



"""simple docstring"""






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCAmelCase      :      Union[str, Any]   					=					logging.get_logger(__name__)

__UpperCAmelCase      :      Dict   					=					{
    '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      A      (			_A,     _A,     _A,     _A,     _A							):




     """simple docstring"""
     for attribute in key.split("."							):
          snake_case_						:str								=						getattr(_A,     _A							)

     if weight_type is not None:
          snake_case_						:List[Any]								=						getattr(_A,     _A							).shape
     else:
          snake_case_						:Optional[Any]								=						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_						:Union[str, Any]								=						value
     elif weight_type == "weight_g":
          snake_case_						:Optional[Any]								=						value
     elif weight_type == "weight_v":
          snake_case_						:List[Any]								=						value
     elif weight_type == "bias":
          snake_case_						:str								=						value
     else:
          snake_case_						:Any								=						value

     logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''							)
def      A      (			_A,     _A							):




     """simple docstring"""
     snake_case_						:List[Any]								=						[]
     snake_case_						:List[str]								=						fairseq_model.state_dict()

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

     for name, value in fairseq_dict.items():
          snake_case_						:Union[str, Any]								=						False
          if "conv_layers" in name:
               load_conv_layer(
                   _A,     _A,     _A,     _A,     hf_model.config.feat_extract_norm == "group",     )
               snake_case_						:str								=						True
          else:
               for key, mapped_key in MAPPING.items():
                    snake_case_						:str								=						mapped_key

                    if key in name:
                         snake_case_						:Dict								=						True
                         if "*" in mapped_key:
                              snake_case_						:Dict								=						name.split(_A							)[0].split("."							)[-2]
                              snake_case_						:List[Any]								=						mapped_key.replace("*",     _A							)
                         if "weight_g" in name:
                              snake_case_						:Any								=						"weight_g"
                         elif "weight_v" in name:
                              snake_case_						:Union[str, Any]								=						"weight_v"
                         elif "weight" in name:
                              snake_case_						:List[str]								=						"weight"
                         elif "bias" in name:
                              snake_case_						:Optional[int]								=						"bias"
                         else:
                              snake_case_						:Any								=						None
                         set_recursively(_A,     _A,     _A,     _A,     _A							)
                    continue
          if not is_used:
               unused_weights.append(_A							)

     logger.warning(F'''Unused weights: {unused_weights}'''							)
def      A      (			_A,     _A,     _A,     _A,     _A							):




     """simple docstring"""
     snake_case_						:Optional[int]								=						full_name.split("conv_layers."							)[-1]
     snake_case_						:Union[str, Any]								=						name.split("."							)
     snake_case_						:Dict								=						int(items[0]							)
     snake_case_						: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.'''
               )
               snake_case_						: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.'''
               )
               snake_case_						:List[Any]								=						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_						:str								=						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_						:Any								=						value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
     else:
          unused_weights.append(_A							)
def      A      (			_A							):




     """simple docstring"""
     snake_case_						:List[str]								=						HubertConfig()
     snake_case_						:str								=						model.config

     snake_case_						:Optional[int]								=						fs_config.activation_dropout
     snake_case_						:Dict								=						False
     snake_case_						:Optional[Any]								=						fs_config.attention_dropout
     snake_case_						:Optional[Any]								=						False
     snake_case_						:Tuple								=						eval(fs_config.extractor_conv_feature_layers							)
     snake_case_						:Dict								=						[x[0] for x in conv_layers]
     snake_case_						:Dict								=						[x[1] for x in conv_layers]
     snake_case_						:Optional[Any]								=						[x[2] for x in conv_layers]
     snake_case_						:int								=						"gelu"
     snake_case_						:Dict								=						"layer" if fs_config.extractor_mode == "layer_norm" else "group"
     snake_case_						:Dict								=						False
     snake_case_						:List[Any]								=						0.0
     snake_case_						:Tuple								=						0.0
     snake_case_						:Optional[int]								=						fs_config.activation_fn
     snake_case_						:Any								=						fs_config.dropout
     snake_case_						:int								=						fs_config.encoder_embed_dim
     snake_case_						:List[str]								=						0.02
     snake_case_						:Optional[int]								=						fs_config.encoder_ffn_embed_dim
     snake_case_						:Union[str, Any]								=						1e-5
     snake_case_						:Tuple								=						0.0
     snake_case_						:List[str]								=						fs_config.encoder_attention_heads
     snake_case_						:Optional[Any]								=						fs_config.conv_pos_groups
     snake_case_						:Tuple								=						fs_config.conv_pos
     snake_case_						:Dict								=						len(_A							)
     snake_case_						:Optional[int]								=						fs_config.encoder_layers

     return config
@torch.no_grad()
def      A      (			_A,     _A=None							):




     """simple docstring"""
     snake_case_						:Optional[Any]								=						distilhubert().model.model

     if config_path is not None:
          snake_case_						:Dict								=						HubertConfig.from_pretrained(_A							)
     else:
          snake_case_						:Union[str, Any]								=						convert_config(_A							)
     snake_case_						:str								=						model.eval()

     snake_case_						:Optional[int]								=						WavaVecaFeatureExtractor(
         feature_size=1,     sampling_rate=16_000,     padding_value=0,     do_normalize=_A,     return_attention_mask=_A,     )
     snake_case_						:List[str]								=						HubertModel(_A							)

     recursively_load_weights(_A,     _A							)

     feature_extractor.save_pretrained(_A							)
     hf_model.save_pretrained(_A							)


if __name__ == "__main__":
    __UpperCAmelCase      :      List[str]   					=					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      :      Any   					=					parser.parse_args()
    convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






"""simple docstring"""






def      A      (			_A,     _A = 1_000							):




					"""simple docstring"""
					if (not isinstance(_A,     _A							)) or (digit_position <= 0):
										raise ValueError("Digit position must be a positive integer"							)
					elif (not isinstance(_A,     _A							)) or (precision < 0):
										raise ValueError("Precision must be a nonnegative integer"							)

					# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
					# accurate
					snake_case_						:List[Any]								=						(
					    4 * _subsum(_A,     1,     _A							)
					    - 2 * _subsum(_A,     4,     _A							)
					    - _subsum(_A,     5,     _A							)
					    - _subsum(_A,     6,     _A							)
					)

					# return the first hex digit of the fractional part of the result
					return hex(int((sum_result % 1) * 16							)							)[2:]
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:int								=						0.0
					for sum_index in range(digit_pos_to_extract + precision							):
										snake_case_						:Optional[Any]								=						8 * sum_index + denominator_addend
										if sum_index < digit_pos_to_extract:
															# if the exponential term is an integer and we mod it by the denominator
															# before dividing, only the integer part of the sum will change;
															# the fractional part will not
															snake_case_						:List[Any]								=						pow(
															    16,     digit_pos_to_extract - 1 - sum_index,     _A							)
										else:
															snake_case_						:Optional[Any]								=						pow(16,     digit_pos_to_extract - 1 - sum_index							)
										total += exponential_term / denominator
					return total


if __name__ == "__main__":
				import doctest

				doctest.testmod()

