import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       SqueezeBertForMaskedLM,
       SqueezeBertForMultipleChoice,
       SqueezeBertForQuestionAnswering,
       SqueezeBertForSequenceClassification,
       SqueezeBertForTokenClassification,
       SqueezeBertModel,
   )







class __snake_case				(						snake_case_		):




   def __init__(    self			:				Any       ,	_lowercase			:				List[str]       ,	_lowercase			:				Any=13       ,	_lowercase			:				List[Any]=7       ,	_lowercase			:				Dict=True       ,	_lowercase			:				Tuple=True       ,	_lowercase			:				str=False       ,	_lowercase			:				Optional[Any]=True       ,	_lowercase			:				Any=99       ,	_lowercase			:				Optional[int]=32       ,	_lowercase			:				List[Any]=5       ,	_lowercase			:				Dict=4       ,	_lowercase			:				Any=64       ,	_lowercase			:				List[Any]="gelu"       ,	_lowercase			:				int=0.1       ,	_lowercase			:				int=0.1       ,	_lowercase			:				int=5_12       ,	_lowercase			:				str=16       ,	_lowercase			:				Optional[int]=2       ,	_lowercase			:				Any=0.02       ,	_lowercase			:				Union[str, Any]=3       ,	_lowercase			:				List[str]=4       ,	_lowercase			:				Optional[int]=None       ,	_lowercase			:				Optional[int]=2       ,	_lowercase			:				Dict=2       ,	_lowercase			:				Dict=2       ,	_lowercase			:				List[Any]=2       ,	_lowercase			:				Optional[Any]=4       ,	_lowercase			:				int=1       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      seq_length
     SCREAMING_SNAKE_CASE__										=      is_training
     SCREAMING_SNAKE_CASE__										=      use_input_mask
     SCREAMING_SNAKE_CASE__										=      use_token_type_ids
     SCREAMING_SNAKE_CASE__										=      use_labels
     SCREAMING_SNAKE_CASE__										=      vocab_size
     SCREAMING_SNAKE_CASE__										=      hidden_size
     SCREAMING_SNAKE_CASE__										=      num_hidden_layers
     SCREAMING_SNAKE_CASE__										=      num_attention_heads
     SCREAMING_SNAKE_CASE__										=      intermediate_size
     SCREAMING_SNAKE_CASE__										=      hidden_act
     SCREAMING_SNAKE_CASE__										=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__										=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__										=      max_position_embeddings
     SCREAMING_SNAKE_CASE__										=      type_vocab_size
     SCREAMING_SNAKE_CASE__										=      type_sequence_label_size
     SCREAMING_SNAKE_CASE__										=      initializer_range
     SCREAMING_SNAKE_CASE__										=      num_labels
     SCREAMING_SNAKE_CASE__										=      num_choices
     SCREAMING_SNAKE_CASE__										=      scope
     SCREAMING_SNAKE_CASE__										=      q_groups
     SCREAMING_SNAKE_CASE__										=      k_groups
     SCREAMING_SNAKE_CASE__										=      v_groups
     SCREAMING_SNAKE_CASE__										=      post_attention_groups
     SCREAMING_SNAKE_CASE__										=      intermediate_groups
     SCREAMING_SNAKE_CASE__										=      output_groups




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size      )

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

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

     SCREAMING_SNAKE_CASE__										=      self.get_config()

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




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     return SqueezeBertConfig(
         embedding_size=self.hidden_size       ,	vocab_size=self.vocab_size       ,	hidden_size=self.hidden_size       ,	num_hidden_layers=self.num_hidden_layers       ,	num_attention_heads=self.num_attention_heads       ,	intermediate_size=self.intermediate_size       ,	hidden_act=self.hidden_act       ,	attention_probs_dropout_prob=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	initializer_range=self.initializer_range       ,	q_groups=self.q_groups       ,	k_groups=self.k_groups       ,	v_groups=self.v_groups       ,	post_attention_groups=self.post_attention_groups       ,	intermediate_groups=self.intermediate_groups       ,	output_groups=self.output_groups       ,	)




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				str       ,	_lowercase			:				Any       ,	_lowercase			:				Tuple       ,	_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SqueezeBertModel(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      model(_lowercase      )
     self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)      )




