import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__     						=			get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class     a_			(						SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= ReformerTokenizer
 UpperCAmelCase_     			= ReformerTokenizerFast
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= True




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        super().setUp()

        lowerCAmelCase__					     = ReformerTokenizer(lowercase__      ,   keep_accents=lowercase__)
        tokenizer.save_pretrained(self.tmpdirname)




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = '<s>'
        lowerCAmelCase__					     = 1

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




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]      ,   '<unk>')
        self.assertEqual(vocab_keys[1]      ,   '<s>')
        self.assertEqual(vocab_keys[-1]      ,   'j')
        self.assertEqual(len(lowercase__)      ,   1_000)




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        self.assertEqual(self.get_tokenizer().vocab_size      ,   1_000)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        if not self.test_rust_tokenizer:
               return

        lowerCAmelCase__					     = self.get_tokenizer()
        lowerCAmelCase__					     = self.get_rust_tokenizer()

        lowerCAmelCase__					     = 'I was born in 92000, and this is falsé.'

        lowerCAmelCase__					     = tokenizer.tokenize(lowercase__)
        lowerCAmelCase__					     = rust_tokenizer.tokenize(lowercase__)
        self.assertListEqual(lowercase__      ,   lowercase__)

        lowerCAmelCase__					     = tokenizer.encode(lowercase__      ,   add_special_tokens=lowercase__)
        lowerCAmelCase__					     = rust_tokenizer.encode(lowercase__      ,   add_special_tokens=lowercase__)
        self.assertListEqual(lowercase__      ,   lowercase__)

        lowerCAmelCase__					     = self.get_rust_tokenizer()
        lowerCAmelCase__					     = tokenizer.encode(lowercase__)
        lowerCAmelCase__					     = rust_tokenizer.encode(lowercase__)
        self.assertListEqual(lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Optional[Any]=15):







        '''simple docstring'''



        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                      lowerCAmelCase__					     = self.rust_tokenizer_class.from_pretrained(lowercase__      ,   **lowercase__)

                      # Simple input
                      lowerCAmelCase__					     = 'This is a simple input'
                      lowerCAmelCase__					     = ['This is a simple input 1', 'This is a simple input 2']
                      lowerCAmelCase__					     = ('This is a simple input', 'This is a pair')
                      lowerCAmelCase__					     = [
                          ('This is a simple input 1', 'This is a simple input 2'),
                          ('This is a simple pair 1', 'This is a simple pair 2'),
                      ]

                      # Simple input tests
                      self.assertRaises(lowercase__      ,   tokenizer_r.encode      ,   lowercase__      ,   max_length=lowercase__      ,   padding='max_length')

                      # Simple input
                      self.assertRaises(lowercase__      ,   tokenizer_r.encode_plus      ,   lowercase__      ,   max_length=lowercase__      ,   padding='max_length')

                      # Simple input
                      self.assertRaises(
                          lowercase__      ,   tokenizer_r.batch_encode_plus      ,   lowercase__      ,   max_length=lowercase__      ,   padding='max_length'      ,   )

                      # Pair input
                      self.assertRaises(lowercase__      ,   tokenizer_r.encode      ,   lowercase__      ,   max_length=lowercase__      ,   padding='max_length')

                      # Pair input
                      self.assertRaises(lowercase__      ,   tokenizer_r.encode_plus      ,   lowercase__      ,   max_length=lowercase__      ,   padding='max_length')

                      # Pair input
                      self.assertRaises(
                          lowercase__      ,   tokenizer_r.batch_encode_plus      ,   lowercase__      ,   max_length=lowercase__      ,   padding='max_length'      ,   )




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = ReformerTokenizer(lowercase__      ,   keep_accents=lowercase__)

        lowerCAmelCase__					     = tokenizer.tokenize('This is a test')
        self.assertListEqual(lowercase__      ,   ['▁This', '▁is', '▁a', '▁t', 'est'])

