"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_a   :	Optional[Any] 					=     logging.get_logger(__name__)

_a   :	Optional[Any] 					=     {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}
_a   :	Union[str, Any] 					=     [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: str			) ->   Tuple:
  for attribute in key.split("""."""			):
    _lowerCAmelCase		: Dict						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			)

  if weight_type is not None:
    _lowerCAmelCase		: Any						    =  getattr(_lowerCamelCase    ,_lowerCamelCase			).shape
  else:
    _lowerCAmelCase		: Any						    =  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":
    _lowerCAmelCase		: Union[str, Any]						    =  value
  elif weight_type == "weight_g":
    _lowerCAmelCase		: Dict						    =  value
  elif weight_type == "weight_v":
    _lowerCAmelCase		: Optional[int]						    =  value
  elif weight_type == "bias":
    _lowerCAmelCase		: int						    =  value
  elif weight_type == "running_mean":
    _lowerCAmelCase		: Union[str, Any]						    =  value
  elif weight_type == "running_var":
    _lowerCAmelCase		: Union[str, Any]						    =  value
  elif weight_type == "num_batches_tracked":
    _lowerCAmelCase		: Any						    =  value
  elif weight_type == "inv_freq":
    _lowerCAmelCase		: Optional[Any]						    =  value
  else:
    _lowerCAmelCase		: int						    =  value

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


def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: int			) ->   Union[str, Any]:
  _lowerCAmelCase		: Any						    =  []
  _lowerCAmelCase		: Optional[int]						    =  fairseq_model.state_dict()

  _lowerCAmelCase		: Union[str, Any]						    =  hf_model.wavaveca_conformer.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase		: int						    =  False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,hf_model.config.feat_extract_norm == """group"""    ,)
      _lowerCAmelCase		: Optional[int]						    =  True
    else:
      for key, mapped_key in MAPPING.items():
        _lowerCAmelCase		: Any						    =  'wav2vec2_conformer.' + 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]:
          _lowerCAmelCase		: Optional[Any]						    =  True
          if "*" in mapped_key:
            _lowerCAmelCase		: str						    =  name.split(_lowerCamelCase			)[0].split("""."""			)[-2]
            _lowerCAmelCase		: int						    =  mapped_key.replace("""*"""    ,_lowerCamelCase			)
          if "pos_bias_u" in name:
            _lowerCAmelCase		: Optional[int]						    =  None
          elif "pos_bias_v" in name:
            _lowerCAmelCase		: Dict						    =  None
          elif "weight_g" in name:
            _lowerCAmelCase		: Optional[Any]						    =  'weight_g'
          elif "weight_v" in name:
            _lowerCAmelCase		: Dict						    =  'weight_v'
          elif "bias" in name:
            _lowerCAmelCase		: Tuple						    =  'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _lowerCAmelCase		: int						    =  'weight'
          elif "running_mean" in name:
            _lowerCAmelCase		: str						    =  'running_mean'
          elif "inv_freq" in name:
            _lowerCAmelCase		: List[Any]						    =  'inv_freq'
          elif "running_var" in name:
            _lowerCAmelCase		: Union[str, Any]						    =  'running_var'
          elif "num_batches_tracked" in name:
            _lowerCAmelCase		: Optional[Any]						    =  'num_batches_tracked'
          else:
            _lowerCAmelCase		: List[str]						    =  None
          set_recursively(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
        continue
    if not is_used:
      unused_weights.append(_lowerCamelCase			)

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


def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: str			) ->   Any:
  _lowerCAmelCase		: str						    =  full_name.split("""conv_layers."""			)[-1]
  _lowerCAmelCase		: str						    =  name.split("""."""			)
  _lowerCAmelCase		: Dict						    =  int(items[0]			)
  _lowerCAmelCase		: Any						    =  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."			)
      _lowerCAmelCase		: int						    =  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."			)
      _lowerCAmelCase		: int						    =  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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."			)
      _lowerCAmelCase		: Any						    =  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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."			)
      _lowerCAmelCase		: List[str]						    =  value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
  else:
    unused_weights.append(_lowerCamelCase			)


