



from itertools import count



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 50				)  ->     int:
 _snake_case    :  int					=					[1] * min_block_length

 for n in count(SCREAMING_SNAKE_CASE__				):
  fill_count_functions.append(1				)

  for block_length in range(SCREAMING_SNAKE_CASE__				,       n + 1				):
   for block_start in range(n - block_length				):
    fill_count_functions[n] += fill_count_functions[
        n - block_start - block_length - 1
    ]

   fill_count_functions[n] += 1

  if fill_count_functions[n] > 1_000_000:
   break

 return n


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





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()
a__    =	logging.get_logger(__name__)

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



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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Any				)  ->     Tuple:
 for attribute in key.split("""."""				):
  _snake_case    :  List[str]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  List[Any]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Union[str, Any]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Any					=					"""param"""

 if weight_type is not None and weight_type != "param":
  _snake_case    :  int					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				).shape
 elif weight_type is not None and weight_type == "param":
  _snake_case    :  Dict					=					hf_pointer
  for attribute in hf_param_name.split("""."""				):
   _snake_case    :  Tuple					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Any					=					shape_pointer.shape

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

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     str:
 _snake_case    :  List[str]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  List[str]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Optional[Any]					=					"""param"""

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

 _snake_case    :  Any					=					value if """lm_head""" in full_key else value[0]


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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Tuple:
 _snake_case    :  Optional[Any]					=					[]
 _snake_case    :  Tuple					=					fairseq_model.state_dict()

 _snake_case    :  List[str]					=					hf_model.wavaveca.feature_extractor

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

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



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



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]=None				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=None				,       SCREAMING_SNAKE_CASE__       :	int=True				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]=False				)  ->     Optional[Any]:
 if config_path is not None:
  _snake_case    :  List[Any]					=					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  Union[str, Any]					=					WavaVecaConfig()

 if is_seq_class:
  _snake_case    :  Union[str, Any]					=					read_txt_into_dict(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  str					=					idalabel
  _snake_case    :  Union[str, Any]					=					WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  List[str]					=					WavaVecaFeatureExtractor(
      feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
  feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__				)

 elif is_finetuned:
  if dict_path:
   _snake_case    :  Optional[Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)

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

   # fairseq has the <pad> and <s> switched
   _snake_case    :  Optional[int]					=					0
   _snake_case    :  Tuple					=					1
   with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as vocab_handle:
    json.dump(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Dict					=					WavaVecaCTCTokenizer(
       SCREAMING_SNAKE_CASE__				,       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=SCREAMING_SNAKE_CASE__				,       )
   _snake_case    :  Any					=					True if config.feat_extract_norm == """layer""" else False
   _snake_case    :  Optional[int]					=					WavaVecaFeatureExtractor(
       feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
   _snake_case    :  List[Any]					=					WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				,       tokenizer=SCREAMING_SNAKE_CASE__				)
   processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

  _snake_case    :  Tuple					=					WavaVecaForCTC(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  List[str]					=					WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__				)

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

  _snake_case	,    _snake_case	,    _snake_case    :  str					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,       task=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					model[0].eval()

 recursively_load_weights(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       not is_finetuned				)

 hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__				)


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

  a__    =	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,
  )