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowercase__)      ,   [285, 46, 10, 170, 382]      ,   )

        lowerCAmelCase__					     = tokenizer.tokenize('I was born in 92000, and this is falsé.')
        self.assertListEqual(
            lowercase__      ,   [
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '9',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                'é',
                '.',
            ]      ,   )
        lowerCAmelCase__					     = tokenizer.convert_tokens_to_ids(lowercase__)
        self.assertListEqual(
            lowercase__      ,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,   )

        lowerCAmelCase__					     = tokenizer.convert_ids_to_tokens(lowercase__)
        self.assertListEqual(
            lowercase__      ,   [
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '<unk>',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                '<unk>',
                '.',
            ]      ,   )




 @cached_property
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')




 @slow
 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = 'Hello World!'
        lowerCAmelCase__					     = [126, 32, 262, 152, 38, 72, 287]

        self.assertListEqual(lowercase__      ,   self.big_tokenizer.encode(lowercase__))




 @slow
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = (
            'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
            ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
        )
        lowerCAmelCase__					     = [
            108,
            265,
            24,
            111,
            4,
            258,
            156,
            35,
            28,
            275,
            3,
            259,
            297,
            260,
            84,
            4,
            35,
            110,
            44,
            8,
            259,
            91,
            268,
            21,
            11,
            209,
            274,
            109,
            266,
            277,
            117,
            86,
            93,
            315,
            258,
            278,
            258,
            277,
            258,
            0,
            258,
            288,
            258,
            319,
            258,
            0,
            258,
            0,
            258,
            0,
            258,
            0,
            258,
            287,
            258,
            315,
            258,
            289,
            258,
            278,
            99,
            269,
            266,
            262,
            8,
            259,
            241,
            4,
            217,
            230,
            268,
            266,
            55,
            168,
            106,
            75,
            193,
            266,
            223,
            27,
            49,
            26,
            282,
            25,
            264,
            299,
            19,
            26,
            0,
            258,
            277,
            117,
            86,
            93,
            176,
            183,
            270,
            11,
            262,
            42,
            61,
            265,
        ]

        self.assertListEqual(lowercase__      ,   self.big_tokenizer.encode(lowercase__))




 @require_torch
 @slow
 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        import torch

        from transformers import ReformerConfig, ReformerModel

        # Build sequence
        lowerCAmelCase__					     = list(self.big_tokenizer.get_vocab().keys())[:10]
        lowerCAmelCase__					     = ' '.join(lowercase__)
        lowerCAmelCase__					     = self.big_tokenizer.encode_plus(lowercase__      ,   return_tensors='pt')
        lowerCAmelCase__					     = self.big_tokenizer.batch_encode_plus([sequence, sequence]      ,   return_tensors='pt')

        lowerCAmelCase__					     = ReformerConfig()
        # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
        lowerCAmelCase__					     = encoded_sequence['input_ids'].shape
        lowerCAmelCase__					     = ReformerModel(lowercase__)

        # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

        with torch.no_grad():
               model(**lowercase__)
               model(**lowercase__)




 @slow
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[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]]}  # noqa: E501
        # fmt: on

        # This tokenizer does not know some characters like ")".
        # That is the reason why we use very simple texts here.
        # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
        lowerCAmelCase__					     = [
            'This is a very simple sentence.',
            'The quick brown fox jumps over the lazy dog.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase__      ,   model_name='google/reformer-crime-and-punishment'      ,   revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'      ,   padding=lowercase__      ,   sequences=lowercase__      ,   )

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase__     						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = {}
       with open(lowerCAmelCase__     ,  'r'     ) as file:
              for line_number, line in enumerate(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = line.strip()
                     if line:
                            lowerCAmelCase__					     = line.split()
                            lowerCAmelCase__					     = line_number
                            lowerCAmelCase__					     = words[0]
                            lowerCAmelCase__					     = value
       return result






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for attribute in key.split('.'     ):
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )

       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     ).shape
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = hf_pointer
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = shape_pointer.shape

              # let's reduce dimension
              lowerCAmelCase__					     = value[0]
       else:
              lowerCAmelCase__					     = hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  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":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_g":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_v":
              lowerCAmelCase__					     = value
       elif weight_type == "bias":
              lowerCAmelCase__					     = value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = value
       else:
              lowerCAmelCase__					     = value

