.special_tokens_map   )
					return self.tokenizer_class.from_pretrained(self.tmpdirname					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					kwargs.update(self.special_tokens_map   )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return "lower newer", "lower newer"
		@cached_property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''   )
		@cached_property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
					_UpperCamelCase					:Dict					     =	[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								_UpperCamelCase					:Union[str, Any]					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				max_length=len(SCREAMING_SNAKE_CASE__   )					,				padding=SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   )
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

								self.assertEqual((2, 9)					,				batch.input_ids.shape   )
								self.assertEqual((2, 9)					,				batch.attention_mask.shape   )
								_UpperCamelCase					:Tuple					     =	batch.input_ids.tolist()[0]
								self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								_UpperCamelCase					:Tuple					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				padding=SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   )
								self.assertIn('''input_ids'''					,				SCREAMING_SNAKE_CASE__   )
								self.assertIn('''attention_mask'''					,				SCREAMING_SNAKE_CASE__   )
								self.assertNotIn('''labels'''					,				SCREAMING_SNAKE_CASE__   )
								self.assertNotIn('''decoder_attention_mask'''					,				SCREAMING_SNAKE_CASE__   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	[
					    '''Summary of the text.''',
					    '''Another summary.''',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								_UpperCamelCase					:Dict					     =	tokenizer(text_target=SCREAMING_SNAKE_CASE__					,				max_length=32					,				padding='''max_length'''					,				return_tensors='''pt'''   )
								self.assertEqual(32					,				targets['''input_ids'''].shape[1]   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								_UpperCamelCase					:List[str]					     =	tokenizer(
								    ['''I am a small frog''' * 10_24, '''I am a small frog''']					,				padding=SCREAMING_SNAKE_CASE__					,				truncation=SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   )
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								self.assertEqual(batch.input_ids.shape					,				(2, 51_22)   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	['''A long paragraph for summarization.''']
					_UpperCamelCase					:Optional[int]					     =	[
					    '''Summary of the text.''',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								_UpperCamelCase					:Optional[Any]					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   )
								_UpperCamelCase					:str					     =	tokenizer(text_target=SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   )
								_UpperCamelCase					:Tuple					     =	inputs['''input_ids''']
								_UpperCamelCase					:List[str]					     =	targets['''input_ids''']
								self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
								self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
								self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
								self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								_UpperCamelCase					:Optional[int]					     =	['''Summary of the text.''', '''Another summary.''']
								_UpperCamelCase					:List[str]					     =	[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

								_UpperCamelCase					:Union[str, Any]					     =	tokenizer(SCREAMING_SNAKE_CASE__					,				padding=SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:Tuple					     =	[[0] * len(SCREAMING_SNAKE_CASE__   ) for x in encoded_output['''input_ids''']]
								_UpperCamelCase					:Optional[int]					     =	tokenizer.pad(SCREAMING_SNAKE_CASE__   )
								self.assertSequenceEqual(outputs['''global_attention_mask''']					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					pass





		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
											_UpperCamelCase					:Optional[int]					     =	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:List[str]					     =	self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Optional[Any]					     =	'''A, <mask> AllenNLP sentence.'''
											_UpperCamelCase					:Any					     =	tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__					,				add_special_tokens=SCREAMING_SNAKE_CASE__					,				return_token_type_ids=SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:int					     =	tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__					,				add_special_tokens=SCREAMING_SNAKE_CASE__					,				return_token_type_ids=SCREAMING_SNAKE_CASE__   )
											self.assertEqual(sum(tokens_r['''token_type_ids''']   )					,				sum(tokens_p['''token_type_ids''']   )   )
											self.assertEqual(
											    sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )					,				sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )					,				)

											_UpperCamelCase					:Optional[Any]					     =	tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
											_UpperCamelCase					:Dict					     =	tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )
											self.assertSequenceEqual(tokens_p['''input_ids''']					,				[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
											self.assertSequenceEqual(tokens_r['''input_ids''']					,				[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

											self.assertSequenceEqual(
											    SCREAMING_SNAKE_CASE__					,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
											self.assertSequenceEqual(
											    SCREAMING_SNAKE_CASE__					,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )





