






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()
snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """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""",
}
snake_case    				=		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    {}
  with open(lowercase						,     "r"				) as file:
    for line_number, line in enumerate(lowercase				):
      SCREAMING_SNAKE_CASE       :      Any							=    line.strip()
      if line:
        SCREAMING_SNAKE_CASE       :      int							=    line.split()
        SCREAMING_SNAKE_CASE       :      int							=    line_number
        SCREAMING_SNAKE_CASE       :      List[str]							=    words[0]
        SCREAMING_SNAKE_CASE       :      int							=    value
  return result


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  for attribute in key.split("."				):
    SCREAMING_SNAKE_CASE       :      Dict							=    getattr(lowercase						,     lowercase				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowercase				):
      SCREAMING_SNAKE_CASE       :      int							=    PARAM_MAPPING[full_name.split("."				)[-1]]
      SCREAMING_SNAKE_CASE       :      Tuple							=    "param"

  if weight_type is not None and weight_type != "param":
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    getattr(lowercase						,     lowercase				).shape
  elif weight_type is not None and weight_type == "param":
    SCREAMING_SNAKE_CASE       :      int							=    hf_pointer
    for attribute in hf_param_name.split("."				):
      SCREAMING_SNAKE_CASE       :      Dict							=    getattr(lowercase						,     lowercase				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    shape_pointer.shape

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

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


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Any							=    None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowercase				):
      SCREAMING_SNAKE_CASE       :      str							=    PARAM_MAPPING[full_name.split("."				)[-1]]
      SCREAMING_SNAKE_CASE       :      str							=    "param"

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

  SCREAMING_SNAKE_CASE       :      Dict							=    value if "lm_head" in full_key else value[0]


snake_case    				=		{
    """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   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=None						,     lowercase=None				):

  """simple docstring"""






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


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
  SCREAMING_SNAKE_CASE       :      List[str]							=    fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE       :      Tuple							=    hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE       :      str							=    False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase						,     lowercase						,     lowercase						,     lowercase						,     hf_model.config.feat_extract_norm == "group"						,     )
      SCREAMING_SNAKE_CASE       :      Tuple							=    True
    else:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    load_wavaveca_layer(lowercase						,     lowercase						,     lowercase				)
    if not is_used:
      unused_weights.append(lowercase				)

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


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    full_name.split("conv_layers."				)[-1]
  SCREAMING_SNAKE_CASE       :      Dict							=    name.split("."				)
  SCREAMING_SNAKE_CASE       :      Dict							=    int(items[0]				)
  SCREAMING_SNAKE_CASE       :      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.'''				)
      SCREAMING_SNAKE_CASE       :      List[str]							=    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.'''				)
      SCREAMING_SNAKE_CASE       :      Optional[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.'''				)
      SCREAMING_SNAKE_CASE       :      List[str]							=    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.'''				)
      SCREAMING_SNAKE_CASE       :      List[Any]							=    value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
  else:
    unused_weights.append(lowercase				)


@torch.no_grad()
def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=None						,     lowercase=None						,     lowercase=True						,     lowercase=False				):

  """simple docstring"""






  if config_path is not None:
    SCREAMING_SNAKE_CASE       :      Any							=    WavaVecaConfig.from_pretrained(lowercase				)
  else:
    SCREAMING_SNAKE_CASE       :      List[str]							=    WavaVecaConfig()

  if is_seq_class:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    read_txt_into_dict(lowercase				)
    SCREAMING_SNAKE_CASE       :      Dict							=    idalabel
    SCREAMING_SNAKE_CASE       :      str							=    WavaVecaForSequenceClassification(lowercase				)
    SCREAMING_SNAKE_CASE       :      Any							=    WavaVecaFeatureExtractor(
        feature_size=1						,     sampling_rate=16000						,     padding_value=0						,     do_normalize=lowercase						,     return_attention_mask=lowercase						,     )
    feature_extractor.save_pretrained(lowercase				)

  elif is_finetuned:
    if dict_path:
      SCREAMING_SNAKE_CASE       :      str							=    Dictionary.load(lowercase				)

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

      # fairseq has the <pad> and <s> switched
      SCREAMING_SNAKE_CASE       :      List[Any]							=    0
      SCREAMING_SNAKE_CASE       :      Tuple							=    1
      with open(lowercase						,     "w"						,     encoding="utf-8"				) as vocab_handle:
        json.dump(lowercase						,     lowercase				)
      SCREAMING_SNAKE_CASE       :      int							=    WavaVecaCTCTokenizer(
          lowercase						,     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=lowercase						,     )
      SCREAMING_SNAKE_CASE       :      Any							=    True if config.feat_extract_norm == "layer" else False
      SCREAMING_SNAKE_CASE       :      int							=    WavaVecaFeatureExtractor(
          feature_size=1						,     sampling_rate=16000						,     padding_value=0						,     do_normalize=lowercase						,     return_attention_mask=lowercase						,     )
      SCREAMING_SNAKE_CASE       :      str							=    WavaVecaProcessor(feature_extractor=lowercase						,     tokenizer=lowercase				)
      processor.save_pretrained(lowercase				)

    SCREAMING_SNAKE_CASE       :      int							=    WavaVecaForCTC(lowercase				)
  else:
    SCREAMING_SNAKE_CASE       :      List[Any]							=    WavaVecaForPreTraining(lowercase				)

  if is_finetuned or is_seq_class:
    SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]						,     arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
  else:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    argparse.Namespace(task="audio_pretraining"				)
    SCREAMING_SNAKE_CASE       :      Dict							=    fairseq.tasks.setup_task(lowercase				)

    SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Tuple							=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,     task=lowercase				)

  SCREAMING_SNAKE_CASE       :      List[Any]							=    model[0].eval()

  recursively_load_weights(lowercase						,     lowercase						,     not is_finetuned				)

  hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
   snake_case    				=		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""",
   )
   snake_case    				=		parser.parse_args()

   snake_case    				=		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,
   )














from __future__ import annotations

from cmath import sqrt


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  if a == 0:
    raise ValueError("Coefficient 'a' must not be zero."				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    b * b - 4 * a * c

  SCREAMING_SNAKE_CASE       :      str							=    (-b + sqrt(lowercase				)) / (2 * a)
  SCREAMING_SNAKE_CASE       :      Any							=    (-b - sqrt(lowercase				)) / (2 * a)

  return (
      root_a.real if not root_a.imag else root_a,
      root_a.real if not root_a.imag else root_a,
  )


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    quadratic_roots(a=5						,     b=6						,     c=1				)
  print(F'''The solutions are: {solutiona} and {solutiona}'''				)


if __name__ == "__main__":
   main()






