code: 
  
  
  
  '''simple docstring'''
  
  
  
  import jax
  import jax.numpy as jnp
  from bigbird_flax import FlaxBigBirdForNaturalQuestions
  from datasets import load_from_disk
  
  from transformers import BigBirdTokenizerFast
  
  
  a__						:			Dict										=			{0: 'null', 1: 'short', 2: 'long', 3: 'yes', 4: 'no'}
  a__						:			List[str]										=			set(''.join(['‘', '’', '´', '`', '.', ',', '-', '"']))
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A=0							,__A=None       ):
  
  
  
   '''simple docstring'''
  
  
  
   return [" ".join(x.split(""" """       )[begin:end]       ) for x in answers if len(x.split(""" """       )       ) > 1]
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A=False       ):
  
  
  
   '''simple docstring'''
  
  
  
   if make_sub_answers:
    # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
    # *e.g.* if the correct answer contains a prefix such as "the", or "a"
    __UpperCamelCase				      =  (
        given_answers + get_sub_answers(__A							,begin=1       ) + get_sub_answers(__A							,end=-1       )
    )
   __UpperCamelCase				      =  []
   for answer in given_answers:
    __UpperCamelCase				      =  answer.replace("""_"""							,""" """       ).lower()
    __UpperCamelCase				      =  """""".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else """ """ for c in alias       )
    answers.append(""" """.join(alias.split()       ).strip()       )
   return set(__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A=1							,__A=100       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase      ,		__UpperCamelCase				      =  jax.lax.top_k(__A							,__A       )
   __UpperCamelCase      ,		__UpperCamelCase				      =  jax.lax.top_k(__A							,__A       )
  
   __UpperCamelCase				      =  best_end_idx[:, None] - best_start_idx[None, :]
   __UpperCamelCase				      =  jnp.logical_or(widths < 0							,widths > max_size       )
   __UpperCamelCase				      =  (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
   __UpperCamelCase				      =  jnp.argmax(__A       ).item()
  
   return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  sample["""question"""]["""text"""]
   __UpperCamelCase				      =  sample["""document"""]["""tokens"""]["""token"""]
   __UpperCamelCase				      =  sample["""document"""]["""tokens"""]["""is_html"""]
   __UpperCamelCase				      =  sample["""annotations"""]["""long_answer"""]
   __UpperCamelCase				      =  sample["""annotations"""]["""short_answers"""]
  
   __UpperCamelCase				      =  """ """.join([context[i] for i in range(len(__A       )       ) if not is_html[i]]       )
  
   # 0 - No ; 1 - Yes
   for answer in sample["annotations"]["yes_no_answer"]:
    if answer == 0 or answer == 1:
     return {
         "question": question,
         "context": context_string,
         "short": [],
         "long": [],
         "category": "no" if answer == 0 else "yes",
     }
  
   __UpperCamelCase				      =  []
   for s in short_answers:
    short_targets.extend(s["""text"""]       )
   __UpperCamelCase				      =  list(set(__A       )       )
  
   __UpperCamelCase				      =  []
   for s in long_answers:
    if s["start_token"] == -1:
     continue
    __UpperCamelCase				      =  context[s["""start_token"""] : s["""end_token"""]]
    __UpperCamelCase				      =  is_html[s["""start_token"""] : s["""end_token"""]]
    __UpperCamelCase				      =  """ """.join([answer[i] for i in range(len(__A       )       ) if not html[i]]       )
    if new_answer not in long_targets:
     long_targets.append(__A       )
  
   __UpperCamelCase				      =  """long_short""" if len(short_targets + long_targets       ) > 0 else """null"""
  
   return {
       "question": question,
       "context": context_string,
       "short": short_targets,
       "long": long_targets,
       "category": category,
   }
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  load_from_disk("""natural-questions-validation"""       )
   __UpperCamelCase				      =  dataset.map(__A       ).remove_columns(["""annotations""", """document""", """id"""]       )
   print(__A       )
  
   __UpperCamelCase				      =  dataset.filter(lambda __A       : (len(x["""question"""]       ) + len(x["""context"""]       )) < 4 * 4_096       )
   __UpperCamelCase				      =  short_validation_dataset.filter(lambda __A       : x["category"] != "null"       )
   short_validation_dataset
  
   __UpperCamelCase				      =  """vasudevgupta/flax-bigbird-natural-questions"""
   __UpperCamelCase				      =  FlaxBigBirdForNaturalQuestions.from_pretrained(__A       )
   __UpperCamelCase				      =  BigBirdTokenizerFast.from_pretrained(__A       )
  
   @jax.jit
   def forward(*__A							,**__A       ):
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  model(*__A							,**__A       )
    return start_logits, end_logits, jnp.argmax(__A							,axis=-1       )
  
   def evaluate(__A       ):
    # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
    __UpperCamelCase				      =  tokenizer(
        example["""question"""]							,example["""context"""]							,return_tensors="""np"""							,max_length=4_096							,padding="""max_length"""							,truncation=__A							,)
  
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  forward(**__A       )
  
    __UpperCamelCase				      =  CATEGORY_MAPPING[category.item()]
  
    __UpperCamelCase				      =  example["""long"""] + example["""short"""]
    if example["category"] in ["yes", "no", "null"]:
     __UpperCamelCase				      =  [example["""category"""]]
    __UpperCamelCase				      =  example["""category"""] != """null"""
    # Now target can be: "yes", "no", "null", "list of long & short answers"
  
    if predicted_category in ["yes", "no", "null"]:
     __UpperCamelCase				      =  [predicted_category]
     __UpperCamelCase				      =  example["""output"""] == example["""targets"""]
     __UpperCamelCase				      =  predicted_category != """null"""
     return example
  
    __UpperCamelCase				      =  38 if predicted_category == """short""" else 1_024
    __UpperCamelCase      ,		__UpperCamelCase				      =  get_best_valid_start_end_idx(
        start_scores[0]							,end_scores[0]							,top_k=8							,max_size=__A       )
  
    __UpperCamelCase				      =  inputs["""input_ids"""][0].tolist()
    __UpperCamelCase				      =  [tokenizer.decode(input_ids[start_score : end_score + 1]       )]
  
    __UpperCamelCase				      =  expand_to_aliases(example["""targets"""]							,make_sub_answers=__A       )
    __UpperCamelCase				      =  expand_to_aliases(example["""output"""]       )
  
    # some preprocessing to both prediction and answer
    __UpperCamelCase				      =  {"""""".join(a.split()       ) for a in answers}
    __UpperCamelCase				      =  {"""""".join(p.split()       ) for p in predictions}
    __UpperCamelCase				      =  {s for s in predictions if s not in ["""``""", """''""", """`""", """'"""]}
  
    # if there is a common element, it's a exact match
    __UpperCamelCase				      =  len(list(answers & predictions       )       ) > 0
    __UpperCamelCase				      =  predicted_category != """null""" and len(__A       ) > 0
  
    return example
  
   __UpperCamelCase				      =  short_validation_dataset.map(__A       )
  
   __UpperCamelCase				      =  len(__A       )
   __UpperCamelCase				      =  len(short_validation_dataset.filter(lambda __A       : x["match"] == 1       )       )
   print("""EM score:"""							,(matched / total) * 100							,"""%"""       )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
  
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)
  
  
  
  
  
  