



"""simple docstring"""
def 				lowerCamelCase_		(UpperCamelCase__	:	int = 30							):
    _UpperCAmelCase							:						List[Any]       							=					exponent + 2
    _UpperCAmelCase							:						Union[str, Any]       							=					(1 + 5**0.5) / 2
    _UpperCAmelCase							:						Optional[Any]       							=					(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

    return int(UpperCamelCase__							)


if __name__ == "__main__":
  print(f"{solution() = }")







"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase					:int  =      logging.get_logger(__name__)

_lowerCAmelCase					:Dict  =      {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}





def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	int							):
    for attribute in key.split('''.'''							):
        _UpperCAmelCase							:						Optional[Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							)

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

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





def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	int							):
    _UpperCAmelCase							:						Dict       							=					[]
    _UpperCAmelCase							:						Optional[int]       							=					fairseq_model.state_dict()

    _UpperCAmelCase							:						Any       							=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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





def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	str							):
    _UpperCAmelCase							:						List[Any]       							=					full_name.split('''conv_layers.'''							)[-1]
    _UpperCAmelCase							:						Any       							=					name.split('''.'''							)
    _UpperCAmelCase							:						Union[str, Any]       							=					int(items[0]							)
    _UpperCAmelCase							:						int       							=					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							:						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.'
            )
            _UpperCAmelCase							:						Union[str, 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							:						Union[str, 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.'
            )
            _UpperCAmelCase							:						int       							=					value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
    else:
        unused_weights.append(UpperCamelCase__							)





def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Any							):
    _UpperCAmelCase							:						Any       							=					SEWDConfig()
    if is_finetuned:
        _UpperCAmelCase							:						str       							=					model.wav_encoder.wav_model.cfg
    else:
        _UpperCAmelCase							:						Optional[Any]       							=					model.cfg

    _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_bias
    _UpperCAmelCase							:						Optional[int]       							=					eval(fs_config.conv_feature_layers							)
    _UpperCAmelCase							:						Dict       							=					[x[0] for x in conv_layers]
    _UpperCAmelCase							:						int       							=					[x[1] for x in conv_layers]
    _UpperCAmelCase							:						Optional[int]       							=					[x[2] for x in conv_layers]
    _UpperCAmelCase							:						int       							=					'''gelu'''
    _UpperCAmelCase							:						int       							=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    _UpperCAmelCase							:						Tuple       							=					0.0
    _UpperCAmelCase							:						List[Any]       							=					fs_config.activation_fn.name
    _UpperCAmelCase							:						Optional[Any]       							=					fs_config.encoder_embed_dim
    _UpperCAmelCase							:						Optional[int]       							=					0.02
    _UpperCAmelCase							:						List[Any]       							=					fs_config.encoder_ffn_embed_dim
    _UpperCAmelCase							:						Dict       							=					1E-5
    _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.encoder_layerdrop
    _UpperCAmelCase							:						Tuple       							=					fs_config.encoder_attention_heads
    _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_pos_groups
    _UpperCAmelCase							:						Any       							=					fs_config.conv_pos
    _UpperCAmelCase							:						Optional[Any]       							=					len(UpperCamelCase__							)
    _UpperCAmelCase							:						Any       							=					fs_config.encoder_layers
    _UpperCAmelCase							:						Dict       							=					fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    _UpperCAmelCase							:						int       							=					fs_config.max_position_embeddings
    _UpperCAmelCase							:						Any       							=					fs_config.position_buckets
    _UpperCAmelCase							:						Optional[int]       							=					fs_config.share_att_key
    _UpperCAmelCase							:						Dict       							=					fs_config.relative_attention
    _UpperCAmelCase							:						List[Any]       							=					fs_config.position_biased_input
    _UpperCAmelCase							:						str       							=					tuple(fs_config.pos_att_type.split('''|'''							)							)
    _UpperCAmelCase							:						List[Any]       							=					fs_config.norm_rel_ebd

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        _UpperCAmelCase							:						Any       							=					model.cfg
        _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.final_dropout
        _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.layerdrop
    _UpperCAmelCase							:						str       							=					fs_config.activation_dropout
    _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    _UpperCAmelCase							:						List[Any]       							=					fs_config.attention_dropout
    _UpperCAmelCase							:						List[Any]       							=					fs_config.dropout_input
    _UpperCAmelCase							:						str       							=					fs_config.dropout
    _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_channel_length
    _UpperCAmelCase							:						Tuple       							=					fs_config.mask_channel_prob
    _UpperCAmelCase							:						List[str]       							=					fs_config.mask_length
    _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.mask_prob

    _UpperCAmelCase							:						int       							=					'''Wav2Vec2FeatureExtractor'''
    _UpperCAmelCase							:						str       							=					'''Wav2Vec2CTCTokenizer'''

    return config





@torch.no_grad()
def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	Any=None  , UpperCamelCase__	:	Union[str, Any]=True							):

    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							:						Tuple       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

    if config_path is not None:
        _UpperCAmelCase							:						List[str]       							=					SEWDConfig.from_pretrained(UpperCamelCase__							)
    else:
        _UpperCAmelCase							:						Optional[Any]       							=					convert_config(model[0]  , UpperCamelCase__							)
    _UpperCAmelCase							:						Dict       							=					model[0].eval()

    _UpperCAmelCase							:						List[str]       							=					True if config.feat_extract_norm == '''layer''' else False
    _UpperCAmelCase							:						Optional[int]       							=					WavaVecaFeatureExtractor(
        feature_size=1  , sampling_rate=1_6000  , padding_value=0  , do_normalize=UpperCamelCase__  , return_attention_mask=UpperCamelCase__  , )

    if is_finetuned:
        if dict_path:
            _UpperCAmelCase							:						List[str]       							=					Dictionary.load(UpperCamelCase__							)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.pad_index
            _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.bos_index
            _UpperCAmelCase							:						int       							=					target_dict.pad_index
            _UpperCAmelCase							:						str       							=					target_dict.bos_index
            _UpperCAmelCase							:						Tuple       							=					target_dict.eos_index
            _UpperCAmelCase							:						List[str]       							=					len(target_dict.symbols							)
            _UpperCAmelCase							:						Optional[Any]       							=					os.path.join(UpperCamelCase__  , '''vocab.json'''							)
            if not os.path.isdir(UpperCamelCase__							):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase__							)							)
                return
            os.makedirs(UpperCamelCase__  , exist_ok=UpperCamelCase__							)
            with open(UpperCamelCase__  , '''w'''  , encoding='''utf-8'''							) as vocab_handle:
                json.dump(target_dict.indices  , UpperCamelCase__							)
            _UpperCAmelCase							:						Any       							=					WavaVecaCTCTokenizer(
                UpperCamelCase__  , 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=UpperCamelCase__  , )
            _UpperCAmelCase							:						Dict       							=					WavaVecaProcessor(feature_extractor=UpperCamelCase__  , tokenizer=UpperCamelCase__							)
            processor.save_pretrained(UpperCamelCase__							)

        _UpperCAmelCase							:						Tuple       							=					SEWDForCTC(UpperCamelCase__							)
    else:
        _UpperCAmelCase							:						Any       							=					SEWDModel(UpperCamelCase__							)
        feature_extractor.save_pretrained(UpperCamelCase__							)

    recursively_load_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)

    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase					:Union[str, Any]  =      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(
      '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
  )
  _lowerCAmelCase					:int  =      parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )