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				Dict       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SqueezeBertForMaskedLM(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	labels=_lowercase      )
     self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)      )




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				str       ,	_lowercase			:				Tuple       ,	_lowercase			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SqueezeBertForQuestionAnswering(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(
         _lowercase       ,	attention_mask=_lowercase       ,	start_positions=_lowercase       ,	end_positions=_lowercase      )
     self.parent.assertEqual(result.start_logits.shape       ,	(self.batch_size, self.seq_length)      )
     self.parent.assertEqual(result.end_logits.shape       ,	(self.batch_size, self.seq_length)      )




   def 	__a						(    self			:				Dict       ,	_lowercase			:				Tuple       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Dict       ,	_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.num_labels
     SCREAMING_SNAKE_CASE__										=      SqueezeBertForSequenceClassification(_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	labels=_lowercase      )
     self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				str       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Optional[int]       ,	_lowercase			:				Any       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.num_labels
     SCREAMING_SNAKE_CASE__										=      SqueezeBertForTokenClassification(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()

     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	labels=_lowercase      )
     self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels)      )




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Any       ,	_lowercase			:				List[str]       ,	_lowercase			:				int       ,	_lowercase			:				Dict       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.num_choices
     SCREAMING_SNAKE_CASE__										=      SqueezeBertForMultipleChoice(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      input_ids.unsqueeze(1      ).expand(-1       ,	self.num_choices       ,	-1      ).contiguous()
     SCREAMING_SNAKE_CASE__										=      input_mask.unsqueeze(1      ).expand(-1       ,	self.num_choices       ,	-1      ).contiguous()
     SCREAMING_SNAKE_CASE__										=      model(
         _lowercase       ,	attention_mask=_lowercase       ,	labels=_lowercase       ,	)
     self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_choices)      )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.prepare_config_and_inputs()
     ((SCREAMING_SNAKE_CASE__)							,     (SCREAMING_SNAKE_CASE__)							,     (SCREAMING_SNAKE_CASE__)							,     (SCREAMING_SNAKE_CASE__)							,     (SCREAMING_SNAKE_CASE__)							,     (SCREAMING_SNAKE_CASE__))										=      config_and_inputs
     SCREAMING_SNAKE_CASE__										=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
     return config, inputs_dict







@require_torch
class __snake_case				(						snake_case_   ,		snake_case_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							(
       (
           SqueezeBertModel,
           SqueezeBertForMaskedLM,
           SqueezeBertForMultipleChoice,
           SqueezeBertForQuestionAnswering,
           SqueezeBertForSequenceClassification,
           SqueezeBertForTokenClassification,
       )
       if is_torch_available()
       else None
   )
   lowerCAmelCase_														=							(
       {
           "feature-extraction": SqueezeBertModel,
           "fill-mask": SqueezeBertForMaskedLM,
           "question-answering": SqueezeBertForQuestionAnswering,
           "text-classification": SqueezeBertForSequenceClassification,
           "token-classification": SqueezeBertForTokenClassification,
           "zero-shot": SqueezeBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SqueezeBertModelTester(self      )
     SCREAMING_SNAKE_CASE__										=      ConfigTester(self       ,	config_class=_lowercase       ,	dim=37      )




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



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_squeezebert_model(*_lowercase      )




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_squeezebert_for_masked_lm(*_lowercase      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_squeezebert_for_question_answering(*_lowercase      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_lowercase      )




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_squeezebert_for_token_classification(*_lowercase      )




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_lowercase      )




   @slow
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE__										=      SqueezeBertModel.from_pretrained(_lowercase      )
       self.assertIsNotNone(_lowercase      )







@require_sentencepiece
@require_tokenizers
@require_torch
class __snake_case				(						unittest.TestCase		):




   @slow
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""      )

     SCREAMING_SNAKE_CASE__										=      torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]      )
     SCREAMING_SNAKE_CASE__										=      model(_lowercase      )[0]
     SCREAMING_SNAKE_CASE__										=      torch.Size((1, 3)      )
     self.assertEqual(output.shape       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.tensor([[0.64_01, -0.03_49, -0.60_41]]      )
     self.assertTrue(torch.allclose(_lowercase       ,	_lowercase       ,	atol=1E-4      )      )







