code: 
  
  
  
  '''simple docstring'''
  
  
  
  import sys
  from typing import Tuple
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import nn
  
  from transformers.image_utils import PILImageResampling
  from utils import img_tensorize
  class        UpperCAmelCase__  :
  
  
  
      def __init__(  self		,	lowercase		,	lowercase=sys.maxsize			)  ->							Any:
       __UpperCamelCase				      =  "bilinear"
       __UpperCamelCase				      =  max_size
       __UpperCamelCase				      =  short_edge_length
  
  
  
  
      def __call__(  self		,	lowercase			)  ->							Union[str, Any]:
       __UpperCamelCase				      =  []
       for img in imgs:
        __UpperCamelCase				      =  img.shape[:2]
        # later: provide list and randomly choose index for resize
        __UpperCamelCase				      =  np.random.randint(self.short_edge_length[0]		,	self.short_edge_length[1] + 1			)
        if size == 0:
         return img
        __UpperCamelCase				      =  size * 1.0 / min(lowercase		,	lowercase			)
        if h < w:
         __UpperCamelCase				      =  size, scale * w
        else:
         __UpperCamelCase				      =  scale * h, size
        if max(lowercase		,	lowercase			) > self.max_size:
         __UpperCamelCase				      =  self.max_size * 1.0 / max(lowercase		,	lowercase			)
         __UpperCamelCase				      =  newh * scale
         __UpperCamelCase				      =  neww * scale
        __UpperCamelCase				      =  int(neww + 0.5			)
        __UpperCamelCase				      =  int(newh + 0.5			)
  
        if img.dtype == np.uinta:
         __UpperCamelCase				      =  Image.fromarray(lowercase			)
         __UpperCamelCase				      =  pil_image.resize((neww, newh)		,	PILImageResampling.BILINEAR			)
         __UpperCamelCase				      =  np.asarray(lowercase			)
        else:
         __UpperCamelCase				      =  img.permute(2		,	0		,	1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
         __UpperCamelCase				      =  nn.functional.interpolate(
             lowercase		,	(newh, neww)		,	mode=self.interp_method		,	align_corners=lowercase			).squeeze(0			)
        img_augs.append(lowercase			)
  
       return img_augs
  
  
  class        UpperCAmelCase__  :
  
  
  
      def __init__(  self		,	lowercase			)  ->							List[str]:
       __UpperCamelCase				      =  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,	cfg.INPUT.MAX_SIZE_TEST			)
       __UpperCamelCase				      =  cfg.INPUT.FORMAT
       __UpperCamelCase				      =  cfg.SIZE_DIVISIBILITY
       __UpperCamelCase				      =  cfg.PAD_VALUE
       __UpperCamelCase				      =  cfg.INPUT.MAX_SIZE_TEST
       __UpperCamelCase				      =  cfg.MODEL.DEVICE
       __UpperCamelCase				      =  torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)		,	1		,	1			)
       __UpperCamelCase				      =  torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)		,	1		,	1			)
       __UpperCamelCase				      =  lambda lowercase			: (x - self.pixel_mean) / self.pixel_std
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase			)  ->							Tuple:
       __UpperCamelCase				      =  tuple(max(lowercase			) for s in zip(*[img.shape for img in images]			)			)
       __UpperCamelCase				      =  [im.shape[-2:] for im in images]
       __UpperCamelCase				      =  [
           nn.functional.pad(
               lowercase		,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,	value=self.pad_value		,	)
           for size, im in zip(lowercase		,	lowercase			)
       ]
  
       return torch.stack(lowercase			), torch.tensor(lowercase			)
  
  
  
  
      def __call__(  self		,	lowercase		,	lowercase=False			)  ->							Dict:
  
  
  
  
  
  
       with torch.no_grad():
        if not isinstance(lowercase		,	lowercase			):
         __UpperCamelCase				      =  [images]
        if single_image:
         assert len(lowercase			) == 1
        for i in range(len(lowercase			)			):
         if isinstance(images[i]		,	torch.Tensor			):
          images.insert(lowercase		,	images.pop(lowercase			).to(self.device			).float()			)
         elif not isinstance(images[i]		,	torch.Tensor			):
          images.insert(
              lowercase		,	torch.as_tensor(img_tensorize(images.pop(lowercase			)		,	input_format=self.input_format			)			)
              .to(self.device			)
              .float()		,	)
         # resize smallest edge
        __UpperCamelCase				      =  torch.tensor([im.shape[:2] for im in images]			)
        __UpperCamelCase				      =  self.aug(lowercase			)
        # transpose images and convert to torch tensors
        # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
        # now normalize before pad to avoid useless arithmetic
        __UpperCamelCase				      =  [self.normalizer(lowercase			) for x in images]
        # now pad them to do the following operations
        __UpperCamelCase				      =  self.pad(lowercase			)
        # Normalize
  
