





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()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     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",
}
__lowerCAmelCase							:     Optional[Any]		     =[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 						UpperCamelCase    (							_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : str						,	_lowerCamelCase : Dict						,	_lowerCamelCase : Union[str, Any]	):
    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 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int						,	_lowerCamelCase : str	):
    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 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : str						,	_lowerCamelCase : Any						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : List[Any]	):
    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 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Dict=None						,	_lowerCamelCase : Dict=None						,	_lowerCamelCase : Optional[int]=True	):
    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__":
  __lowerCAmelCase							:     int		     =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"
  )
  __lowerCAmelCase							:     int		     =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 						UpperCamelCase    (							_lowerCamelCase : int	):

    if not isinstance(_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      F"Input value of [number={number}] must be an integer"
        raise TypeError(_lowerCamelCase	)

    if number < 1:
        A__    =      F"Input value of [number={number}] must be > 0"
        raise ValueError(_lowerCamelCase	)

    A__    =      1

    for i in range(1						,	_lowerCamelCase	):
        current_number *= 4 * i - 2
        current_number //= i + 1

    return current_number


if __name__ == "__main__":
  import doctest

  doctest.testmod()



