

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCamelCase_    =					{
    '''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    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_							)	->				str:





  '''simple docstring'''





  for attribute in key.split("""."""							):
    snake_case_       						=      getattr(lowercase_			,					lowercase_							)

  if weight_type is not None:
    snake_case_       						=      getattr(lowercase_			,					lowercase_							).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    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Dict:





  '''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(
          lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					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(lowercase_							)[0].split("""."""							)[-2]
            snake_case_       						=      mapped_key.replace("""*"""			,					lowercase_							)
          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(lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_							)
        continue
    if not is_used:
      unused_weights.append(lowercase_							)

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



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''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(lowercase_							)



def    UpperCamelCase(					lowercase_							)	->				Optional[int]:





  '''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_       						=      0.02
  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(lowercase_							)
  snake_case_       						=      fs_config.encoder_layers

  return config



@torch.no_grad()
def    UpperCamelCase(					lowercase_			,					lowercase_=None							)	->				int:





  '''simple docstring'''





  snake_case_       						=      distilhubert().model.model

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

  snake_case_       						=      WavaVecaFeatureExtractor(
      feature_size=1			,					sampling_rate=16000			,					padding_value=0			,					do_normalize=lowercase_			,					return_attention_mask=lowercase_			,					)
  snake_case_       						=      HubertModel(lowercase_							)

  recursively_load_weights(lowercase_			,					lowercase_							)

  feature_extractor.save_pretrained(lowercase_							)
  hf_model.save_pretrained(lowercase_							)


if __name__ == "__main__":
    lowerCamelCase_    =					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''')
    lowerCamelCase_    =					parser.parse_args()
    convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


def    UpperCamelCase(					lowercase_							)	->				str:





  '''simple docstring'''





  if len(lowercase_							) == 0 or not all(len(lowercase_							) == 2 for pair in coordinates							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  if len({tuple(lowercase_							) for pair in coordinates}							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      {x for x, _ in coordinates}
  if len(lowercase_							) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(lowercase_							) != len(lowercase_							):
    raise ValueError("""The program cannot work out a fitting polynomial."""							)

  snake_case_       						=      len(lowercase_							)

  snake_case_       						=      0
  snake_case_       						=      []
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    snake_case_       						=      0
    snake_case_       						=      coordinates[count_of_line][0]
    snake_case_       						=      []
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))							)
      count_in_line += 1
    matrix.append(lowercase_							)
    count_of_line += 1

  snake_case_       						=      0
  # put the y values into a vector
  snake_case_       						=      []
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]							)
    count_of_line += 1

  snake_case_       						=      0

  while count < x:
    snake_case_       						=      0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      snake_case_       						=      matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]							):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  snake_case_       						=      0
  # make solutions
  snake_case_       						=      []
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]							)							)
    count += 1

  snake_case_       						=      0
  snake_case_       						=      """f(x)="""

  while count < x:
    snake_case_       						=      solution[count].split("""E"""							)
    if len(lowercase_							) > 1:
      snake_case_       						=      f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
    print(points_to_polynomial([]))
    print(points_to_polynomial([[]]))
    print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
    print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
    print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
    print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
    print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
    print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
    print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))