"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__				:Optional[int]			   =	logging.get_logger(__name__)

UpperCamelCase__				:int			   =	{
    """post_extract_proj""": """feature_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_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.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 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			for attribute in key.split('''.'''		):
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		)

			if weight_type is not None:
						_UpperCamelCase					:Any					     =	getattr(snake_case__       ,					snake_case__		).shape
			else:
						_UpperCamelCase					:Optional[int]					     =	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":
						_UpperCamelCase					:str					     =	value
			elif weight_type == "weight_g":
						_UpperCamelCase					:Dict					     =	value
			elif weight_type == "weight_v":
						_UpperCamelCase					:Optional[Any]					     =	value
			elif weight_type == "bias":
						_UpperCamelCase					:str					     =	value
			else:
						_UpperCamelCase					:int					     =	value

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Optional[int]					     =	[]
			_UpperCamelCase					:List[str]					     =	fairseq_model.state_dict()

			_UpperCamelCase					:str					     =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCamelCase					:Optional[Any]					     =	False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					hf_model.config.feat_extract_norm == '''group'''       ,					)
									_UpperCamelCase					:Dict					     =	True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCamelCase					:Optional[int]					     =	'''sew.''' + 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]:
															_UpperCamelCase					:List[str]					     =	True
															if "*" in mapped_key:
																		_UpperCamelCase					:List[str]					     =	name.split(snake_case__		)[0].split('''.'''		)[-2]
																		_UpperCamelCase					:Tuple					     =	mapped_key.replace('''*'''       ,					snake_case__		)
															if "weight_g" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight_g'''
															elif "weight_v" in name:
																		_UpperCamelCase					:Union[str, Any]					     =	'''weight_v'''
															elif "weight" in name:
																		_UpperCamelCase					:List[Any]					     =	'''weight'''
															elif "bias" in name:
																		_UpperCamelCase					:List[Any]					     =	'''bias'''
															else:
																		_UpperCamelCase					:List[Any]					     =	None
															set_recursively(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
												continue
						if not is_used:
									unused_weights.append(snake_case__		)

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





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	full_name.split('''conv_layers.'''		)[-1]
			_UpperCamelCase					:Optional[int]					     =	name.split('''.'''		)
			_UpperCamelCase					:Optional[Any]					     =	int(items[0]		)
			_UpperCamelCase					:List[str]					     =	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."
									)
									_UpperCamelCase					:Optional[int]					     =	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."
									)
									_UpperCamelCase					:Optional[Any]					     =	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."
									)
									_UpperCamelCase					:int					     =	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."
									)
									_UpperCamelCase					:Dict					     =	value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
			else:
						unused_weights.append(snake_case__		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:str					     =	SEWConfig()
			if is_finetuned:
						_UpperCamelCase					:Optional[int]					     =	model.wav_encoder.wav_model.cfg
			else:
						_UpperCamelCase					:Dict					     =	model.cfg

			_UpperCamelCase					:Dict					     =	fs_config.conv_bias
			_UpperCamelCase					:int					     =	eval(fs_config.conv_feature_layers		)
			_UpperCamelCase					:List[Any]					     =	[x[0] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[1] for x in conv_layers]
			_UpperCamelCase					:Optional[int]					     =	[x[2] for x in conv_layers]
			_UpperCamelCase					:str					     =	'''gelu'''
			_UpperCamelCase					:Optional[int]					     =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
			_UpperCamelCase					:List[Any]					     =	0.0
			_UpperCamelCase					:Optional[int]					     =	fs_config.activation_fn.name
			_UpperCamelCase					:str					     =	fs_config.encoder_embed_dim
			_UpperCamelCase					:Dict					     =	0.02
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_ffn_embed_dim
			_UpperCamelCase					:str					     =	1E-5
			_UpperCamelCase					:int					     =	fs_config.encoder_layerdrop
			_UpperCamelCase					:Union[str, Any]					     =	fs_config.encoder_attention_heads
			_UpperCamelCase					:List[str]					     =	fs_config.conv_pos_groups
			_UpperCamelCase					:List[Any]					     =	fs_config.conv_pos
			_UpperCamelCase					:List[str]					     =	len(snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	fs_config.encoder_layers
			_UpperCamelCase					:Optional[int]					     =	fs_config.squeeze_factor