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Tuple    ,_lowerCamelCase		: List[Any]=None    ,_lowerCamelCase		: Union[str, Any]=None    ,_lowerCamelCase		: Any=True			) ->   Any:
  if config_path is not None:
    _lowerCAmelCase		: Tuple						    =  WavaVecaConformerConfig.from_pretrained(_lowerCamelCase    ,hidden_act="""swish"""			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  WavaVecaConformerConfig()

  if "rope" in checkpoint_path:
    _lowerCAmelCase		: Dict						    =  'rotary'

  if is_finetuned:
    if dict_path:
      _lowerCAmelCase		: Dict						    =  Dictionary.load(_lowerCamelCase			)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowerCAmelCase		: int						    =  target_dict.pad_index
      _lowerCAmelCase		: List[Any]						    =  target_dict.bos_index
      _lowerCAmelCase		: Any						    =  target_dict.eos_index
      _lowerCAmelCase		: Dict						    =  len(target_dict.symbols			)
      _lowerCAmelCase		: Optional[Any]						    =  os.path.join(_lowerCamelCase    ,"""vocab.json"""			)
      if not os.path.isdir(_lowerCamelCase			):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCamelCase			)			)
        return
      os.makedirs(_lowerCamelCase    ,exist_ok=_lowerCamelCase			)
      _lowerCAmelCase		: List[str]						    =  target_dict.indices

      # fairseq has the <pad> and <s> switched
      _lowerCAmelCase		: int						    =  0
      _lowerCAmelCase		: Optional[Any]						    =  1
      with open(_lowerCamelCase    ,"""w"""    ,encoding="""utf-8"""			) as vocab_handle:
        json.dump(_lowerCamelCase    ,_lowerCamelCase			)
      _lowerCAmelCase		: Optional[Any]						    =  WavaVecaCTCTokenizer(
          _lowerCamelCase    ,unk_token=target_dict.unk_word    ,pad_token=target_dict.pad_word    ,bos_token=target_dict.bos_word    ,eos_token=target_dict.eos_word    ,word_delimiter_token="""|"""    ,do_lower_case=_lowerCamelCase    ,)
      _lowerCAmelCase		: Tuple						    =  True if config.feat_extract_norm == 'layer' else False
      _lowerCAmelCase		: Any						    =  WavaVecaFeatureExtractor(
          feature_size=1    ,sampling_rate=16000    ,padding_value=0    ,do_normalize=_lowerCamelCase    ,return_attention_mask=_lowerCamelCase    ,)
      _lowerCAmelCase		: Optional[int]						    =  WavaVecaProcessor(feature_extractor=_lowerCamelCase    ,tokenizer=_lowerCamelCase			)
      processor.save_pretrained(_lowerCamelCase			)

    _lowerCAmelCase		: List[Any]						    =  WavaVecaConformerForCTC(_lowerCamelCase			)
  else:
    _lowerCAmelCase		: List[Any]						    =  WavaVecaConformerForPreTraining(_lowerCamelCase			)

  if is_finetuned:
    _lowerCAmelCase		: List[Any]						    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""			)[:-1]			)}			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  argparse.Namespace(task="""audio_pretraining"""			)
    _lowerCAmelCase		: str						    =  fairseq.tasks.setup_task(_lowerCamelCase			)

    _lowerCAmelCase		: List[Any]						    =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,task=_lowerCamelCase			)

  _lowerCAmelCase		: Tuple						    =  model[0].eval()

  recursively_load_weights(_lowerCamelCase    ,_lowerCamelCase    ,not is_finetuned			)

  hf_wavavec.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
     _a   :	int 					=     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'
     )
     _a   :	List[str] 					=     parser.parse_args()
     convert_wavaveca_conformer_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )





"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple			) ->   Dict:
  _lowerCAmelCase		: List[str]						    =  int(_lowerCamelCase			)
  assert noofclusters < len(_lowerCamelCase			)

  # Find out the dimensionality
  _lowerCAmelCase		: Any						    =  len(vectors[0]			)

  # Will help select random centroids from among the available vectors
  _lowerCAmelCase		: Any						    =  list(range(len(_lowerCamelCase			)			)			)
  shuffle(_lowerCamelCase			)

  # GRAPH OF COMPUTATION
  # We initialize a new graph and set it as the default during each run
  # of this algorithm. This ensures that as this function is called
  # multiple times, the default graph doesn't keep getting crowded with
  # unused ops and Variables from previous function calls.

