


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

SCREAMING_SNAKE_CASE						:List[str]			=  {
    '''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''',
}
SCREAMING_SNAKE_CASE						:Any			=  [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any		,    lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Any				)->Any:
   '''simple docstring'''






   for attribute in key.split("."				):
      snake_case_            =       getattr(lowerCAmelCase_		,    lowerCAmelCase_				)

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

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Optional[int]				)->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       []
   snake_case_            =       fairseq_model.state_dict()

   snake_case_            =       hf_model.wavaveca_conformer.feature_extractor

   for name, value in fairseq_dict.items():
      snake_case_            =       False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    hf_model.config.feat_extract_norm == "group"		,    )
         snake_case_            =       True
      else:
         for key, mapped_key in MAPPING.items():
            snake_case_            =       "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]:
               snake_case_            =       True
               if "*" in mapped_key:
                  snake_case_            =       name.split(lowerCAmelCase_				)[0].split("."				)[-2]
                  snake_case_            =       mapped_key.replace("*"		,    lowerCAmelCase_				)
               if "pos_bias_u" in name:
                  snake_case_            =       None
               elif "pos_bias_v" in name:
                  snake_case_            =       None
               elif "weight_g" in name:
                  snake_case_            =       "weight_g"
               elif "weight_v" in name:
                  snake_case_            =       "weight_v"
               elif "bias" in name:
                  snake_case_            =       "bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  snake_case_            =       "weight"
               elif "running_mean" in name:
                  snake_case_            =       "running_mean"
               elif "inv_freq" in name:
                  snake_case_            =       "inv_freq"
               elif "running_var" in name:
                  snake_case_            =       "running_var"
               elif "num_batches_tracked" in name:
                  snake_case_            =       "num_batches_tracked"
               else:
                  snake_case_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:Any				)->Tuple:
   '''simple docstring'''






   snake_case_            =       full_name.split("conv_layers."				)[-1]
   snake_case_            =       name.split("."				)
   snake_case_            =       int(items[0]				)
   snake_case_            =       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_            =       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_            =       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_            =       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_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:int=None		,    lowerCAmelCase_				:Union[str, Any]=None		,    lowerCAmelCase_				:Optional[int]=True				)->int:
   '''simple docstring'''






   if config_path is not None:
      snake_case_            =       WavaVecaConformerConfig.from_pretrained(lowerCAmelCase_		,    hidden_act="swish"				)
   else:
      snake_case_            =       WavaVecaConformerConfig()

   if "rope" in checkpoint_path:
      snake_case_            =       "rotary"

   if is_finetuned:
      if dict_path:
         snake_case_            =       Dictionary.load(lowerCAmelCase_				)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         snake_case_            =       target_dict.pad_index
         snake_case_            =       target_dict.bos_index
         snake_case_            =       target_dict.eos_index
         snake_case_            =       len(target_dict.symbols				)
         snake_case_            =       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_				)
         snake_case_            =       target_dict.indices

         # fairseq has the <pad> and <s> switched
         snake_case_            =       0
         snake_case_            =       1
         with open(lowerCAmelCase_		,    "w"		,    encoding="utf-8"				) as vocab_handle:
            json.dump(lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       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_		,    )
         snake_case_            =       True if config.feat_extract_norm == "layer" else False
         snake_case_            =       WavaVecaFeatureExtractor(
             feature_size=1		,    sampling_rate=16_000		,    padding_value=0		,    do_normalize=lowerCAmelCase_		,    return_attention_mask=lowerCAmelCase_		,    )
         snake_case_            =       WavaVecaProcessor(feature_extractor=lowerCAmelCase_		,    tokenizer=lowerCAmelCase_				)
         processor.save_pretrained(lowerCAmelCase_				)

      snake_case_            =       WavaVecaConformerForCTC(lowerCAmelCase_				)
   else:
      snake_case_            =       WavaVecaConformerForPreTraining(lowerCAmelCase_				)

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

      snake_case_     ,     snake_case_     ,     snake_case_            =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,    task=lowerCAmelCase_				)

   snake_case_            =       model[0].eval()

   recursively_load_weights(lowerCAmelCase_		,    lowerCAmelCase_		,    not is_finetuned				)

   hf_wavavec.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:List[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'''
   )
   SCREAMING_SNAKE_CASE						:Tuple			=  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
   )








def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->str:
   '''simple docstring'''






   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				):
      raise ValueError("iterations must be defined as integers"				)
   if not isinstance(lowerCAmelCase_		,    lowerCAmelCase_				) or not number >= 1:
      raise ValueError(
          "starting number must be\n                         and integer and be more than 0"				)
   if not iterations >= 1:
      raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

   snake_case_            =       ""
   while number <= iterations:
      if number % 3 == 0:
         out += "Fizz"
      if number % 5 == 0:
         out += "Buzz"
      if 0 not in (number % 3, number % 5):
         out += str(lowerCAmelCase_				)

      # print(out)
      number += 1
      out += " "
   return out


if __name__ == "__main__":
   import doctest

   doctest.testmod()




