



"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowercase__		:				List[Any]          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
lowercase__		:				Any          =	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= {}
  with open(lowerCAmelCase__    ,   'r'						) as file:
    for line_number, line in enumerate(lowerCAmelCase__						):
      lowerCAmelCase_   :				Any								= line.strip()
      if line:
        lowerCAmelCase_   :				Optional[Any]								= line.split()
        lowerCAmelCase_   :				Union[str, Any]								= line_number
        lowerCAmelCase_   :				Any								= words[0]
        lowerCAmelCase_   :				Optional[Any]								= value
  return result

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Union[str, Any]						)						->   List[str]:

  """simple docstring"""




  for attribute in key.split('.'						):
    lowerCAmelCase_   :				Dict								= getattr(lowerCAmelCase__    ,   lowerCAmelCase__						)

  lowerCAmelCase_   :				Tuple								= None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowerCAmelCase__						):
      lowerCAmelCase_   :				Optional[int]								= PARAM_MAPPING[full_name.split('.'						)[-1]]
      lowerCAmelCase_   :				Dict								= 'param'

  if weight_type is not None and weight_type != "param":
    lowerCAmelCase_   :				Optional[int]								= getattr(lowerCAmelCase__    ,   lowerCAmelCase__						).shape
  elif weight_type is not None and weight_type == "param":
    lowerCAmelCase_   :				Optional[Any]								= hf_pointer
    for attribute in hf_param_name.split('.'						):
      lowerCAmelCase_   :				Tuple								= getattr(lowerCAmelCase__    ,   lowerCAmelCase__						)
    lowerCAmelCase_   :				Any								= shape_pointer.shape

    # let's reduce dimension
    lowerCAmelCase_   :				Union[str, Any]								= value[0]
  else:
    lowerCAmelCase_   :				str								= 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_   :				Any								= value
  elif weight_type == "weight_g":
    lowerCAmelCase_   :				Union[str, Any]								= value
  elif weight_type == "weight_v":
    lowerCAmelCase_   :				List[Any]								= value
  elif weight_type == "bias":
    lowerCAmelCase_   :				int								= value
  elif weight_type == "param":
    for attribute in hf_param_name.split('.'						):
      lowerCAmelCase_   :				Tuple								= getattr(lowerCAmelCase__    ,   lowerCAmelCase__						)
    lowerCAmelCase_   :				Union[str, Any]								= value
  else:
    lowerCAmelCase_   :				Optional[Any]								= value

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]						)						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowerCAmelCase__						):
      lowerCAmelCase_   :				Optional[int]								= PARAM_MAPPING[full_name.split('.'						)[-1]]
      lowerCAmelCase_   :				List[Any]								= 'param'

  if weight_type is not None and weight_type != "param":
    lowerCAmelCase_   :				Any								= '.'.join([key, weight_type]						)
  elif weight_type is not None and weight_type == "param":
    lowerCAmelCase_   :				List[Any]								= '.'.join([key, hf_param_name]						)
  else:
    lowerCAmelCase_   :				Tuple								= key

  lowerCAmelCase_   :				Optional[Any]								= value if 'lm_head' in full_key else value[0]


lowercase__		:				Optional[Any]          =	{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Dict=None    ,   lowerCAmelCase__      :				Optional[int]=None						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				Dict								= False
  for key, mapped_key in MAPPING.items():
    lowerCAmelCase_   :				Union[str, Any]								= 'wav2vec2.' + 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_   :				Any								= True
      if "*" in mapped_key:
        lowerCAmelCase_   :				Any								= name.split(lowerCAmelCase__						)[0].split('.'						)[-2]
        lowerCAmelCase_   :				str								= mapped_key.replace('*'    ,   lowerCAmelCase__						)
      if "weight_g" in name:
        lowerCAmelCase_   :				Any								= 'weight_g'
      elif "weight_v" in name:
        lowerCAmelCase_   :				Union[str, Any]								= 'weight_v'
      elif "bias" in name:
        lowerCAmelCase_   :				Optional[int]								= 'bias'
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        lowerCAmelCase_   :				Tuple								= 'weight'
      else:
        lowerCAmelCase_   :				Optional[Any]								= None
      if hf_dict is not None:
        rename_dict(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
      else:
        set_recursively(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
      return is_used
  return is_used

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				Tuple						)						->   Any:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= []
  lowerCAmelCase_   :				str								= fairseq_model.state_dict()

  lowerCAmelCase_   :				Optional[Any]								= hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCAmelCase_   :				Dict								= False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   hf_model.config.feat_extract_norm == 'group'    ,   )
      lowerCAmelCase_   :				List[Any]								= True
    else:
      lowerCAmelCase_   :				List[Any]								= load_wavaveca_layer(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
    if not is_used:
      unused_weights.append(lowerCAmelCase__						)

