
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()
a__							:	Optional[int]  						=			logging.get_logger(__name__)

a__							:	Any  						=			{
    '''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 							UpperCAmelCase_(  a__      ,		a__      ,		a__      ,		a__      ,		a__					):



    """simple docstring"""



    for attribute in key.split('''.'''					):
        SCREAMING_SNAKE_CASE							:							Any								=      getattr(_A      ,		_A					)

    if weight_type is not None:
        SCREAMING_SNAKE_CASE							:							Dict								=      getattr(_A      ,		_A					).shape
    else:
        SCREAMING_SNAKE_CASE							:							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":
        SCREAMING_SNAKE_CASE							:							str								=      value
    elif weight_type == "weight_g":
        SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      value
    elif weight_type == "weight_v":
        SCREAMING_SNAKE_CASE							:							List[Any]								=      value
    elif weight_type == "bias":
        SCREAMING_SNAKE_CASE							:							Tuple								=      value
    else:
        SCREAMING_SNAKE_CASE							:							Optional[Any]								=      value

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







def 							UpperCAmelCase_(  a__      ,		a__      ,		a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							Dict								=      []
    SCREAMING_SNAKE_CASE							:							Dict								=      fairseq_model.state_dict()

    SCREAMING_SNAKE_CASE							:							Any								=      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      False
        if "conv_layers" in name:
            load_conv_layer(
                _A      ,		_A      ,		_A      ,		_A      ,		hf_model.config.feat_extract_norm == '''group'''      ,		)
            SCREAMING_SNAKE_CASE							:							str								=      True
        else:
            for key, mapped_key in MAPPING.items():
                SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      '''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							:							Dict								=      True
                    if "*" in mapped_key:
                        SCREAMING_SNAKE_CASE							:							List[Any]								=      name.split(_A					)[0].split('''.'''					)[-2]
                        SCREAMING_SNAKE_CASE							:							Optional[Any]								=      mapped_key.replace('''*'''      ,		_A					)
                    if "weight_g" in name:
                        SCREAMING_SNAKE_CASE							:							List[str]								=      '''weight_g'''
                    elif "weight_v" in name:
                        SCREAMING_SNAKE_CASE							:							List[Any]								=      '''weight_v'''
                    elif "weight" in name:
                        SCREAMING_SNAKE_CASE							:							Optional[Any]								=      '''weight'''
                    elif "bias" in name:
                        SCREAMING_SNAKE_CASE							:							Tuple								=      '''bias'''
                    else:
                        SCREAMING_SNAKE_CASE							:							Optional[int]								=      None
                    set_recursively(_A      ,		_A      ,		_A      ,		_A      ,		_A					)
                continue
        if not is_used:
            unused_weights.append(_A					)

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







def 							UpperCAmelCase_(  a__      ,		a__      ,		a__      ,		a__      ,		a__					):



    """simple docstring"""



    SCREAMING_SNAKE_CASE							:							Tuple								=      full_name.split('''conv_layers.'''					)[-1]
    SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      name.split('''.'''					)
    SCREAMING_SNAKE_CASE							:							str								=      int(items[0]					)
    SCREAMING_SNAKE_CASE							:							Any								=      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							:							Tuple								=      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							:							Dict								=      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							:							List[Any]								=      value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
            )
            SCREAMING_SNAKE_CASE							:							Tuple								=      value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
    else:
        unused_weights.append(_A					)







@torch.no_grad()
def 							UpperCAmelCase_(  a__      ,		a__      ,		a__=None      ,		a__=None      ,		a__=True					):



    """simple docstring"""



    if config_path is not None:
        SCREAMING_SNAKE_CASE							:							Optional[int]								=      HubertConfig.from_pretrained(_A					)
    else:
        SCREAMING_SNAKE_CASE							:							int								=      HubertConfig()

    if is_finetuned:
        if dict_path:
            SCREAMING_SNAKE_CASE							:							int								=      Dictionary.load(_A					)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            SCREAMING_SNAKE_CASE							:							str								=      target_dict.pad_index
            SCREAMING_SNAKE_CASE							:							str								=      target_dict.bos_index
            SCREAMING_SNAKE_CASE							:							int								=      target_dict.eos_index
            SCREAMING_SNAKE_CASE							:							Dict								=      len(target_dict.symbols					)
            SCREAMING_SNAKE_CASE							:							str								=      os.path.join(_A      ,		'''vocab.json'''					)
            if not os.path.isdir(_A					):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_A					)					)
                return
            os.makedirs(_A      ,		exist_ok=_A					)
            with open(_A      ,		'''w'''      ,		encoding='''utf-8'''					) as vocab_handle:
                json.dump(target_dict.indices      ,		_A					)
            SCREAMING_SNAKE_CASE							:							List[Any]								=      WavaVecaCTCTokenizer(
                _A      ,		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=_A      ,		)
            SCREAMING_SNAKE_CASE							:							Optional[Any]								=      True if config.feat_extract_norm == '''layer''' else False
            SCREAMING_SNAKE_CASE							:							int								=      WavaVecaFeatureExtractor(
                feature_size=1      ,		sampling_rate=16_000      ,		padding_value=0      ,		do_normalize=_A      ,		return_attention_mask=_A      ,		)
            SCREAMING_SNAKE_CASE							:							str								=      WavaVecaProcessor(feature_extractor=_A      ,		tokenizer=_A					)
            processor.save_pretrained(_A					)

        SCREAMING_SNAKE_CASE							:							List[Any]								=      HubertForCTC(_A					)
    else:
        SCREAMING_SNAKE_CASE							:							Dict								=      HubertModel(_A					)

    if is_finetuned:
        SCREAMING_SNAKE_CASE	,				SCREAMING_SNAKE_CASE	,				SCREAMING_SNAKE_CASE							:							Tuple								=      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							:							List[Any]								=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

    SCREAMING_SNAKE_CASE							:							int								=      model[0].eval()

    recursively_load_weights(_A      ,		_A      ,		_A					)

    hf_wavavec.save_pretrained(_A					)


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

'''simple docstring'''




def 				snake_case__							(			_A:					int      ,   _A:					int )    ->	int:

		'''simple docstring'''


		while a != 0:
				lowerCAmelCase					,     lowerCAmelCase   					=  b % a, a
		return b






def 				snake_case__							(			_A:					int      ,   _A:					int )    ->	int:

		'''simple docstring'''


		if gcd(_A      ,   _A ) != 1:
				lowerCAmelCase   					=  f"mod inverse of {a!r} and {m!r} does not exist"
				raise ValueError(_A )
		lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  1, 0, a
		lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  0, 1, m
		while va != 0:
				lowerCAmelCase   					=  ua // va
				lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
		return ua % m




