

import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


lowercase_									=   {
    'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
    'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
    'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
    'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
    'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
    'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
    'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
    'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
    'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
    'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}
def      a      (			A__	:   Tuple     )    ->      Any:





      """simple docstring"""



      _lowercase	       =['layers', 'blocks']
      for k in ignore_keys:
            state_dict.pop(A__      ,   A__     )


lowercase_									=   {
    'blocks': 'layers',
    'mlp.0': 'fc1',
    'mlp.2': 'fc2',
    'mlp_ln': 'final_layer_norm',
    '.attn.query': '.self_attn.q_proj',
    '.attn.key': '.self_attn.k_proj',
    '.attn.value': '.self_attn.v_proj',
    '.attn_ln': '.self_attn_layer_norm',
    '.attn.out': '.self_attn.out_proj',
    '.cross_attn.query': '.encoder_attn.q_proj',
    '.cross_attn.key': '.encoder_attn.k_proj',
    '.cross_attn.value': '.encoder_attn.v_proj',
    '.cross_attn_ln': '.encoder_attn_layer_norm',
    '.cross_attn.out': '.encoder_attn.out_proj',
    'decoder.ln.': 'decoder.layer_norm.',
    'encoder.ln.': 'encoder.layer_norm.',
    'token_embedding': 'embed_tokens',
    'encoder.positional_embedding': 'encoder.embed_positions.weight',
    'decoder.positional_embedding': 'decoder.embed_positions.weight',
    'ln_post': 'layer_norm',
}
def      a      (			A__	:   Any     )    ->      Any:





      """simple docstring"""



      _lowercase	       =list(s_dict.keys()     )
      for key in keys:
            _lowercase	       =key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        _lowercase	       =new_key.replace(A__      ,   A__     )

            print(F'''{key} -> {new_key}'''     )

            _lowercase	       =s_dict.pop(A__     )
      return s_dict
def      a      (			A__	:   List[str]     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase       ,       _lowercase	       =emb.weight.shape
      _lowercase	       =nn.Linear(A__      ,   A__      ,   bias=A__     )
      _lowercase	       =emb.weight.data
      return lin_layer
def      a      (			A__	:   str      ,   A__	:   str     )    ->      bytes:





      """simple docstring"""



      os.makedirs(A__      ,   exist_ok=A__     )
      _lowercase	       =os.path.basename(A__     )

      _lowercase	       =url.split('/'     )[-2]
      _lowercase	       =os.path.join(A__      ,   A__     )

      if os.path.exists(A__     ) and not os.path.isfile(A__     ):
            raise RuntimeError(F'''{download_target} exists and is not a regular file'''     )

      if os.path.isfile(A__     ):
            _lowercase	       =open(A__      ,   'rb'     ).read()
            if hashlib.shaaaa(A__     ).hexdigest() == expected_shaaaa:
                  return model_bytes
            else:
                  warnings.warn(F'''{download_target} exists, but the SHA256 checksum does not match; re-downloading the file'''     )

      with urllib.request.urlopen(A__     ) as source, open(A__      ,   'wb'     ) as output:
            with tqdm(
                total=int(source.info().get('Content-Length'     )     )      ,   ncols=80      ,   unit='iB'      ,   unit_scale=A__      ,   unit_divisor=1024     ) as loop:
                  while True:
                        _lowercase	       =source.read(8192     )
                        if not buffer:
                              break

                        output.write(A__     )
                        loop.update(len(A__     )     )

      _lowercase	       =open(A__      ,   'rb'     ).read()
      if hashlib.shaaaa(A__     ).hexdigest() != expected_shaaaa:
            raise RuntimeError(
                'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'     )

      return model_bytes
def      a      (			A__	:   List[Any]      ,   A__	:   Union[str, Any]     )    ->      Union[str, Any]:





      """simple docstring"""



      if ".pt" not in checkpoint_path:
            _lowercase	       =_download(_MODELS[checkpoint_path]     )
      else:
            _lowercase	       =torch.load(A__      ,   map_location='cpu'     )
      _lowercase	       =original_checkpoint['dims']
      _lowercase	       =original_checkpoint['model_state_dict']
      _lowercase	       =state_dict['decoder.token_embedding.weight']
      remove_ignore_keys_(A__     )
      rename_keys(A__     )
      _lowercase	       =True
      _lowercase	       =state_dict['decoder.layers.0.fc1.weight'].shape[0]

      _lowercase	       =WhisperConfig(
          vocab_size=dimensions['n_vocab']      ,   encoder_ffn_dim=A__      ,   decoder_ffn_dim=A__      ,   num_mel_bins=dimensions['n_mels']      ,   d_model=dimensions['n_audio_state']      ,   max_target_positions=dimensions['n_text_ctx']      ,   encoder_layers=dimensions['n_audio_layer']      ,   encoder_attention_heads=dimensions['n_audio_head']      ,   decoder_layers=dimensions['n_text_layer']      ,   decoder_attention_heads=dimensions['n_text_state']      ,   max_source_positions=dimensions['n_audio_ctx']      ,   )

      _lowercase	       =WhisperForConditionalGeneration(A__     )
      _lowercase       ,       _lowercase	       =model.model.load_state_dict(A__      ,   strict=A__     )
      if len(A__     ) > 0 and not set(A__     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
                F''' but all the following weights are missing {missing}'''     )

      if tie_embeds:
            _lowercase	       =make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            _lowercase	       =proj_out_weights

      model.save_pretrained(A__     )


if __name__ == "__main__":
  lowercase_									=   argparse.ArgumentParser()
  # # Required parameters
  parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  lowercase_									=   parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





def      a      (			A__	:   int = 1000000     )    ->      int:





      """simple docstring"""



      _lowercase	       =1
      _lowercase	       =1
      _lowercase	       ={1: 1}

      for inputa in range(2      ,   A__     ):
            _lowercase	       =0
            _lowercase	       =inputa

            while True:
                  if number in counters:
                        counter += counters[number]
                        break
                  if number % 2 == 0:
                        number //= 2
                        counter += 1
                  else:
                        _lowercase	       =(3 * number) + 1
                        counter += 1

            if inputa not in counters:
                  _lowercase	       =counter

            if counter > pre_counter:
                  _lowercase	       =inputa
                  _lowercase	       =counter
      return largest_number


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