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				List[Any]						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= full_name.split('conv_layers.'						)[-1]
  lowerCAmelCase_   :				Tuple								= name.split('.'						)
  lowerCAmelCase_   :				Tuple								= int(items[0]						)
  lowerCAmelCase_   :				List[str]								= 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_   :				Any								= 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_   :				Any								= 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_   :				Union[str, 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_   :				Optional[int]								= 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 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				List[str]=None    ,   lowerCAmelCase__      :				Tuple=None    ,   lowerCAmelCase__      :				Union[str, Any]=True    ,   lowerCAmelCase__      :				Dict=False						)						->   Optional[int]:

  """simple docstring"""




  if config_path is not None:
    lowerCAmelCase_   :				Any								= WavaVecaConfig.from_pretrained(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				List[Any]								= WavaVecaConfig()

  if is_seq_class:
    lowerCAmelCase_   :				Tuple								= read_txt_into_dict(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[Any]								= idalabel
    lowerCAmelCase_   :				str								= WavaVecaForSequenceClassification(lowerCAmelCase__						)
    lowerCAmelCase_   :				Tuple								= WavaVecaFeatureExtractor(
        feature_size=1    ,   sampling_rate=1_6000    ,   padding_value=0    ,   do_normalize=lowerCAmelCase__    ,   return_attention_mask=lowerCAmelCase__    ,   )
    feature_extractor.save_pretrained(lowerCAmelCase__						)

  elif is_finetuned:
    if dict_path:
      lowerCAmelCase_   :				str								= Dictionary.load(lowerCAmelCase__						)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCAmelCase_   :				Dict								= target_dict.pad_index
      lowerCAmelCase_   :				str								= target_dict.bos_index
      lowerCAmelCase_   :				List[Any]								= target_dict.eos_index
      lowerCAmelCase_   :				List[Any]								= len(target_dict.symbols						)
      lowerCAmelCase_   :				List[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_   :				Dict								= 0
      lowerCAmelCase_   :				Optional[int]								= 1
      with open(lowerCAmelCase__    ,   'w'    ,   encoding='utf-8'						) as vocab_handle:
        json.dump(lowerCAmelCase__    ,   lowerCAmelCase__						)
      lowerCAmelCase_   :				Tuple								= 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=1_6000    ,   padding_value=0    ,   do_normalize=lowerCAmelCase__    ,   return_attention_mask=lowerCAmelCase__    ,   )
      lowerCAmelCase_   :				int								= WavaVecaProcessor(feature_extractor=lowerCAmelCase__    ,   tokenizer=lowerCAmelCase__						)
      processor.save_pretrained(lowerCAmelCase__						)

    lowerCAmelCase_   :				Any								= WavaVecaForCTC(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				str								= WavaVecaForPreTraining(lowerCAmelCase__						)

  if is_finetuned or is_seq_class:
    lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				List[str]								= fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,   arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						)}						)
  else:
    lowerCAmelCase_   :				Any								= argparse.Namespace(task='audio_pretraining'						)
    lowerCAmelCase_   :				str								= fairseq.tasks.setup_task(lowerCAmelCase__						)

    lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				int								= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,   task=lowerCAmelCase__						)

  lowerCAmelCase_   :				str								= model[0].eval()

  recursively_load_weights(lowerCAmelCase__    ,   lowerCAmelCase__    ,   not is_finetuned						)

  hf_wavavec.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				List[Any]          =	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"""
     )
     parser.add_argument(
         """--is_seq_class""",
         action="""store_true""",
         help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
     )
     lowercase__		:				str          =	parser.parse_args()

     lowercase__		:				Union[str, Any]          =	not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )










"""simple docstring"""







import random

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float    ,   lowerCAmelCase__      :				bool = False						)						->   dict:

  """simple docstring"""




  lowerCAmelCase_   :				dict								= {i: [] for i in range(lowerCAmelCase__						)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowerCAmelCase__						)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowerCAmelCase__						):
    for j in range(i + 1    ,   lowerCAmelCase__						):
      if random.random() < probability:
        graph[i].append(lowerCAmelCase__						)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowerCAmelCase__						)
  return graph

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   dict:

  """simple docstring"""




  return {
      i: [j for j in range(lowerCAmelCase__						) if i != j] for i in range(lowerCAmelCase__						)
  }


if __name__ == "__main__":
     import doctest

     doctest.testmod()