			# take care of any params that are overridden by the Wav2VecCtc model
			if is_finetuned:
						_UpperCamelCase					:List[Any]					     =	model.cfg
						_UpperCamelCase					:List[Any]					     =	fs_config.final_dropout
						_UpperCamelCase					:Dict					     =	fs_config.layerdrop
			_UpperCamelCase					:Any					     =	fs_config.activation_dropout
			_UpperCamelCase					:List[str]					     =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
			_UpperCamelCase					:Optional[Any]					     =	fs_config.attention_dropout
			_UpperCamelCase					:List[Any]					     =	fs_config.dropout_input
			_UpperCamelCase					:Dict					     =	fs_config.dropout
			_UpperCamelCase					:int					     =	fs_config.mask_channel_length
			_UpperCamelCase					:Tuple					     =	fs_config.mask_channel_prob
			_UpperCamelCase					:int					     =	fs_config.mask_length
			_UpperCamelCase					:Dict					     =	fs_config.mask_prob

			_UpperCamelCase					:List[Any]					     =	'''Wav2Vec2FeatureExtractor'''
			_UpperCamelCase					:Optional[Any]					     =	'''Wav2Vec2CTCTokenizer'''

			return config





@torch.no_grad()
def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__=None       ,					snake_case__=None       ,					snake_case__=True		)					->  int:

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

			if config_path is not None:
						_UpperCamelCase					:Any					     =	SEWConfig.from_pretrained(snake_case__		)
			else:
						_UpperCamelCase					:List[str]					     =	convert_config(model[0]       ,					snake_case__		)
			_UpperCamelCase					:List[str]					     =	model[0].eval()

			_UpperCamelCase					:Optional[Any]					     =	True if config.feat_extract_norm == '''layer''' else False
			_UpperCamelCase					:str					     =	WavaVecaFeatureExtractor(
			    feature_size=1       ,					sampling_rate=1_60_00       ,					padding_value=0       ,					do_normalize=snake_case__       ,					return_attention_mask=snake_case__       ,					)

			if is_finetuned:
						if dict_path:
									_UpperCamelCase					:int					     =	Dictionary.load(snake_case__		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_UpperCamelCase					:List[Any]					     =	target_dict.pad_index
									_UpperCamelCase					:str					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.pad_index
									_UpperCamelCase					:Dict					     =	target_dict.bos_index
									_UpperCamelCase					:int					     =	target_dict.eos_index
									_UpperCamelCase					:str					     =	len(target_dict.symbols		)
									_UpperCamelCase					:Optional[int]					     =	os.path.join(snake_case__       ,					'''vocab.json'''		)
									if not os.path.isdir(snake_case__		):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__		)		)
												return
									os.makedirs(snake_case__       ,					exist_ok=snake_case__		)
									with open(snake_case__       ,					'''w'''       ,					encoding='''utf-8'''		) as vocab_handle:
												json.dump(target_dict.indices       ,					snake_case__		)
									_UpperCamelCase					:int					     =	WavaVecaCTCTokenizer(
									    snake_case__       ,					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=snake_case__       ,					)
									_UpperCamelCase					:Dict					     =	WavaVecaProcessor(feature_extractor=snake_case__       ,					tokenizer=snake_case__		)
									processor.save_pretrained(snake_case__		)

						_UpperCamelCase					:Tuple					     =	SEWForCTC(snake_case__		)
			else:
						_UpperCamelCase					:str					     =	SEWModel(snake_case__		)
						feature_extractor.save_pretrained(snake_case__		)

			recursively_load_weights(snake_case__       ,					snake_case__       ,					snake_case__		)

			hf_model.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__				:List[str]			   =	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"""
					)
					UpperCamelCase__				:Tuple			   =	parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

