
'''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()
__snake_case:  List[Any]								=			logging.get_logger(__name__)

__snake_case:  Optional[int]								=			{
    "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:  int								=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]






def 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    int     ,		A_       :    Tuple     ,		A_       :    int     ,		A_       :    Union[str, Any]     ,		A_       :    Tuple   ):
  """simple docstring"""







  for attribute in key.split("""."""   ):
    a_      :	Tuple      =							getattr(A_     ,		A_   )

  a_      :	List[Any]      =							None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(A_   ):
      a_      :	Tuple      =							PARAM_MAPPING[full_name.split("""."""   )[-1]]
      a_      :	Any      =							"""param"""

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

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

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






def 	_snake_case						(      A_       :    Optional[Any]     ,		A_       :    Optional[Any]     ,		A_       :    str     ,		A_       :    Optional[int]     ,		A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(A_   ):
      a_      :	Tuple      =							PARAM_MAPPING[full_name.split("""."""   )[-1]]
      a_      :	Dict      =							"""param"""

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

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


__snake_case:  Tuple								=			{
    "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 	_snake_case						(      A_       :    Any     ,		A_       :    Optional[Any]     ,		A_       :    Dict=None     ,		A_       :    Optional[int]=None   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Tuple     ,		A_       :    Dict     ,		A_       :    Optional[Any]   ):
  """simple docstring"""







  a_      :	Any      =							[]
  a_      :	Tuple      =							fairseq_model.state_dict()

  a_      :	Any      =							hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    a_      :	str      =							False
    if "conv_layers" in name:
      load_conv_layer(
          A_     ,		A_     ,		A_     ,		A_     ,		hf_model.config.feat_extract_norm == """group"""     ,		)
      a_      :	Any      =							True
    else:
      a_      :	int      =							load_wavaveca_layer(A_     ,		A_     ,		A_   )
    if not is_used:
      unused_weights.append(A_   )

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






def 	_snake_case						(      A_       :    Dict     ,		A_       :    Optional[int]     ,		A_       :    Optional[int]     ,		A_       :    Union[str, Any]     ,		A_       :    str   ):
  """simple docstring"""







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






@torch.no_grad()
def 	_snake_case						(      A_       :    List[str]     ,		A_       :    int     ,		A_       :    Any=None     ,		A_       :    List[str]=None     ,		A_       :    Optional[int]=True     ,		A_       :    str=False   ):
  """simple docstring"""







  if config_path is not None:
    a_      :	Tuple      =							WavaVecaConfig.from_pretrained(A_   )
  else:
    a_      :	Dict      =							WavaVecaConfig()

  if is_seq_class:
    a_      :	Optional[Any]      =							read_txt_into_dict(A_   )
    a_      :	Any      =							idalabel
    a_      :	List[Any]      =							WavaVecaForSequenceClassification(A_   )
    a_      :	Optional[Any]      =							WavaVecaFeatureExtractor(
        feature_size=1     ,		sampling_rate=1_6000     ,		padding_value=0     ,		do_normalize=A_     ,		return_attention_mask=A_     ,		)
    feature_extractor.save_pretrained(A_   )

  elif is_finetuned:
    if dict_path:
      a_      :	Optional[int]      =							Dictionary.load(A_   )

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

      # fairseq has the <pad> and <s> switched
      a_      :	str      =							0
      a_      :	Any      =							1
      with open(A_     ,		"""w"""     ,		encoding="""utf-8"""   ) as vocab_handle:
        json.dump(A_     ,		A_   )
      a_      :	List[Any]      =							WavaVecaCTCTokenizer(
          A_     ,		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=A_     ,		)
      a_      :	Dict      =							True if config.feat_extract_norm == """layer""" else False
      a_      :	Any      =							WavaVecaFeatureExtractor(
          feature_size=1     ,		sampling_rate=1_6000     ,		padding_value=0     ,		do_normalize=A_     ,		return_attention_mask=A_     ,		)
      a_      :	Union[str, Any]      =							WavaVecaProcessor(feature_extractor=A_     ,		tokenizer=A_   )
      processor.save_pretrained(A_   )

    a_      :	List[str]      =							WavaVecaForCTC(A_   )
  else:
    a_      :	Union[str, Any]      =							WavaVecaForPreTraining(A_   )

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

    a_      , a_      , a_      :	Optional[int]      =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,		task=A_   )

  a_      :	Any      =							model[0].eval()

  recursively_load_weights(A_     ,		A_     ,		not is_finetuned   )

  hf_wavavec.save_pretrained(A_   )


if __name__ == "__main__":
    __snake_case:  Dict								=			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:  Any								=			parser.parse_args()

    __snake_case:  Optional[int]								=			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'''

from math import factorial

__snake_case:  List[Any]								=			{str(d): factorial(d) for d in range(10)}






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  return sum(DIGIT_FACTORIAL[d] for d in str(A_   )   )






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Union[str, Any]      =							7 * factorial(9   ) + 1
  return sum(i for i in range(3     ,		A_   ) if sum_of_digit_factorial(A_   ) == i   )


if __name__ == "__main__":
    print(F"""{solution() = }""")


