


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()
__UpperCamelCase				:  Optional[int]      					=							logging.get_logger(__name__)

__UpperCamelCase				:  Dict      					=							{
    """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""",
}
__UpperCamelCase				:  int      					=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      Dict						=   {}
   with open(__lowercase			,       '''r'''       ) as file:
      for line_number, line in enumerate(__lowercase       ):
         UpperCAmelCase_							:      Dict						=   line.strip()
         if line:
            UpperCAmelCase_							:      Optional[Any]						=   line.split()
            UpperCAmelCase_							:      List[str]						=   line_number
            UpperCAmelCase_							:      Optional[Any]						=   words[0]
            UpperCAmelCase_							:      Union[str, Any]						=   value
   return result
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       ):

   for attribute in key.split('''.'''       ):
      UpperCAmelCase_							:      List[str]						=   getattr(__lowercase			,       __lowercase       )

   UpperCAmelCase_							:      List[Any]						=   None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__lowercase       ):
         UpperCAmelCase_							:      List[str]						=   PARAM_MAPPING[full_name.split('''.'''       )[-1]]
         UpperCAmelCase_							:      str						=   '''param'''

   if weight_type is not None and weight_type != "param":
      UpperCAmelCase_							:      Dict						=   getattr(__lowercase			,       __lowercase       ).shape
   elif weight_type is not None and weight_type == "param":
      UpperCAmelCase_							:      Tuple						=   hf_pointer
      for attribute in hf_param_name.split('''.'''       ):
         UpperCAmelCase_							:      Union[str, Any]						=   getattr(__lowercase			,       __lowercase       )
      UpperCAmelCase_							:      int						=   shape_pointer.shape

      # let's reduce dimension
      UpperCAmelCase_							:      Tuple						=   value[0]
   else:
      UpperCAmelCase_							:      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":
      UpperCAmelCase_							:      List[str]						=   value
   elif weight_type == "weight_g":
      UpperCAmelCase_							:      Optional[Any]						=   value
   elif weight_type == "weight_v":
      UpperCAmelCase_							:      Any						=   value
   elif weight_type == "bias":
      UpperCAmelCase_							:      List[str]						=   value
   elif weight_type == "param":
      for attribute in hf_param_name.split('''.'''       ):
         UpperCAmelCase_							:      str						=   getattr(__lowercase			,       __lowercase       )
      UpperCAmelCase_							:      str						=   value
   else:
      UpperCAmelCase_							:      List[Any]						=   value

   logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''       )
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Any						=   None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__lowercase       ):
         UpperCAmelCase_							:      Optional[Any]						=   PARAM_MAPPING[full_name.split('''.'''       )[-1]]
         UpperCAmelCase_							:      Any						=   '''param'''

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

   UpperCAmelCase_							:      List[Any]						=   value if '''lm_head''' in full_key else value[0]


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

   UpperCAmelCase_							:      List[Any]						=   hf_model.wavaveca.feature_extractor

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

   logger.warning(F'''Unused weights: {unused_weights}'''       )
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Tuple						=   full_name.split('''conv_layers.'''       )[-1]
   UpperCAmelCase_							:      List[str]						=   name.split('''.'''       )
   UpperCAmelCase_							:      int						=   int(items[0]       )
   UpperCAmelCase_							:      Dict						=   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.'''       )
         UpperCAmelCase_							:      Optional[Any]						=   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.'''       )
         UpperCAmelCase_							:      Any						=   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.'''       )
         UpperCAmelCase_							:      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:
         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.'''       )
         UpperCAmelCase_							:      Tuple						=   value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
   else:
      unused_weights.append(__lowercase       )
@torch.no_grad()
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       __lowercase=None			,       __lowercase=True			,       __lowercase=False       ):

   if config_path is not None:
      UpperCAmelCase_							:      Any						=   WavaVecaConfig.from_pretrained(__lowercase       )
   else:
      UpperCAmelCase_							:      int						=   WavaVecaConfig()

   if is_seq_class:
      UpperCAmelCase_							:      str						=   read_txt_into_dict(__lowercase       )
      UpperCAmelCase_							:      Any						=   idalabel
      UpperCAmelCase_							:      Union[str, Any]						=   WavaVecaForSequenceClassification(__lowercase       )
      UpperCAmelCase_							:      Dict						=   WavaVecaFeatureExtractor(
          feature_size=1			,       sampling_rate=1_6_0_0_0			,       padding_value=0			,       do_normalize=__lowercase			,       return_attention_mask=__lowercase			,       )
      feature_extractor.save_pretrained(__lowercase       )

   elif is_finetuned:
      if dict_path:
         UpperCAmelCase_							:      Any						=   Dictionary.load(__lowercase       )

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

         # fairseq has the <pad> and <s> switched
         UpperCAmelCase_							:      Optional[Any]						=   0
         UpperCAmelCase_							:      Any						=   1
         with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as vocab_handle:
            json.dump(__lowercase			,       __lowercase       )
         UpperCAmelCase_							:      Optional[Any]						=   WavaVecaCTCTokenizer(
             __lowercase			,       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=__lowercase			,       )
         UpperCAmelCase_							:      List[str]						=   True if config.feat_extract_norm == '''layer''' else False
         UpperCAmelCase_							:      List[str]						=   WavaVecaFeatureExtractor(
             feature_size=1			,       sampling_rate=1_6_0_0_0			,       padding_value=0			,       do_normalize=__lowercase			,       return_attention_mask=__lowercase			,       )
         UpperCAmelCase_							:      List[str]						=   WavaVecaProcessor(feature_extractor=__lowercase			,       tokenizer=__lowercase       )
         processor.save_pretrained(__lowercase       )

      UpperCAmelCase_							:      Tuple						=   WavaVecaForCTC(__lowercase       )
   else:
      UpperCAmelCase_							:      Dict						=   WavaVecaForPreTraining(__lowercase       )

   if is_finetuned or is_seq_class:
      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]			,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
   else:
      UpperCAmelCase_							:      List[str]						=   argparse.Namespace(task='''audio_pretraining'''       )
      UpperCAmelCase_							:      List[str]						=   fairseq.tasks.setup_task(__lowercase       )

      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,       task=__lowercase       )

   UpperCAmelCase_							:      List[Any]						=   model[0].eval()

   recursively_load_weights(__lowercase			,       __lowercase			,       not is_finetuned       )

   hf_wavavec.save_pretrained(__lowercase       )


if __name__ == "__main__":
       __UpperCamelCase				:  Union[str, 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'
       )
       parser.add_argument(
           '--is_seq_class',
           action='store_true',
           help='Whether the model to convert is a fine-tuned sequence classification model or not',
       )
       __UpperCamelCase				:  Tuple      					=							parser.parse_args()

       __UpperCamelCase				:  Optional[int]      					=							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,
       )







import fire

from utils import calculate_rouge, save_json
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       **__lowercase       ):
   UpperCAmelCase_							:      Tuple						=   [x.strip() for x in open(__lowercase       ).readlines()]
   UpperCAmelCase_							:      Dict						=   [x.strip() for x in open(__lowercase       ).readlines()][: len(__lowercase       )]
   UpperCAmelCase_							:      int						=   calculate_rouge(__lowercase			,       __lowercase			,       **__lowercase       )
   if save_path is not None:
      save_json(__lowercase			,       __lowercase			,       indent=__lowercase       )
   return metrics  # these print nicely


if __name__ == "__main__":
       fire.Fire(calculate_rouge_path)