


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

UpperCAmelCase     =       {
    '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',
    'mask_emb': 'masked_spec_embed',
}






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    str:




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




      """simple docstring"""

      snake_case_			= []
      snake_case_			= fairseq_model.state_dict()

      snake_case_			= 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_			= mapped_key

                        if key in name:
                              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 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    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   )






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




      """simple docstring"""

      snake_case_			= HubertConfig()
      snake_case_			= model.config

      snake_case_			= fs_config.activation_dropout
      snake_case_			= False
      snake_case_			= fs_config.attention_dropout
      snake_case_			= False
      snake_case_			= eval(fs_config.extractor_conv_feature_layers   )
      snake_case_			= [x[0] for x in conv_layers]
      snake_case_			= [x[1] for x in conv_layers]
      snake_case_			= [x[2] for x in conv_layers]
      snake_case_			= '''gelu'''
      snake_case_			= '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      snake_case_			= False
      snake_case_			= 0.0
      snake_case_			= 0.0
      snake_case_			= fs_config.activation_fn
      snake_case_			= fs_config.dropout
      snake_case_			= fs_config.encoder_embed_dim
      snake_case_			= 0.0_2
      snake_case_			= fs_config.encoder_ffn_embed_dim
      snake_case_			= 1E-5
      snake_case_			= 0.0
      snake_case_			= fs_config.encoder_attention_heads
      snake_case_			= fs_config.conv_pos_groups
      snake_case_			= fs_config.conv_pos
      snake_case_			= len(_UpperCamelCase   )
      snake_case_			= fs_config.encoder_layers

      return config






@torch.no_grad()
def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE=None   )->    Union[str, Any]:




      """simple docstring"""

      snake_case_			= distilhubert().model.model

      if config_path is not None:
            snake_case_			= HubertConfig.from_pretrained(_UpperCamelCase   )
      else:
            snake_case_			= convert_config(_UpperCamelCase   )
      snake_case_			= model.eval()

      snake_case_			= WavaVecaFeatureExtractor(
          feature_size=1   ,   sampling_rate=1_6000   ,   padding_value=0   ,   do_normalize=_UpperCamelCase   ,   return_attention_mask=_UpperCamelCase   ,   )
      snake_case_			= HubertModel(_UpperCamelCase   )

      recursively_load_weights(_UpperCamelCase   ,   _UpperCamelCase   )

      feature_extractor.save_pretrained(_UpperCamelCase   )
      hf_model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCAmelCase     =       argparse.ArgumentParser()
   parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   UpperCAmelCase     =       parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
'''simple docstring'''




import itertools





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int = 1_00_00_00  )      -> int:
    A_      =  [1]

    for i in itertools.count(len(_UpperCamelCase  )  ):
        A_      =  0
        for j in itertools.count(1  ):
            A_      =  -1 if j % 2 == 0 else +1
            A_      =  (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(_UpperCamelCase  )

    return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")






