






'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: Optional[Any]    ,   a_							: Union[str, Any]    ,   a_							: int    ,   a_							: Optional[Any]	)       ->				Union[str, Any]:





      """simple docstring"""

      for attribute in key.split("""."""	):
            UpperCamelCase_				:   Dict					    =							getattr(a_    ,   a_	)

      if weight_type is not None:
            UpperCamelCase_				:   Union[str, Any]					    =							getattr(a_    ,   a_	).shape
      else:
            UpperCamelCase_				:   List[Any]					    =							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":
            UpperCamelCase_				:   List[Any]					    =							value
      elif weight_type == "weight_g":
            UpperCamelCase_				:   Union[str, Any]					    =							value
      elif weight_type == "weight_v":
            UpperCamelCase_				:   List[str]					    =							value
      elif weight_type == "bias":
            UpperCamelCase_				:   Tuple					    =							value
      else:
            UpperCamelCase_				:   Union[str, Any]					    =							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[Any]    ,   a_							: List[Any]	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							[]
      UpperCamelCase_				:   Any					    =							fairseq_model.state_dict()

      UpperCamelCase_				:   List[str]					    =							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCamelCase_				:   Union[str, Any]					    =							False
            if "conv_layers" in name:
                  load_conv_layer(
                      a_    ,   a_    ,   a_    ,   a_    ,   hf_model.config.feat_extract_norm == """group"""    ,   )
                  UpperCamelCase_				:   Tuple					    =							True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCamelCase_				:   Optional[int]					    =							"""hubert.""" + 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] and not is_finetuned):
                              UpperCamelCase_				:   Tuple					    =							True
                              if "*" in mapped_key:
                                    UpperCamelCase_				:   Union[str, Any]					    =							name.split(a_	)[0].split("""."""	)[-2]
                                    UpperCamelCase_				:   Dict					    =							mapped_key.replace("""*"""    ,   a_	)
                              if "weight_g" in name:
                                    UpperCamelCase_				:   Tuple					    =							"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCamelCase_				:   str					    =							"""weight_v"""
                              elif "weight" in name:
                                    UpperCamelCase_				:   str					    =							"""weight"""
                              elif "bias" in name:
                                    UpperCamelCase_				:   List[Any]					    =							"""bias"""
                              else:
                                    UpperCamelCase_				:   int					    =							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_							: str    ,   a_							: Tuple    ,   a_							: str    ,   a_							: Optional[int]    ,   a_							: List[Any]	)       ->				Any:





      """simple docstring"""

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





      """simple docstring"""

      if config_path is not None:
            UpperCamelCase_				:   Optional[int]					    =							HubertConfig.from_pretrained(a_	)
      else:
            UpperCamelCase_				:   Union[str, Any]					    =							HubertConfig()

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

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCamelCase_				:   Any					    =							target_dict.pad_index
                  UpperCamelCase_				:   Union[str, Any]					    =							target_dict.bos_index
                  UpperCamelCase_				:   List[Any]					    =							target_dict.eos_index
                  UpperCamelCase_				:   List[Any]					    =							len(target_dict.symbols	)
                  UpperCamelCase_				:   str					    =							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_	)
                  UpperCamelCase_				:   int					    =							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_    ,   )
                  UpperCamelCase_				:   Dict					    =							True if config.feat_extract_norm == """layer""" else False
                  UpperCamelCase_				:   List[Any]					    =							WavaVecaFeatureExtractor(
                      feature_size=1    ,   sampling_rate=16_000    ,   padding_value=0    ,   do_normalize=a_    ,   return_attention_mask=a_    ,   )
                  UpperCamelCase_				:   str					    =							WavaVecaProcessor(feature_extractor=a_    ,   tokenizer=a_	)
                  processor.save_pretrained(a_	)

            UpperCamelCase_				:   Optional[Any]					    =							HubertForCTC(a_	)
      else:
            UpperCamelCase_				:   Union[str, Any]					    =							HubertModel(a_	)

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

      UpperCamelCase_				:   int					    =							model[0].eval()

      recursively_load_weights(a_    ,   a_    ,   a_	)

      hf_wavavec.save_pretrained(a_	)


if __name__ == "__main__":
       UpperCamelCase							=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"
       )
       UpperCamelCase							=parser.parse_args()
       convert_hubert_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )














'''simple docstring'''

from itertools import count



def 							snake_case					(     a_							: int = 50	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							[1] * min_block_length

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

            for block_length in range(a_    ,   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() = }")