  _lowerCAmelCase		: List[Any]						    =  tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    _lowerCAmelCase		: str						    =  tf.Session()

    ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

    ##First lets ensure we have a Variable vector for each centroid,
    ##initialized to one of the vectors from the available data points
    _lowerCAmelCase		: List[str]						    =  [
        tf.Variable(vectors[vector_indices[i]]			) for i in range(_lowerCamelCase			)
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float64"""    ,[dim]			)
    _lowerCAmelCase		: Optional[int]						    =  []
    for centroid in centroids:
      cent_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Variables for cluster assignments of individual vectors(initialized
    ##to 0 at first)
    _lowerCAmelCase		: Dict						    =  [tf.Variable(0			) for i in range(len(_lowerCamelCase			)			)]
    ##These nodes will assign an assignment Variable the appropriate
    ##value
    _lowerCAmelCase		: List[Any]						    =  tf.placeholder("""int32"""			)
    _lowerCAmelCase		: Any						    =  []
    for assignment in assignments:
      cluster_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float"""    ,[None, dim]			)
    # The Node/op takes the input and computes a mean along the 0th
    # dimension, i.e. the list of input vectors
    _lowerCAmelCase		: Optional[int]						    =  tf.reduce_mean(_lowerCamelCase    ,0			)

    ##Node for computing Euclidean distances
    # Placeholders for input
    _lowerCAmelCase		: Dict						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: List[Any]						    =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase    ,_lowerCamelCase			)    ,2			)			)			)

    ##This node will figure out which cluster to assign a vector to,
    ##based on Euclidean distances of the vector from the centroids.
    # Placeholder for input
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[noofclusters]			)
    _lowerCAmelCase		: str						    =  tf.argmin(_lowerCamelCase    ,0			)

    ##INITIALIZING STATE VARIABLES

    ##This will help initialization of all Variables defined with respect
    ##to the graph. The Variable-initializer should be defined after
    ##all the Variables have been constructed, so that each of them
    ##will be included in the initialization.
    _lowerCAmelCase		: Optional[Any]						    =  tf.initialize_all_variables()

    # Initialize all variables
    sess.run(_lowerCamelCase			)

    ##CLUSTERING ITERATIONS

    # Now perform the Expectation-Maximization steps of K-Means clustering
    # iterations. To keep things simple, we will only do a set number of
    # iterations, instead of using a Stopping Criterion.
    _lowerCAmelCase		: List[str]						    =  100
    for _ in range(_lowerCamelCase			):
      ##EXPECTATION STEP
      ##Based on the centroid locations till last iteration, compute
      ##the _expected_ centroid assignments.
      # Iterate over each vector
      for vector_n in range(len(_lowerCamelCase			)			):
        _lowerCAmelCase		: int						    =  vectors[vector_n]
        # Compute Euclidean distance between this vector and each
        # centroid. Remember that this list cannot be named
        #'centroid_distances', since that is the input to the
        # cluster assignment node.
        _lowerCAmelCase		: Any						    =  [
            sess.run(_lowerCamelCase    ,feed_dict={va: vect, va: sess.run(_lowerCamelCase			)}			)
            for centroid in centroids
        ]
        # Now use the cluster assignment node, with the distances
        # as the input
        _lowerCAmelCase		: Any						    =  sess.run(
            _lowerCamelCase    ,feed_dict={centroid_distances: distances}			)
        # Now assign the value to the appropriate state variable
        sess.run(
            cluster_assigns[vector_n]    ,feed_dict={assignment_value: assignment}			)

      ##MAXIMIZATION STEP
      # Based on the expected state computed from the Expectation Step,
      # compute the locations of the centroids so as to maximize the
      # overall objective of minimizing within-cluster Sum-of-Squares
      for cluster_n in range(_lowerCamelCase			):
        # Collect all the vectors assigned to this cluster
        _lowerCAmelCase		: List[Any]						    =  [
            vectors[i]
            for i in range(len(_lowerCamelCase			)			)
            if sess.run(assignments[i]			) == cluster_n
        ]
        # Compute new centroid location
        _lowerCAmelCase		: Optional[int]						    =  sess.run(
            _lowerCamelCase    ,feed_dict={mean_input: array(_lowerCamelCase			)}			)
        # Assign value to appropriate variable
        sess.run(
            cent_assigns[cluster_n]    ,feed_dict={centroid_value: new_location}			)

        # Return centroids and assignments
    _lowerCAmelCase		: Optional[int]						    =  sess.run(_lowerCamelCase			)
    _lowerCAmelCase		: List[Any]						    =  sess.run(_lowerCamelCase			)
    return centroids, assignments




