
'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case:  Dict								=			logging.get_logger(__name__)

__snake_case:  Union[str, Any]								=			{
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






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







  for attribute in key.split("""."""   ):
    a_      :	Optional[int]      =							getattr(A_     ,		A_   )

  if weight_type is not None:
    a_      :	Any      =							getattr(A_     ,		A_   ).shape
  else:
    a_      :	List[str]      =							hf_pointer.shape

  assert hf_shape == value.shape, (
      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_      :	int      =							value
  elif weight_type == "weight_g":
    a_      :	Tuple      =							value
  elif weight_type == "weight_v":
    a_      :	str      =							value
  elif weight_type == "bias":
    a_      :	Any      =							value
  else:
    a_      :	List[str]      =							value

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






def 	_snake_case						(      A_       :    Dict     ,		A_       :    List[str]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Union[str, Any]      =							[]
  a_      :	Union[str, Any]      =							fairseq_model.state_dict()

  a_      :	Any      =							hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    a_      :	Dict      =							False
    if "conv_layers" in name:
      load_conv_layer(
          A_     ,		A_     ,		A_     ,		A_     ,		hf_model.config.feat_extract_norm == """group"""     ,		)
      a_      :	Dict      =							True
    else:
      for key, mapped_key in MAPPING.items():
        a_      :	List[str]      =							"""sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

        if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
          a_      :	int      =							True
          if "*" in mapped_key:
            a_      :	Optional[int]      =							name.split(A_   )[0].split("""."""   )[-2]
            a_      :	List[Any]      =							mapped_key.replace("""*"""     ,		A_   )
          if "weight_g" in name:
            a_      :	List[str]      =							"""weight_g"""
          elif "weight_v" in name:
            a_      :	Union[str, Any]      =							"""weight_v"""
          elif "weight" in name:
            a_      :	Any      =							"""weight"""
          elif "bias" in name:
            a_      :	Optional[Any]      =							"""bias"""
          else:
            a_      :	List[Any]      =							None
          set_recursively(A_     ,		A_     ,		A_     ,		A_     ,		A_   )
        continue
    if not is_used:
      unused_weights.append(A_   )

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






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







  a_      :	Union[str, Any]      =							full_name.split("""conv_layers."""   )[-1]
  a_      :	List[Any]      =							name.split("""."""   )
  a_      :	str      =							int(items[0]   )
  a_      :	str      =							int(items[1]   )

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
      )
      a_      :	Optional[int]      =							value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''   )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
      )
      a_      :	List[str]      =							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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
          " found."
      )
      a_      :	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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
      )
      a_      :	Optional[int]      =							value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
  else:
    unused_weights.append(A_   )






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







  a_      :	Optional[Any]      =							SEWConfig()
  if is_finetuned:
    a_      :	List[str]      =							model.wav_encoder.wav_model.cfg
  else:
    a_      :	Any      =							model.cfg

  a_      :	int      =							fs_config.conv_bias
  a_      :	Optional[int]      =							eval(fs_config.conv_feature_layers   )
  a_      :	Tuple      =							[x[0] for x in conv_layers]
  a_      :	Any      =							[x[1] for x in conv_layers]
  a_      :	Any      =							[x[2] for x in conv_layers]
  a_      :	str      =							"""gelu"""
  a_      :	Dict      =							"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  a_      :	List[Any]      =							0.0
  a_      :	Tuple      =							fs_config.activation_fn.name
  a_      :	List[str]      =							fs_config.encoder_embed_dim
  a_      :	int      =							0.02
  a_      :	str      =							fs_config.encoder_ffn_embed_dim
  a_      :	Any      =							1E-5
  a_      :	List[Any]      =							fs_config.encoder_layerdrop
  a_      :	int      =							fs_config.encoder_attention_heads
  a_      :	Optional[int]      =							fs_config.conv_pos_groups
  a_      :	Any      =							fs_config.conv_pos
  a_      :	Tuple      =							len(A_   )
  a_      :	Tuple      =							fs_config.encoder_layers
  a_      :	Optional[int]      =							fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    a_      :	Tuple      =							model.cfg
    a_      :	int      =							fs_config.final_dropout
    a_      :	str      =							fs_config.layerdrop
  a_      :	Optional[int]      =							fs_config.activation_dropout
  a_      :	List[str]      =							fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  a_      :	Optional[Any]      =							fs_config.attention_dropout
  a_      :	Union[str, Any]      =							fs_config.dropout_input
  a_      :	Union[str, Any]      =							fs_config.dropout
  a_      :	Union[str, Any]      =							fs_config.mask_channel_length
  a_      :	Tuple      =							fs_config.mask_channel_prob
  a_      :	str      =							fs_config.mask_length
  a_      :	Optional[Any]      =							fs_config.mask_prob

  a_      :	Optional[int]      =							"""Wav2Vec2FeatureExtractor"""
  a_      :	List[Any]      =							"""Wav2Vec2CTCTokenizer"""

  return config






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







  if is_finetuned:
    a_      , a_      , a_      :	Any      =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]     ,		arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
  else:
    a_      , a_      , a_      :	int      =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

  if config_path is not None:
    a_      :	Tuple      =							SEWConfig.from_pretrained(A_   )
  else:
    a_      :	Any      =							convert_config(model[0]     ,		A_   )
  a_      :	Union[str, Any]      =							model[0].eval()

  a_      :	str      =							True if config.feat_extract_norm == """layer""" else False
  a_      :	Dict      =							WavaVecaFeatureExtractor(
      feature_size=1     ,		sampling_rate=1_6000     ,		padding_value=0     ,		do_normalize=A_     ,		return_attention_mask=A_     ,		)

  if 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_      :	Dict      =							target_dict.pad_index
      a_      :	List[Any]      =							target_dict.bos_index
      a_      :	List[str]      =							target_dict.pad_index
      a_      :	Union[str, Any]      =							target_dict.bos_index
      a_      :	Dict      =							target_dict.eos_index
      a_      :	List[str]      =							len(target_dict.symbols   )
      a_      :	List[Any]      =							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_   )
      with open(A_     ,		"""w"""     ,		encoding="""utf-8"""   ) as vocab_handle:
        json.dump(target_dict.indices     ,		A_   )
      a_      :	str      =							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_      :	Optional[Any]      =							WavaVecaProcessor(feature_extractor=A_     ,		tokenizer=A_   )
      processor.save_pretrained(A_   )

    a_      :	Tuple      =							SEWForCTC(A_   )
  else:
    a_      :	Optional[int]      =							SEWModel(A_   )
    feature_extractor.save_pretrained(A_   )

  recursively_load_weights(A_     ,		A_     ,		A_   )

  hf_model.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(
        "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    __snake_case:  Optional[int]								=			parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )




'''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() = }""")


