






import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase     :							str	     =			get_tests_dir('''fixtures/spiece.model''')




@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase_  (		lowerCamelCase_    ,     unittest.TestCase    ):


						'''simple docstring'''




						A   :      Union[str, Any]						 =    ErnieMTokenizer
						A   :      Any						 =    False
						A   :      List[str]						 =    True
						A   :      str						 =    False
						A   :      int						 =    False






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								snake_case_ :							Any         =				ErnieMTokenizer(snake_case_						, unk_token="<unk>"						, pad_token="<pad>" )
								tokenizer.save_pretrained(self.tmpdirname )






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> Optional[int]:
								snake_case_ :							List[str]         =				'this is a test'
								snake_case_ :							Any         =				'this is a test'
								return input_text, output_text






						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								snake_case_ :							Union[str, Any]         =				'<pad>'
								snake_case_ :							Union[str, Any]         =				0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ )						, snake_case_ )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ )						, snake_case_ )






						def 					_lowerCAmelCase				(		self )       -> Any:
								snake_case_ :							Dict         =				list(self.get_tokenizer().get_vocab().keys() )

								self.assertEqual(vocab_keys[0]						, "<pad>" )
								self.assertEqual(vocab_keys[1]						, "<unk>" )
								self.assertEqual(vocab_keys[-1]						, "▁eloquent" )
								self.assertEqual(len(snake_case_ )						, 3_0000 )






						def 					_lowerCAmelCase				(		self )       -> Dict:
								self.assertEqual(self.get_tokenizer().vocab_size						, 3_0000 )






						def 					_lowerCAmelCase				(		self )       -> List[str]:

								if not self.test_rust_tokenizer:
										return

								snake_case_ :							Any         =				self.get_tokenizer()
								snake_case_ :							List[Any]         =				self.get_rust_tokenizer()

								snake_case_ :							Union[str, Any]         =				'I was born in 92000, and this is falsé.'

								snake_case_ :							Optional[Any]         =				tokenizer.tokenize(snake_case_ )
								snake_case_ :							Tuple         =				rust_tokenizer.tokenize(snake_case_ )
								self.assertListEqual(snake_case_						, snake_case_ )

								snake_case_ :							Optional[int]         =				tokenizer.encode(snake_case_						, add_special_tokens=snake_case_ )
								snake_case_ :							str         =				rust_tokenizer.encode(snake_case_						, add_special_tokens=snake_case_ )
								self.assertListEqual(snake_case_						, snake_case_ )

								snake_case_ :							Optional[int]         =				self.get_rust_tokenizer()
								snake_case_ :							Dict         =				tokenizer.encode(snake_case_ )
								snake_case_ :							Optional[int]         =				rust_tokenizer.encode(snake_case_ )
								self.assertListEqual(snake_case_						, snake_case_ )






						def 					_lowerCAmelCase				(		self )       -> Optional[int]:
								snake_case_ :							Any         =				ErnieMTokenizer(snake_case_						, do_lower_case=snake_case_						, unk_token="<unk>"						, pad_token="<pad>" )

								snake_case_ :							Optional[Any]         =				tokenizer.tokenize("This is a test" )
								self.assertListEqual(snake_case_						, ["▁this", "▁is", "▁a", "▁test"] )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )						, [48, 25, 21, 1289] )

								snake_case_ :							Tuple         =				tokenizer.tokenize("I was born in 92000, and this is falsé." )
								# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
								self.assertListEqual(
								    snake_case_						, ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."] )
								snake_case_ :							Union[str, Any]         =				tokenizer.convert_tokens_to_ids(snake_case_ )
								self.assertListEqual(snake_case_						, [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9] )

								snake_case_ :							Optional[Any]         =				tokenizer.convert_ids_to_tokens(snake_case_ )
								self.assertListEqual(
								    snake_case_						, ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]						, )






						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								snake_case_ :							Union[str, Any]         =				ErnieMTokenizer(snake_case_						, unk_token="<unk>"						, pad_token="<pad>" )

								snake_case_ :							Optional[int]         =				tokenizer.encode("sequence builders" )
								snake_case_ :							List[Any]         =				tokenizer.encode("multi-sequence build" )

								snake_case_ :							str         =				tokenizer.build_inputs_with_special_tokens(snake_case_ )
								snake_case_ :							Optional[int]         =				tokenizer.build_inputs_with_special_tokens(snake_case_						, snake_case_ )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
								    tokenizer.sep_token_id
								] + text_a + [tokenizer.sep_token_id]






						@slow
						def 					_lowerCAmelCase				(		self )       -> Union[str, Any]:
								snake_case_ :							Union[str, Any]         =				{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=snake_case_						, model_name="susnato/ernie-m-base_pytorch"						, sequences=[
								        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
								        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
								        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
								        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
								        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
								        "conditioning on both left and right context in all layers.",
								        "The quick brown fox jumps over the lazy dog.",
								    ]						, )

