






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowerCamelCase     :							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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase     :							Optional[Any]    =[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[Any]:





     for attribute in key.split("."      ):
          UpperCamelCase__      :       List[Any]	    =  getattr(lowercase__ ,   lowercase__      )

     if weight_type is not None:
          UpperCamelCase__      :       Optional[Any]	    =  getattr(lowercase__ ,   lowercase__      ).shape
     else:
          UpperCamelCase__      :       Tuple	    =  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__      :       Dict	    =  value
     elif weight_type == "weight_v":
          UpperCamelCase__      :       List[Any]	    =  value
     elif weight_type == "bias":
          UpperCamelCase__      :       Dict	    =  value
     else:
          UpperCamelCase__      :       List[Any]	    =  value

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Tuple:
     UpperCamelCase__      :       Union[str, Any]	    =  []
     UpperCamelCase__      :       Optional[int]	    =  fairseq_model.state_dict()

     UpperCamelCase__      :       Union[str, Any]	    =  hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     UpperCamelCase__      :       List[Any]	    =  None

     for name, value in fairseq_dict.items():
          UpperCamelCase__      :       List[str]	    =  False
          if "conv_layers" in name:
               load_conv_layer(
                   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   hf_model.config.feat_extract_norm == "group" ,   )
               UpperCamelCase__      :       Any	    =  True
          elif name.split("."      )[0] == "proj":
               UpperCamelCase__      :       List[str]	    =  fairseq_model.proj
               UpperCamelCase__      :       List[str]	    =  True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         UpperCamelCase__      :       Dict	    =  True
                         if "*" in mapped_key:
                              UpperCamelCase__      :       Optional[int]	    =  name.split(lowercase__      )[0].split("."      )[-2]
                              UpperCamelCase__      :       List[Any]	    =  mapped_key.replace("*" ,   lowercase__      )
                         if "weight_g" in name:
                              UpperCamelCase__      :       List[Any]	    =  """weight_g"""
                         elif "weight_v" in name:
                              UpperCamelCase__      :       Any	    =  """weight_v"""
                         elif "bias" in name:
                              UpperCamelCase__      :       Union[str, Any]	    =  """bias"""
                         elif "weight" in name:
                              UpperCamelCase__      :       str	    =  """weight"""
                         else:
                              UpperCamelCase__      :       List[Any]	    =  None
                         set_recursively(lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__      )
                    continue
          if not is_used:
               unused_weights.append(lowercase__      )

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

     return proj_weight







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       List[Any]	    =  full_name.split("conv_layers."      )[-1]
     UpperCamelCase__      :       Optional[Any]	    =  name.split("."      )
     UpperCamelCase__      :       Optional[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__      :       List[str]	    =  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__      :       Optional[int]	    =  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__      :       Any	    =  value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
     else:
          unused_weights.append(lowercase__      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
     UpperCamelCase__      :       Optional[Any]	    =  emb.weight.shape
     UpperCamelCase__      :       Optional[Any]	    =  nn.Linear(lowercase__ ,   lowercase__ ,   bias=lowercase__      )
     UpperCamelCase__      :       Optional[int]	    =  emb.weight.data
     return lin_layer







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[int]:





     with open(lowercase__ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCamelCase__      :       int	    =  f.readlines()
          UpperCamelCase__      :       Dict	    =  [line.split(" "      )[0] for line in lines]

     UpperCamelCase__      :       Optional[Any]	    =  len(lowercase__      )

     UpperCamelCase__      :       Dict	    =  {
         """<s>""": 0,
         """<pad>""": 1,
         """</s>""": 2,
         """<unk>""": 3,
     }

     vocab_dict.update(dict(zip(lowercase__ ,   range(4 ,   num_words + 4      )      )      )      )
     return vocab_dict







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   )			->     Union[str, Any]:
     UpperCamelCase__      :       Tuple	    =  WavaVecaConfig.from_pretrained(lowercase__      )
     UpperCamelCase__      :       List[Any]	    =  SpeechaTextaConfig.from_pretrained(
         lowercase__ ,   vocab_size=lowercase__ ,   decoder_layers=lowercase__ ,   do_stable_layer_norm=lowercase__      )