        if self.size_divisibility > 0:
         raise NotImplementedError()
        # pad
        __UpperCamelCase				      =  torch.true_divide(lowercase		,	lowercase			)
        if single_image:
         return images[0], sizes[0], scales_yx[0]
        else:
         return images, sizes, scales_yx
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   assert torch.isfinite(__A       ).all(), "Box tensor contains infinite or NaN!"
   __UpperCamelCase				      =  box_size
   tensor[:, 0].clamp_(min=0							,max=__A       )
   tensor[:, 1].clamp_(min=0							,max=__A       )
   tensor[:, 2].clamp_(min=0							,max=__A       )
   tensor[:, 3].clamp_(min=0							,max=__A       )
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  a__						:			str										=			logging.get_logger(__name__)
  
  a__						:			Optional[int]										=			{
      '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 _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   for attribute in key.split("""."""       ):
    __UpperCamelCase				      =  getattr(__A							,__A       )
  
   if weight_type is not None:
    __UpperCamelCase				      =  getattr(__A							,__A       ).shape
   else:
    __UpperCamelCase				      =  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				      =  value
   elif weight_type == "weight_g":
    __UpperCamelCase				      =  value
   elif weight_type == "weight_v":
    __UpperCamelCase				      =  value
   elif weight_type == "bias":
    __UpperCamelCase				      =  value
   else:
    __UpperCamelCase				      =  value
  
   logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  []
   __UpperCamelCase				      =  fairseq_model.state_dict()
  
   __UpperCamelCase				      =  hf_model.feature_extractor
  
   for name, value in fairseq_dict.items():
    __UpperCamelCase				      =  False
    if "conv_layers" in name:
     load_conv_layer(
         __A							,__A							,__A							,__A							,hf_model.config.feat_extract_norm == """group"""							,)
     __UpperCamelCase				      =  True
    else:
     for key, mapped_key in MAPPING.items():
      __UpperCamelCase				      =  mapped_key
  
      if key in name:
       __UpperCamelCase				      =  True
       if "*" in mapped_key:
        __UpperCamelCase				      =  name.split(__A       )[0].split("""."""       )[-2]
        __UpperCamelCase				      =  mapped_key.replace("""*"""							,__A       )
       if "weight_g" in name:
        __UpperCamelCase				      =  """weight_g"""
       elif "weight_v" in name:
        __UpperCamelCase				      =  """weight_v"""
       elif "weight" in name:
        __UpperCamelCase				      =  """weight"""
       elif "bias" in name:
        __UpperCamelCase				      =  """bias"""
       else:
        __UpperCamelCase				      =  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 _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  full_name.split("""conv_layers."""       )[-1]
   __UpperCamelCase				      =  name.split("""."""       )
   __UpperCamelCase				      =  int(items[0]       )
   __UpperCamelCase				      =  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				      =  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				      =  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				      =  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				      =  value
     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
   else:
    unused_weights.append(__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  HubertConfig()
   __UpperCamelCase				      =  model.config
  
   __UpperCamelCase				      =  fs_config.activation_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  fs_config.attention_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  eval(fs_config.extractor_conv_feature_layers       )
   __UpperCamelCase				      =  [x[0] for x in conv_layers]
   __UpperCamelCase				      =  [x[1] for x in conv_layers]
   __UpperCamelCase				      =  [x[2] for x in conv_layers]
   __UpperCamelCase				      =  """gelu"""
   __UpperCamelCase				      =  """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.activation_fn
   __UpperCamelCase				      =  fs_config.dropout
   __UpperCamelCase				      =  fs_config.encoder_embed_dim
   __UpperCamelCase				      =  0.02
   __UpperCamelCase				      =  fs_config.encoder_ffn_embed_dim
   __UpperCamelCase				      =  1E-5
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.encoder_attention_heads
   __UpperCamelCase				      =  fs_config.conv_pos_groups
   __UpperCamelCase				      =  fs_config.conv_pos
   __UpperCamelCase				      =  len(__A       )
   __UpperCamelCase				      =  fs_config.encoder_layers
  
   return config
  
  
  
  
  
  
  
  @torch.no_grad()
  def _lowercase   (  __A							,__A=None       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  distilhubert().model.model
  
   if config_path is not None:
    __UpperCamelCase				      =  HubertConfig.from_pretrained(__A       )
   else:
    __UpperCamelCase				      =  convert_config(__A       )
   __UpperCamelCase				      =  model.eval()
  
   __UpperCamelCase				      =  WavaVecaFeatureExtractor(
       feature_size=1							,sampling_rate=16_000							,padding_value=0							,do_normalize=__A							,return_attention_mask=__A							,)
   __UpperCamelCase				      =  HubertModel(__A       )
  
   recursively_load_weights(__A							,__A       )
  
   feature_extractor.save_pretrained(__A       )
   hf_model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			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')
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
  
  
  
  
  
  