




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    :		Optional[Any]			   =       logging.get_logger(__name__)

lowerCamelCase    :		Union[str, Any]			   =       {
    "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    :		Any			   =       [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]     ,							lowercase     :	Union[str, Any]     ,							lowercase     :	int     ,							lowercase     :	List[Any]     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      for attribute in key.split('.'					):
            lowerCamelCase_							=   getattr(lowercase     ,							lowercase					)

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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]     ,							lowercase     :	str     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      lowerCamelCase_							=   []
      lowerCamelCase_							=   fairseq_model.state_dict()

      lowerCamelCase_							=   hf_model.wavaveca_conformer.feature_extractor

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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	int     ,							lowercase     :	Optional[int]     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   full_name.split('conv_layers.'					)[-1]
      lowerCamelCase_							=   name.split('.'					)
      lowerCamelCase_							=   int(items[0]					)
      lowerCamelCase_							=   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."""					)
                  lowerCamelCase_							=   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."""					)
                  lowerCamelCase_							=   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."""					)
                  lowerCamelCase_							=   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."""					)
                  lowerCamelCase_							=   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       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]     ,							lowercase     :	Dict     ,							lowercase     :	Optional[Any]=None     ,							lowercase     :	List[Any]=None     ,							lowercase     :	Optional[int]=True					):


      '''simple docstring'''



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

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

      if is_finetuned:
            if dict_path:
                  lowerCamelCase_							=   Dictionary.load(lowercase					)

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

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

            lowerCamelCase_							=   WavaVecaConformerForCTC(lowercase					)
      else:
            lowerCamelCase_							=   WavaVecaConformerForPreTraining(lowercase					)

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

            lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,							task=lowercase					)

      lowerCamelCase_							=   model[0].eval()

      recursively_load_weights(lowercase     ,							lowercase     ,							not is_finetuned					)

      hf_wavavec.save_pretrained(lowercase					)


if __name__ == "__main__":
 lowerCamelCase    :		List[str]			   =       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    :		List[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
 )









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



