
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowercase__       =logging.get_logger(__name__)

lowercase__       ={
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowercase__       =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]




def    __UpperCamelCase     (				lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Optional[Any]  ):
 for attribute in key.split('''.'''  ):
  __a :  Dict						=  getattr(lowerCAmelCase__  ,   lowerCAmelCase__  )

 if weight_type is not None:
  __a :  str						=  getattr(lowerCAmelCase__  ,   lowerCAmelCase__  ).shape
 else:
  __a :  str						=  hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      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":
  __a :  Tuple						=  value
 elif weight_type == "weight_g":
  __a :  List[str]						=  value
 elif weight_type == "weight_v":
  __a :  int						=  value
 elif weight_type == "bias":
  __a :  Optional[Any]						=  value
 else:
  __a :  Tuple						=  value

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




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				Optional[int]  ):
 __a :  int						=  []
 __a :  str						=  fairseq_model.state_dict()

 __a :  List[Any]						=  hf_model.unispeech_sat.feature_extractor

 for name, value in fairseq_dict.items():
  __a :  Any						=  False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   hf_model.config.feat_extract_norm == '''group'''  ,   )
   __a :  List[str]						=  True
  else:
   for key, mapped_key in MAPPING.items():
    __a :  str						=  '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split('''w2v_model.'''  )[-1] == name.split('''.'''  )[0]:
     if "layer_norm_for_extract" in name and (".".join(name.split('''.'''  )[:-1]  ) != key):
      # special case since naming is very similar
      continue
     __a :  int						=  True
     if "*" in mapped_key:
      __a :  Dict						=  name.split(lowerCAmelCase__  )[0].split('''.'''  )[-2]
      __a :  List[str]						=  mapped_key.replace('''*'''  ,   lowerCAmelCase__  )
     if "weight_g" in name:
      __a :  Any						=  '''weight_g'''
     elif "weight_v" in name:
      __a :  str						=  '''weight_v'''
     elif "bias" in name:
      __a :  Optional[Any]						=  '''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __a :  List[Any]						=  '''weight'''
     else:
      __a :  Tuple						=  None
     set_recursively(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
    continue
  if not is_used:
   unused_weights.append(lowerCAmelCase__  )

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




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  full_name.split('''conv_layers.'''  )[-1]
 __a :  Tuple						=  name.split('''.'''  )
 __a :  Union[str, Any]						=  int(items[0]  )
 __a :  List[Any]						=  int(items[1]  )

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."  )
   __a :  Tuple						=  value
   logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."  )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."  )
   __a :  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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."  )
   __a :  Tuple						=  value
   logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        f"{full_name} has size {value.shape}, but"
        f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."  )
   __a :  Union[str, Any]						=  value
   logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
 else:
  unused_weights.append(lowerCAmelCase__  )




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				Union[str, Any]=None  ,   lowerCAmelCase__							:				Optional[Any]=None  ,   lowerCAmelCase__							:				Union[str, Any]=True  ):
 if config_path is not None:
  __a :  List[str]						=  UniSpeechSatConfig.from_pretrained(lowerCAmelCase__  )
 else:
  __a :  Union[str, Any]						=  UniSpeechSatConfig()

 __a :  Dict						=  ''''''

 if is_finetuned:
  __a :  int						=  UniSpeechSatForCTC(lowerCAmelCase__  )
 else:
  __a :  List[str]						=  UniSpeechSatForPreTraining(lowerCAmelCase__  )

 __a				,       __a				,       __a :  int						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]  ,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
 __a :  List[str]						=  model[0].eval()

 recursively_load_weights(lowerCAmelCase__  ,   lowerCAmelCase__  )

 hf_wavavec.save_pretrained(lowerCAmelCase__  )


if __name__ == "__main__":
      lowercase__       =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'
      )
      lowercase__       =parser.parse_args()
      convert_unispeech_sat_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )


def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 5_0_0_0_0_0_0_0  ):
 __a :  int						=  set()
 __a :  str						=  int((limit - 2_4) ** (1 / 2)  )

 __a :  int						=  set(range(3  ,   prime_square_limit + 1  ,   2  )  )
 primes.add(2  )
 for p in range(3  ,   prime_square_limit + 1  ,   2  ):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p  ,   prime_square_limit + 1  ,   lowerCAmelCase__  )  )  )

 for primea in primes:
  __a :  Union[str, Any]						=  primea * primea
  for primea in primes:
   __a :  Union[str, Any]						=  primea * primea * primea
   if square + cube >= limit - 1_6:
    break
   for primea in primes:
    __a :  int						=  primea * primea * primea * primea
    __a :  Union[str, Any]						=  square + cube + tetr
    if total >= limit:
     break
    ret.add(lowerCAmelCase__  )

 return len(lowerCAmelCase__  )


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