




'''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()
a    :							Any										=    logging.get_logger(__name__)

a    :							int										=    {
    '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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					for attribute in key.split('''.'''      ):
										snake_case_		     =							getattr(__UpperCAmelCase, __UpperCAmelCase      )

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

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


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							fairseq_model.state_dict()

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

					for name, value in fairseq_dict.items():
										snake_case_		     =							False
										if "conv_layers" in name:
															load_conv_layer(
															    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, hf_model.config.feat_extract_norm == '''group''', )
															snake_case_		     =							True
										else:
															for key, mapped_key in MAPPING.items():
																				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):
																									snake_case_		     =							True
																									if "*" in mapped_key:
																														snake_case_		     =							name.split(__UpperCAmelCase      )[0].split('''.'''      )[-2]
																														snake_case_		     =							mapped_key.replace('''*''', __UpperCAmelCase      )
																									if "weight_g" in name:
																														snake_case_		     =							'''weight_g'''
																									elif "weight_v" in name:
																														snake_case_		     =							'''weight_v'''
																									elif "weight" in name:
																														snake_case_		     =							'''weight'''
																									elif "bias" in name:
																														snake_case_		     =							'''bias'''
																									else:
																														snake_case_		     =							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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							full_name.split('''conv_layers.'''      )[-1]
					snake_case_		     =							name.split('''.'''      )
					snake_case_		     =							int(items[0]      )
					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."
															)
															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."
															)
															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."
															)
															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."
															)
															snake_case_		     =							value
															logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
					else:
										unused_weights.append(__UpperCAmelCase      )


@torch.no_grad()
def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=None, __UpperCAmelCase=None, __UpperCAmelCase=True      )						->   int:

					'''simple docstring'''

					if config_path is not None:
										snake_case_		     =							HubertConfig.from_pretrained(__UpperCAmelCase      )
					else:
										snake_case_		     =							HubertConfig()

					if is_finetuned:
										if dict_path:
															snake_case_		     =							Dictionary.load(__UpperCAmelCase      )

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

										snake_case_		     =							HubertForCTC(__UpperCAmelCase      )
					else:
										snake_case_		     =							HubertModel(__UpperCAmelCase      )

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

					snake_case_		     =							model[0].eval()

					recursively_load_weights(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					hf_wavavec.save_pretrained(__UpperCAmelCase      )


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    :							Dict										=    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'''

from collections.abc import Generator


def 						__magic_name__       (      )						->   Generator[int, None, None]:

					'''simple docstring'''

					snake_case_     ,snake_case_		     =							0, 1
					while True:
										snake_case_     ,snake_case_		     =							b, a + b
										yield b


def 						__magic_name__       (      __UpperCAmelCase = 1000      )						->   int:

					'''simple docstring'''

					snake_case_		     =							1
					snake_case_		     =							fibonacci_generator()
					while len(str(next(__UpperCAmelCase      )      )      ) < n:
										answer += 1
					return answer + 1


if __name__ == "__main__":
							print(solution(int(str(input()).strip())))