     UpperCamelCase__      :       Optional[int]	    =  WavaVecaFeatureExtractor(
         feature_size=1 ,   sampling_rate=1_6000 ,   padding_value=0 ,   do_normalize=lowercase__ ,   return_attention_mask=lowercase__ ,   )

     UpperCamelCase__      :       Optional[int]	    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path] ,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     UpperCamelCase__      :       Optional[Any]	    =  model[0].eval()

     # set weights for wav2vec2 encoder
     UpperCamelCase__      :       int	    =  WavaVecaModel(lowercase__      )
     UpperCamelCase__      :       List[Any]	    =  recursively_load_weights_wavaveca(model.encoder ,   lowercase__      )

     UpperCamelCase__      :       Dict	    =  SpeechaTextaForCausalLM(lowercase__      )
     UpperCamelCase__      :       Any	    =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,   strict=lowercase__      )

     # set output linear layer
     unexpected_keys.remove("embed_out"      )
     UpperCamelCase__      :       str	    =  nn.Parameter(model.decoder.embed_out.detach()      )

     # layer norm is init to identity matrix so leaving it is fine
     logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'      )
     logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'      )

     UpperCamelCase__      :       int	    =  SpeechEncoderDecoderModel(encoder=lowercase__ ,   decoder=lowercase__      )
     UpperCamelCase__      :       Dict	    =  False

     # add projection layer
     UpperCamelCase__      :       List[Any]	    =  nn.Parameter(projection_layer.weight      )
     UpperCamelCase__      :       Optional[int]	    =  nn.Parameter(projection_layer.bias      )

     UpperCamelCase__      :       Union[str, Any]	    =  create_vocab_dict(lowercase__      )

     with open(os.path.join(lowercase__ ,   "vocab.json"      ) ,   "w"      ) as fp:
          json.dump(lowercase__ ,   lowercase__      )

     UpperCamelCase__      :       int	    =  SpeechaTextaTokenizer(os.path.join(lowercase__ ,   "vocab.json"      )      )
     tokenizer.save_pretrained(lowercase__      )

     UpperCamelCase__      :       Optional[Any]	    =  hf_wavavec.config.to_dict()
     UpperCamelCase__      :       Union[str, Any]	    =  tokenizer.pad_token_id
     UpperCamelCase__      :       List[str]	    =  tokenizer.bos_token_id
     UpperCamelCase__      :       List[Any]	    =  tokenizer.eos_token_id
     UpperCamelCase__      :       Optional[int]	    =  """speech_to_text_2"""
     UpperCamelCase__      :       Union[str, Any]	    =  """wav2vec2"""

     UpperCamelCase__      :       Any	    =  SpeechEncoderDecoderConfig.from_dict(lowercase__      )

     hf_wavavec.save_pretrained(lowercase__      )
     feature_extractor.save_pretrained(lowercase__      )


if __name__ == "__main__":
 lowerCamelCase     :							Optional[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(
     '''--encoder_config_path''',
     default='''facebook/wav2vec2-large-lv60''',
     type=str,
     help='''Path to hf encoder wav2vec2 checkpoint config''',
 )
 parser.add_argument(
     '''--decoder_config_path''',
     default='''facebook/s2t-small-mustc-en-fr-st''',
     type=str,
     help='''Path to hf decoder s2t checkpoint config''',
 )
 parser.add_argument('''--vocab_size''', default=10224, type=int, help='''Vocab size of decoder''')
 parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

 lowerCamelCase     :							int    =parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       int	    =  1
     for i in range(1 ,   num + 1      ):
          fact *= i
     return fact







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       List[Any]	    =  0
     while number > 0:
          UpperCamelCase__      :       List[Any]	    =  number % 10
          sum_of_digits += last_digit
          UpperCamelCase__      :       int	    =  number // 10  # Removing the last_digit from the given number
     return sum_of_digits







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase = 100      )			->     int:
     UpperCamelCase__      :       Optional[Any]	    =  factorial(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[int]	    =  split_and_add(__lowerCAmelCase      )
     return result


if __name__ == "__main__":
 print(solution(int(input('''Enter the Number: ''').strip())))