






"""simple docstring"""


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowercase_				     =     {
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowercase_				     =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  for attribute in key.split('''.''' ):
    __A									= getattr(__UpperCamelCase		,       __UpperCamelCase )

  if weight_type is not None:
    __A									= getattr(__UpperCamelCase		,       __UpperCamelCase ).shape
  else:
    __A									= hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        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":
    __A									= value
  elif weight_type == "weight_g":
    __A									= value
  elif weight_type == "weight_v":
    __A									= value
  elif weight_type == "bias":
    __A									= value
  else:
    __A									= value

  logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= []
  __A									= fairseq_model.state_dict()

  __A									= hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    __A									= False
    if "conv_layers" in name:
      load_conv_layer(
          __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       hf_model.config.feat_extract_norm == '''group'''		,       )
      __A									= True
    else:
      for key, mapped_key in MAPPING.items():
        __A									= '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
          if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
            # special case since naming is very similar
            continue
          __A									= True
          if "*" in mapped_key:
            __A									= name.split(__UpperCamelCase )[0].split('''.''' )[-2]
            __A									= mapped_key.replace('''*'''		,       __UpperCamelCase )
          if "weight_g" in name:
            __A									= '''weight_g'''
          elif "weight_v" in name:
            __A									= '''weight_v'''
          elif "bias" in name:
            __A									= '''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __A									= '''weight'''
          else:
            __A									= 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				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= full_name.split('''conv_layers.''' )[-1]
  __A									= name.split('''.''' )
  __A									= int(items[0] )
  __A									= int(items[1] )

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
      __A									= value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
      __A									= 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:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
      __A									= value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
      __A									= value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
  else:
    unused_weights.append(__UpperCamelCase )



@torch.no_grad()
def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase=None		,       __UpperCamelCase=None		,       __UpperCamelCase=True ):




  """simple docstring"""







  if config_path is not None:
    __A									= UniSpeechSatConfig.from_pretrained(__UpperCamelCase )
  else:
    __A									= UniSpeechSatConfig()

  __A									= ''''''

  if is_finetuned:
    __A									= UniSpeechSatForCTC(__UpperCamelCase )
  else:
    __A									= UniSpeechSatForPreTraining(__UpperCamelCase )

  __A			,    __A			,    __A									= fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]		,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
  __A									= model[0].eval()

  recursively_load_weights(__UpperCamelCase		,       __UpperCamelCase )

  hf_wavavec.save_pretrained(__UpperCamelCase )


if __name__ == "__main__":
       lowercase_				     =     argparse.ArgumentParser()
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
       )
       lowercase_				     =     parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )












"""simple docstring"""


def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= current_set.copy()
  for row_index, row in enumerate(__UpperCamelCase ):
    __A									= row[0]
    for column_index, column in enumerate(__UpperCamelCase ):
      if magnitude == 0:
        __A									= column
        continue
      __A									= column / magnitude
    # Subtract to cancel term
  __A									= current_set[0]
  __A									= [first_row]
  __A									= current_set[1::]
  for row in current_set:
    __A									= []
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(__UpperCamelCase )
      continue
    for column_index in range(len(__UpperCamelCase ) ):
      temp_row.append(first_row[column_index] - row[column_index] )
    final_set.append(__UpperCamelCase )
  # Create next recursion iteration set
  if len(final_set[0] ) != 3:
    __A									= final_set[0]
    __A									= []
    __A									= []
    for row in final_set[1::]:
      current_first_column.append(row[0] )
      next_iteration.append(row[1::] )
    __A									= simplify(__UpperCamelCase )
    for i in range(len(__UpperCamelCase ) ):
      resultant[i].insert(0		,       current_first_column[i] )
    resultant.insert(0		,       __UpperCamelCase )
    __A									= resultant
  return final_set



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if len(__UpperCamelCase ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  __A									= len(__UpperCamelCase ) + 1
  if any(len(__UpperCamelCase ) != _length for item in equations ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  for row in equations:
    if any(not isinstance(__UpperCamelCase		,       (int, float) ) for column in row ):
      raise ValueError('''solve_simultaneous() requires lists of integers''' )
  if len(__UpperCamelCase ) == 1:
    return [equations[0][-1] / equations[0][0]]
  __A									= equations.copy()
  if any(0 in row for row in data_set ):
    __A									= data_set.copy()
    __A									= []
    for row_index, row in enumerate(__UpperCamelCase ):
      if 0 not in row:
        __A									= data_set.pop(__UpperCamelCase )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
    data_set.insert(0		,       __UpperCamelCase )
  __A									= data_set.copy()
  __A									= simplify(__UpperCamelCase )
  __A									= simplified[::-1]
  __A									= []
  for row in simplified:
    __A									= row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0 )
        continue
      solutions.append(current_solution / row[-2] )
      continue
    __A									= row.copy()[: len(__UpperCamelCase ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0 )
    if len(__UpperCamelCase ) == 0:
      solutions.append(0 )
      continue
    __A									= temp_row[1::]
    __A									= temp_row[::-1]
    for column_index, column in enumerate(__UpperCamelCase ):
      current_solution -= column * solutions[column_index]
    solutions.append(__UpperCamelCase )
  __A									= []
  for item in solutions:
    final.append(float(round(__UpperCamelCase		,       5 ) ) )
  return final[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_				     =     [
           [2, 1, 1, 1, 1, 4],
           [1, 2, 1, 1, 1, 5],
           [1, 1, 2, 1, 1, 6],
           [1, 1, 1, 2, 1, 7],
           [1, 1, 1, 1, 2, 8],
       ]
       print(solve_simultaneous(eq))
       print(solve_simultaneous([[4, 2]]))




