
"""simple docstring"""




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__      :	int          =    logging.get_logger(__name__)

A__      :	int          =    {
    """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__      :	Any          =    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



def 							a__ (				lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						{}
     with open(lowerCAmelCase       ,    "r"		) as file:
          for line_number, line in enumerate(lowerCAmelCase		):
               UpperCAmelCase__							: str 							=						line.strip()
               if line:
                    UpperCAmelCase__							: List[str] 							=						line.split()
                    UpperCAmelCase__							: str 							=						line_number
                    UpperCAmelCase__							: List[str] 							=						words[0]
                    UpperCAmelCase__							: Tuple 							=						value
     return result



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     for attribute in key.split("."		):
          UpperCAmelCase__							: Dict 							=						getattr(lowerCAmelCase       ,    lowerCAmelCase		)

     UpperCAmelCase__							: Optional[Any] 							=						None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase		):
               UpperCAmelCase__							: int 							=						PARAM_MAPPING[full_name.split("."		)[-1]]
               UpperCAmelCase__							: Tuple 							=						"param"

     if weight_type is not None and weight_type != "param":
          UpperCAmelCase__							: Tuple 							=						getattr(lowerCAmelCase       ,    lowerCAmelCase		).shape
     elif weight_type is not None and weight_type == "param":
          UpperCAmelCase__							: str 							=						hf_pointer
          for attribute in hf_param_name.split("."		):
               UpperCAmelCase__							: Optional[int] 							=						getattr(lowerCAmelCase       ,    lowerCAmelCase		)
          UpperCAmelCase__							: Optional[Any] 							=						shape_pointer.shape

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

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



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowerCAmelCase		):
               UpperCAmelCase__							: Tuple 							=						PARAM_MAPPING[full_name.split("."		)[-1]]
               UpperCAmelCase__							: List[Any] 							=						"param"

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

     UpperCAmelCase__							: Dict 							=						value if "lm_head" in full_key else value[0]


A__      :	Any          =    {
    """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 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       Any=None       ,    lowerCAmelCase	:       Union[str, Any]=None		):






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



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Any		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     UpperCAmelCase__							: Union[str, Any] 							=						fairseq_model.state_dict()

     UpperCAmelCase__							: str 							=						hf_model.wavaveca.feature_extractor

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

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



def 							a__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Any		):






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						full_name.split("conv_layers."		)[-1]
     UpperCAmelCase__							: Any 							=						name.split("."		)
     UpperCAmelCase__							: Optional[int] 							=						int(items[0]		)
     UpperCAmelCase__							: Any 							=						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__							: int 							=						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__							: 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."		)
               UpperCAmelCase__							: List[str] 							=						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	:       str       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Tuple=None       ,    lowerCAmelCase	:       str=None       ,    lowerCAmelCase	:       Optional[int]=True       ,    lowerCAmelCase	:       Any=False		):






     '''simple docstring'''
     if config_path is not None:
          UpperCAmelCase__							: Optional[int] 							=						WavaVecaConfig.from_pretrained(lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Any 							=						WavaVecaConfig()

     if is_seq_class:
          UpperCAmelCase__							: str 							=						read_txt_into_dict(lowerCAmelCase		)
          UpperCAmelCase__							: Optional[Any] 							=						idalabel
          UpperCAmelCase__							: List[str] 							=						WavaVecaForSequenceClassification(lowerCAmelCase		)
          UpperCAmelCase__							: Tuple 							=						WavaVecaFeatureExtractor(
              feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase       ,    return_attention_mask=lowerCAmelCase       ,    )
          feature_extractor.save_pretrained(lowerCAmelCase		)

     elif is_finetuned:
          if dict_path:
               UpperCAmelCase__							: Optional[Any] 							=						Dictionary.load(lowerCAmelCase		)

               # 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__							: List[str] 							=						target_dict.eos_index
               UpperCAmelCase__							: List[str] 							=						len(target_dict.symbols		)
               UpperCAmelCase__							: List[Any] 							=						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		)
               UpperCAmelCase__							: List[str] 							=						target_dict.indices

               # fairseq has the <pad> and <s> switched
               UpperCAmelCase__							: List[Any] 							=						0
               UpperCAmelCase__							: Union[str, Any] 							=						1
               with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as vocab_handle:
                    json.dump(lowerCAmelCase       ,    lowerCAmelCase		)
               UpperCAmelCase__							: Optional[int] 							=						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       ,    )
               UpperCAmelCase__							: List[str] 							=						True if config.feat_extract_norm == "layer" else False
               UpperCAmelCase__							: Union[str, Any] 							=						WavaVecaFeatureExtractor(
                   feature_size=1       ,    sampling_rate=1_6000       ,    padding_value=0       ,    do_normalize=lowerCAmelCase       ,    return_attention_mask=lowerCAmelCase       ,    )
               UpperCAmelCase__							: Optional[int] 							=						WavaVecaProcessor(feature_extractor=lowerCAmelCase       ,    tokenizer=lowerCAmelCase		)
               processor.save_pretrained(lowerCAmelCase		)

          UpperCAmelCase__							: Union[str, Any] 							=						WavaVecaForCTC(lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Any 							=						WavaVecaForPreTraining(lowerCAmelCase		)

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

          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Any 							=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,    task=lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						model[0].eval()

     recursively_load_weights(lowerCAmelCase       ,    lowerCAmelCase       ,    not is_finetuned		)

     hf_wavavec.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__      :	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""",
    )
    A__      :	Union[str, Any]          =    parser.parse_args()

    A__      :	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,
    )





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if a < 0:
          raise ValueError("Input value must be a positive integer"		)
     elif isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          raise TypeError("Input value must be a 'int' type"		)
     return bin(lowerCAmelCase		).count("1"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



