


'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_   :      Any				    =      logging.get_logger(__name__)

a_   :      int				    =      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "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",
}
a_   :      List[Any]				    =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def 		_A			(lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:List[str]						)				->					Tuple:




     '''simple docstring'''



     for attribute in key.split('.'						):
          _a      =							getattr(lowerCAmelCase__			,  lowerCAmelCase__						)

     if weight_type is not None:
          _a      =							getattr(lowerCAmelCase__			,  lowerCAmelCase__						).shape
     else:
          _a      =							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      =							value
     elif weight_type == "weight_g":
          _a      =							value
     elif weight_type == "weight_v":
          _a      =							value
     elif weight_type == "bias":
          _a      =							value
     elif weight_type == "running_mean":
          _a      =							value
     elif weight_type == "running_var":
          _a      =							value
     elif weight_type == "num_batches_tracked":
          _a      =							value
     elif weight_type == "inv_freq":
          _a      =							value
     else:
          _a      =							value

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






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Dict						)				->					List[str]:




     '''simple docstring'''



     _a      =							[]
     _a      =							fairseq_model.state_dict()

     _a      =							hf_model.wavaveca_conformer.feature_extractor

     for name, value in fairseq_dict.items():
          _a      =							False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  hf_model.config.feat_extract_norm == 'group'			,  )
               _a      =							True
          else:
               for key, mapped_key in MAPPING.items():
                    _a      =							'wav2vec2_conformer.' + 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      =							True
                         if "*" in mapped_key:
                              _a      =							name.split(lowerCAmelCase__						)[0].split('.'						)[-2]
                              _a      =							mapped_key.replace('*'			,  lowerCAmelCase__						)
                         if "pos_bias_u" in name:
                              _a      =							None
                         elif "pos_bias_v" in name:
                              _a      =							None
                         elif "weight_g" in name:
                              _a      =							'weight_g'
                         elif "weight_v" in name:
                              _a      =							'weight_v'
                         elif "bias" in name:
                              _a      =							'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _a      =							'weight'
                         elif "running_mean" in name:
                              _a      =							'running_mean'
                         elif "inv_freq" in name:
                              _a      =							'inv_freq'
                         elif "running_var" in name:
                              _a      =							'running_var'
                         elif "num_batches_tracked" in name:
                              _a      =							'num_batches_tracked'
                         else:
                              _a      =							None
                         set_recursively(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
                    continue
          if not is_used:
               unused_weights.append(lowerCAmelCase__						)

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






def 		_A			(lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any						)				->					List[Any]:




     '''simple docstring'''



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






@torch.no_grad()
def 		_A			(lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:int=None			,  lowerCAmelCase__					:Any=None			,  lowerCAmelCase__					:Union[str, Any]=True						)				->					int:




     '''simple docstring'''



     if config_path is not None:
          _a      =							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase__			,  hidden_act='swish'						)
     else:
          _a      =							WavaVecaConformerConfig()

     if "rope" in checkpoint_path:
          _a      =							'rotary'

     if is_finetuned:
          if dict_path:
               _a      =							Dictionary.load(lowerCAmelCase__						)

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

               # fairseq has the <pad> and <s> switched
               _a      =							0
               _a      =							1
               with open(lowerCAmelCase__			,  'w'			,  encoding='utf-8'						) as vocab_handle:
                    json.dump(lowerCAmelCase__			,  lowerCAmelCase__						)
               _a      =							WavaVecaCTCTokenizer(
                   lowerCAmelCase__			,  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=lowerCAmelCase__			,  )
               _a      =							True if config.feat_extract_norm == 'layer' else False
               _a      =							WavaVecaFeatureExtractor(
                   feature_size=1			,  sampling_rate=1_60_00			,  padding_value=0			,  do_normalize=lowerCAmelCase__			,  return_attention_mask=lowerCAmelCase__			,  )
               _a      =							WavaVecaProcessor(feature_extractor=lowerCAmelCase__			,  tokenizer=lowerCAmelCase__						)
               processor.save_pretrained(lowerCAmelCase__						)

          _a      =							WavaVecaConformerForCTC(lowerCAmelCase__						)
     else:
          _a      =							WavaVecaConformerForPreTraining(lowerCAmelCase__						)

     if is_finetuned:
          _a ,				_a ,				_a      =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]			,  arg_overrides={'data': '/'.join(dict_path.split('/'						)[:-1]						)}						)
     else:
          _a      =							argparse.Namespace(task='audio_pretraining'						)
          _a      =							fairseq.tasks.setup_task(lowerCAmelCase__						)

          _a ,				_a ,				_a      =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,  task=lowerCAmelCase__						)

     _a      =							model[0].eval()

     recursively_load_weights(lowerCAmelCase__			,  lowerCAmelCase__			,  not is_finetuned						)

     hf_wavavec.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
 a_   :      Optional[Any]				    =      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"
 )
 a_   :      Union[str, Any]				    =      parser.parse_args()
 convert_wavaveca_conformer_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     assert (
         isinstance(lowerCAmelCase__			,  lowerCAmelCase__						) and number_of_steps > 0
     ), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
     if number_of_steps == 1:
          return 1
     _a ,				_a      =							1, 1
     for _ in range(number_of_steps - 1						):
          _a ,				_a      =							current + previous, current
     return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()