'''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()
snake_case__     :			Tuple      			=  logging.get_logger(__name__)

snake_case__     :			Tuple      			=  {
    '''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 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							Any	,     lowerCamelCase_					:							Any   ):



     """simple docstring"""





     for attribute in key.split('.'   ):
          UpperCAmelCase_							:   List[Any]   =      getattr(lowerCamelCase_	,     lowerCamelCase_   )

     if weight_type is not None:
          UpperCAmelCase_							:   Any   =      getattr(lowerCamelCase_	,     lowerCamelCase_   ).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_							:   Optional[Any]   =      value
     elif weight_type == "weight_g":
          UpperCAmelCase_							:   str   =      value
     elif weight_type == "weight_v":
          UpperCAmelCase_							:   str   =      value
     elif weight_type == "bias":
          UpperCAmelCase_							:   Tuple   =      value
     else:
          UpperCAmelCase_							:   str   =      value

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


def 					_lowerCamelCase    (      lowerCamelCase_					:							Any	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      []
     UpperCAmelCase_							:   Union[str, Any]   =      fairseq_model.state_dict()

     UpperCAmelCase_							:   Optional[Any]   =      hf_model.sew_d.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(
                   lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     hf_model.config.feat_extract_norm == 'group'	,     )
               UpperCAmelCase_							:   Any   =      True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_							:   Optional[Any]   =      '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]:
                         UpperCAmelCase_							:   int   =      True
                         if "*" in mapped_key:
                              UpperCAmelCase_							:   str   =      name.split(lowerCamelCase_   )[0].split('.'   )[-2]
                              if not layer_index.isnumeric():
                                   continue
                              UpperCAmelCase_							:   str   =      mapped_key.replace('*'	,     lowerCamelCase_   )
                         if "weight_g" in name:
                              UpperCAmelCase_							:   Dict   =      'weight_g'
                         elif "weight_v" in name:
                              UpperCAmelCase_							:   Optional[int]   =      'weight_v'
                         elif "weight" in name:
                              UpperCAmelCase_							:   Optional[int]   =      'weight'
                         elif "bias" in name:
                              UpperCAmelCase_							:   List[Any]   =      'bias'
                         else:
                              UpperCAmelCase_							:   Any   =      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 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_							:   int   =      full_name.split('conv_layers.'   )[-1]
     UpperCAmelCase_							:   int   =      name.split('.'   )
     UpperCAmelCase_							:   int   =      int(items[0]   )
     UpperCAmelCase_							:   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[Any]   =      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_							:   Any   =      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_							:   List[Any]   =      value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
     else:
          unused_weights.append(lowerCamelCase_   )


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							str   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      SEWDConfig()
     if is_finetuned:
          UpperCAmelCase_							:   List[Any]   =      model.wav_encoder.wav_model.cfg
     else:
          UpperCAmelCase_							:   str   =      model.cfg

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

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

     UpperCAmelCase_							:   Any   =      'Wav2Vec2FeatureExtractor'
     UpperCAmelCase_							:   Union[str, Any]   =      'Wav2Vec2CTCTokenizer'

     return config


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							int=None	,     lowerCamelCase_					:							int=None	,     lowerCamelCase_					:							Dict=True   ):



     """simple docstring"""





     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_							:   Any   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

     if config_path is not None:
          UpperCAmelCase_							:   str   =      SEWDConfig.from_pretrained(lowerCamelCase_   )
     else:
          UpperCAmelCase_							:   Optional[Any]   =      convert_config(model[0]	,     lowerCamelCase_   )
     UpperCAmelCase_							:   List[Any]   =      model[0].eval()

     UpperCAmelCase_							:   Optional[int]   =      True if config.feat_extract_norm == 'layer' else False
     UpperCAmelCase_							:   Dict   =      WavaVecaFeatureExtractor(
         feature_size=1	,     sampling_rate=16000	,     padding_value=0	,     do_normalize=lowerCamelCase_	,     return_attention_mask=lowerCamelCase_	,     )

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_							:   Union[str, Any]   =      Dictionary.load(lowerCamelCase_   )

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

          UpperCAmelCase_							:   Optional[int]   =      SEWDForCTC(lowerCamelCase_   )
     else:
          UpperCAmelCase_							:   List[Any]   =      SEWDModel(lowerCamelCase_   )
          feature_extractor.save_pretrained(lowerCamelCase_   )

     recursively_load_weights(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

     hf_model.save_pretrained(lowerCamelCase_   )


if __name__ == "__main__":
 snake_case__     :			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'''
 )
 snake_case__     :			Any      			=  parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