'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase_					= {
    """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""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    int					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    List[Any]    )						->Union[str, Any]:
    for attribute in key.split("""."""    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       __lowerCamelCase    )

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

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





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[Any]    )						->Any:
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			fairseq_model.state_dict()

    _SCREAMING_SNAKE_CASE									=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _SCREAMING_SNAKE_CASE									=			False
        if "conv_layers" in name:
            load_conv_layer(
                __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       hf_model.config.feat_extract_norm == """group"""					,       )
            _SCREAMING_SNAKE_CASE									=			True
        else:
            for key, mapped_key in MAPPING.items():
                _SCREAMING_SNAKE_CASE									=			"""hubert.""" + 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] and not is_finetuned):
                    _SCREAMING_SNAKE_CASE									=			True
                    if "*" in mapped_key:
                        _SCREAMING_SNAKE_CASE									=			name.split(__lowerCamelCase    )[0].split("""."""    )[-2]
                        _SCREAMING_SNAKE_CASE									=			mapped_key.replace("""*"""					,       __lowerCamelCase    )
                    if "weight_g" in name:
                        _SCREAMING_SNAKE_CASE									=			"""weight_g"""
                    elif "weight_v" in name:
                        _SCREAMING_SNAKE_CASE									=			"""weight_v"""
                    elif "weight" in name:
                        _SCREAMING_SNAKE_CASE									=			"""weight"""
                    elif "bias" in name:
                        _SCREAMING_SNAKE_CASE									=			"""bias"""
                    else:
                        _SCREAMING_SNAKE_CASE									=			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 :    Dict					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Union[str, Any]    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			full_name.split("""conv_layers."""    )[-1]
    _SCREAMING_SNAKE_CASE									=			name.split("""."""    )
    _SCREAMING_SNAKE_CASE									=			int(items[0]    )
    _SCREAMING_SNAKE_CASE									=			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.'
            )
            _SCREAMING_SNAKE_CASE									=			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.'
            )
            _SCREAMING_SNAKE_CASE									=			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."
            )
            _SCREAMING_SNAKE_CASE									=			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.'
            )
            _SCREAMING_SNAKE_CASE									=			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 :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    List[Any]=None					,       __lowerCamelCase :    Optional[int]=None					,       __lowerCamelCase :    Union[str, Any]=True    )						->Optional[int]:
    if config_path is not None:
        _SCREAMING_SNAKE_CASE									=			HubertConfig.from_pretrained(__lowerCamelCase    )
    else:
        _SCREAMING_SNAKE_CASE									=			HubertConfig()

    if is_finetuned:
        if dict_path:
            _SCREAMING_SNAKE_CASE									=			Dictionary.load(__lowerCamelCase    )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _SCREAMING_SNAKE_CASE									=			target_dict.pad_index
            _SCREAMING_SNAKE_CASE									=			target_dict.bos_index
            _SCREAMING_SNAKE_CASE									=			target_dict.eos_index
            _SCREAMING_SNAKE_CASE									=			len(target_dict.symbols    )
            _SCREAMING_SNAKE_CASE									=			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    )
            _SCREAMING_SNAKE_CASE									=			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					,       )
            _SCREAMING_SNAKE_CASE									=			True if config.feat_extract_norm == """layer""" else False
            _SCREAMING_SNAKE_CASE									=			WavaVecaFeatureExtractor(
                feature_size=1					,       sampling_rate=1_6000					,       padding_value=0					,       do_normalize=__lowerCamelCase					,       return_attention_mask=__lowerCamelCase					,       )
            _SCREAMING_SNAKE_CASE									=			WavaVecaProcessor(feature_extractor=__lowerCamelCase					,       tokenizer=__lowerCamelCase    )
            processor.save_pretrained(__lowerCamelCase    )

        _SCREAMING_SNAKE_CASE									=			HubertForCTC(__lowerCamelCase    )
    else:
        _SCREAMING_SNAKE_CASE									=			HubertModel(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			model[0].eval()

    recursively_load_weights(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    hf_wavavec.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
      lowercase_					= 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"""
      )
      lowercase_					= parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



