

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()
SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    "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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  Any )   ->       Tuple:
    for attribute in key.split("." ):
        _UpperCAmelCase       :		Any 		=    getattr(lowerCAmelCase  ,      lowerCAmelCase )

    if weight_type is not None:
        _UpperCAmelCase       :		int 		=    getattr(lowerCAmelCase  ,      lowerCAmelCase ).shape
    else:
        _UpperCAmelCase       :		List[Any] 		=    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       :		Union[str, Any] 		=    value
    elif weight_type == "weight_g":
        _UpperCAmelCase       :		Tuple 		=    value
    elif weight_type == "weight_v":
        _UpperCAmelCase       :		int 		=    value
    elif weight_type == "bias":
        _UpperCAmelCase       :		Any 		=    value
    else:
        _UpperCAmelCase       :		int 		=    value

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  int )   ->       Union[str, Any]:
    _UpperCAmelCase       :		Union[str, Any] 		=    []
    _UpperCAmelCase       :		Dict 		=    fairseq_model.state_dict()

    _UpperCAmelCase       :		str 		=    hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _UpperCAmelCase       :		List[str] 		=    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       :		Dict 		=    "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):
                    _UpperCAmelCase       :		List[str] 		=    True
                    if "*" in mapped_key:
                        _UpperCAmelCase       :		Union[str, Any] 		=    name.split(lowerCAmelCase )[0].split("." )[-2]
                        _UpperCAmelCase       :		Any 		=    mapped_key.replace("*"  ,      lowerCAmelCase )
                    if "weight_g" in name:
                        _UpperCAmelCase       :		List[str] 		=    "weight_g"
                    elif "weight_v" in name:
                        _UpperCAmelCase       :		Union[str, Any] 		=    "weight_v"
                    elif "weight" in name:
                        _UpperCAmelCase       :		Any 		=    "weight"
                    elif "bias" in name:
                        _UpperCAmelCase       :		Any 		=    "bias"
                    else:
                        _UpperCAmelCase       :		Tuple 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Optional[int] )   ->       Any:
    _UpperCAmelCase       :		Dict 		=    full_name.split("conv_layers." )[-1]
    _UpperCAmelCase       :		Union[str, Any] 		=    name.split("." )
    _UpperCAmelCase       :		int 		=    int(items[0] )
    _UpperCAmelCase       :		List[str] 		=    int(items[1] )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
            )
            _UpperCAmelCase       :		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       :		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       :		Optional[int] 		=    value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F'{full_name} has size {value.shape}, but'
                F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
            )
            _UpperCAmelCase       :		Optional[Any] 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Tuple=None  ,      lowerCAmelCase:  List[Any]=None  ,      lowerCAmelCase:  Any=True )   ->       Union[str, Any]:
    if config_path is not None:
        _UpperCAmelCase       :		Dict 		=    HubertConfig.from_pretrained(lowerCAmelCase )
    else:
        _UpperCAmelCase       :		str 		=    HubertConfig()

    if is_finetuned:
        if dict_path:
            _UpperCAmelCase       :		int 		=    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       :		Optional[Any] 		=    target_dict.bos_index
            _UpperCAmelCase       :		Any 		=    target_dict.eos_index
            _UpperCAmelCase       :		Optional[int] 		=    len(target_dict.symbols )
            _UpperCAmelCase       :		Union[str, 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       :		Any 		=    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       :		str 		=    True if config.feat_extract_norm == "layer" else False
            _UpperCAmelCase       :		Optional[Any] 		=    WavaVecaFeatureExtractor(
                feature_size=1  ,      sampling_rate=1_6000  ,      padding_value=0  ,      do_normalize=lowerCAmelCase  ,      return_attention_mask=lowerCAmelCase  ,      )
            _UpperCAmelCase       :		Optional[Any] 		=    WavaVecaProcessor(feature_extractor=lowerCAmelCase  ,      tokenizer=lowerCAmelCase )
            processor.save_pretrained(lowerCAmelCase )

        _UpperCAmelCase       :		List[Any] 		=    HubertForCTC(lowerCAmelCase )
    else:
        _UpperCAmelCase       :		Any 		=    HubertModel(lowerCAmelCase )

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

    _UpperCAmelCase       :		Union[str, Any] 		=    model[0].eval()

    recursively_load_weights(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )

    hf_wavavec.save_pretrained(lowerCAmelCase )


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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


