





'''simple docstring'''

from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


lowercase_					= logging.get_logger(__name__)


lowercase_					= OrderedDict(
    [
        # Base model mapping
        ("""albert""", """FlaxAlbertModel"""),
        ("""bart""", """FlaxBartModel"""),
        ("""beit""", """FlaxBeitModel"""),
        ("""bert""", """FlaxBertModel"""),
        ("""big_bird""", """FlaxBigBirdModel"""),
        ("""blenderbot""", """FlaxBlenderbotModel"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
        ("""clip""", """FlaxCLIPModel"""),
        ("""distilbert""", """FlaxDistilBertModel"""),
        ("""electra""", """FlaxElectraModel"""),
        ("""gpt-sw3""", """FlaxGPT2Model"""),
        ("""gpt2""", """FlaxGPT2Model"""),
        ("""gpt_neo""", """FlaxGPTNeoModel"""),
        ("""gptj""", """FlaxGPTJModel"""),
        ("""longt5""", """FlaxLongT5Model"""),
        ("""marian""", """FlaxMarianModel"""),
        ("""mbart""", """FlaxMBartModel"""),
        ("""mt5""", """FlaxMT5Model"""),
        ("""opt""", """FlaxOPTModel"""),
        ("""pegasus""", """FlaxPegasusModel"""),
        ("""regnet""", """FlaxRegNetModel"""),
        ("""resnet""", """FlaxResNetModel"""),
        ("""roberta""", """FlaxRobertaModel"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
        ("""roformer""", """FlaxRoFormerModel"""),
        ("""t5""", """FlaxT5Model"""),
        ("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
        ("""vit""", """FlaxViTModel"""),
        ("""wav2vec2""", """FlaxWav2Vec2Model"""),
        ("""whisper""", """FlaxWhisperModel"""),
        ("""xglm""", """FlaxXGLMModel"""),
        ("""xlm-roberta""", """FlaxXLMRobertaModel"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for pre-training mapping
        ("""albert""", """FlaxAlbertForPreTraining"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForPreTraining"""),
        ("""big_bird""", """FlaxBigBirdForPreTraining"""),
        ("""electra""", """FlaxElectraForPreTraining"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
        ("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Masked LM mapping
        ("""albert""", """FlaxAlbertForMaskedLM"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForMaskedLM"""),
        ("""big_bird""", """FlaxBigBirdForMaskedLM"""),
        ("""distilbert""", """FlaxDistilBertForMaskedLM"""),
        ("""electra""", """FlaxElectraForMaskedLM"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
        ("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""marian""", """FlaxMarianMTModel"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Causal LM mapping
        ("""bart""", """FlaxBartForCausalLM"""),
        ("""bert""", """FlaxBertForCausalLM"""),
        ("""big_bird""", """FlaxBigBirdForCausalLM"""),
        ("""electra""", """FlaxElectraForCausalLM"""),
        ("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
        ("""gpt2""", """FlaxGPT2LMHeadModel"""),
        ("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
        ("""gptj""", """FlaxGPTJForCausalLM"""),
        ("""opt""", """FlaxOPTForCausalLM"""),
        ("""roberta""", """FlaxRobertaForCausalLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
        ("""xglm""", """FlaxXGLMForCausalLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("""albert""", """FlaxAlbertForSequenceClassification"""),
        ("""bart""", """FlaxBartForSequenceClassification"""),
        ("""bert""", """FlaxBertForSequenceClassification"""),
        ("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
        ("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
        ("""electra""", """FlaxElectraForSequenceClassification"""),
        ("""mbart""", """FlaxMBartForSequenceClassification"""),
        ("""roberta""", """FlaxRobertaForSequenceClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
        ("""roformer""", """FlaxRoFormerForSequenceClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Question Answering mapping
        ("""albert""", """FlaxAlbertForQuestionAnswering"""),
        ("""bart""", """FlaxBartForQuestionAnswering"""),
        ("""bert""", """FlaxBertForQuestionAnswering"""),
        ("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
        ("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
        ("""electra""", """FlaxElectraForQuestionAnswering"""),
        ("""mbart""", """FlaxMBartForQuestionAnswering"""),
        ("""roberta""", """FlaxRobertaForQuestionAnswering"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
        ("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Token Classification mapping
        ("""albert""", """FlaxAlbertForTokenClassification"""),
        ("""bert""", """FlaxBertForTokenClassification"""),
        ("""big_bird""", """FlaxBigBirdForTokenClassification"""),
        ("""distilbert""", """FlaxDistilBertForTokenClassification"""),
        ("""electra""", """FlaxElectraForTokenClassification"""),
        ("""roberta""", """FlaxRobertaForTokenClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
        ("""roformer""", """FlaxRoFormerForTokenClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
    ]
)

lowercase_					= OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("""albert""", """FlaxAlbertForMultipleChoice"""),
        ("""bert""", """FlaxBertForMultipleChoice"""),
        ("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
        ("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
        ("""electra""", """FlaxElectraForMultipleChoice"""),
        ("""roberta""", """FlaxRobertaForMultipleChoice"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
        ("""roformer""", """FlaxRoFormerForMultipleChoice"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

lowercase_					= OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowercase_					= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_MAPPING




lowercase_					= auto_class_update(FlaxAutoModel)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_PRETRAINING_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_MASKED_LM_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




lowercase_					= auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")


class 					a_     (				_BaseAutoModelClass       ):

    '''simple docstring'''
    UpperCamelCase     = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




lowercase_					= auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)










'''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
      )