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = '.'.join([key, weight_type]     )
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = '.'.join([key, hf_param_name]     )
       else:
              lowerCAmelCase__					     = key

       lowerCAmelCase__					     = value if 'lm_head' in full_key else value[0]


lowerCAmelCase__     						=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ):
       lowerCAmelCase__					     = False
       for key, mapped_key in MAPPING.items():
              lowerCAmelCase__					     = 'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
              if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
                     lowerCAmelCase__					     = True
                     if "*" in mapped_key:
                            lowerCAmelCase__					     = name.split(lowerCAmelCase__     )[0].split('.'     )[-2]
                            lowerCAmelCase__					     = mapped_key.replace('*'     ,  lowerCAmelCase__     )
                     if "weight_g" in name:
                            lowerCAmelCase__					     = 'weight_g'
                     elif "weight_v" in name:
                            lowerCAmelCase__					     = 'weight_v'
                     elif "bias" in name:
                            lowerCAmelCase__					     = 'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            lowerCAmelCase__					     = 'weight'
                     else:
                            lowerCAmelCase__					     = None
                     if hf_dict is not None:
                            rename_dict(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     else:
                            set_recursively(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     return is_used
       return is_used






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = []
       lowerCAmelCase__					     = fairseq_model.state_dict()

       lowerCAmelCase__					     = hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCAmelCase__					     = False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
                     lowerCAmelCase__					     = True
              else:
                     lowerCAmelCase__					     = load_wavaveca_layer(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
              if not is_used:
                     unused_weights.append(lowerCAmelCase__     )

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = full_name.split('conv_layers.'     )[-1]
       lowerCAmelCase__					     = name.split('.'     )
       lowerCAmelCase__					     = int(items[0]     )
       lowerCAmelCase__					     = int(items[1]     )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""     )
                     lowerCAmelCase__					     = 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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F"""{full_name} has size {value.shape}, but"""
                                F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
       else:
              unused_weights.append(lowerCAmelCase__     )






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=True     ,  lowerCAmelCase__=False     ):
       if config_path is not None:
              lowerCAmelCase__					     = WavaVecaConfig.from_pretrained(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaConfig()

       if is_seq_class:
              lowerCAmelCase__					     = read_txt_into_dict(lowerCAmelCase__     )
              lowerCAmelCase__					     = idalabel
              lowerCAmelCase__					     = WavaVecaForSequenceClassification(lowerCAmelCase__     )
              lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                  feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
              feature_extractor.save_pretrained(lowerCAmelCase__     )

       elif is_finetuned:
              if dict_path:
                     lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCAmelCase__					     = target_dict.pad_index
                     lowerCAmelCase__					     = target_dict.bos_index
                     lowerCAmelCase__					     = target_dict.eos_index
                     lowerCAmelCase__					     = len(target_dict.symbols     )
                     lowerCAmelCase__					     = 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__     )
                     lowerCAmelCase__					     = target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     lowerCAmelCase__					     = 0
                     lowerCAmelCase__					     = 1
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as vocab_handle:
                            json.dump(lowerCAmelCase__     ,  lowerCAmelCase__     )
                     lowerCAmelCase__					     = 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__     ,  )
                     lowerCAmelCase__					     = True if config.feat_extract_norm == 'layer' else False
                     lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                         feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = WavaVecaProcessor(feature_extractor=lowerCAmelCase__     ,  tokenizer=lowerCAmelCase__     )
                     processor.save_pretrained(lowerCAmelCase__     )

              lowerCAmelCase__					     = WavaVecaForCTC(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaForPreTraining(lowerCAmelCase__     )

       if is_finetuned or is_seq_class:
              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]     ,  arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
       else:
              lowerCAmelCase__					     = argparse.Namespace(task='audio_pretraining'     )
              lowerCAmelCase__					     = fairseq.tasks.setup_task(lowerCAmelCase__     )

              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,  task=lowerCAmelCase__     )

       lowerCAmelCase__					     = model[0].eval()

       recursively_load_weights(lowerCAmelCase__     ,  lowerCAmelCase__     ,  not is_finetuned     )

       hf_wavavec.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			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(
        '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    parser.add_argument(
        '--is_seq_class',
        action='store_true',
        help='Whether the model to convert is a fine-tuned sequence classification model or not',
    )
    lowerCAmelCase__     						=			parser.parse_args()

    lowerCAmelCase__     						=			not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )
