



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

a_    :  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',
}
a_    :  Optional[Any]						  =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      for attribute in key.split("."			):
            lowerCamelCase             =		getattr(UpperCAmelCase__ , UpperCAmelCase__			)

      if weight_type is not None:
            lowerCamelCase             =		getattr(UpperCAmelCase__ , UpperCAmelCase__			).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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """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(
                      UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , 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(UpperCAmelCase__			)[0].split("."			)[-2]
                                    lowerCamelCase             =		mapped_key.replace("*" , UpperCAmelCase__			)
                              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(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
                        continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase__			)

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

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """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(UpperCAmelCase__			)

@torch.no_grad()
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=True			):


      """simple docstring"""



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

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

      if is_finetuned:
            if dict_path:
                  lowerCamelCase             =		Dictionary.load(UpperCAmelCase__			)

                  # 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(UpperCAmelCase__ , "vocab.json"			)
                  if not os.path.isdir(UpperCAmelCase__			):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase__			)			)
                        return
                  os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__			)
                  lowerCamelCase             =		target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  lowerCamelCase             =		0
                  lowerCamelCase             =		1
                  with open(UpperCAmelCase__ , "w" , encoding="utf-8"			) as vocab_handle:
                        json.dump(UpperCAmelCase__ , UpperCAmelCase__			)
                  lowerCamelCase             =		WavaVecaCTCTokenizer(
                      UpperCAmelCase__ , 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=UpperCAmelCase__ , )
                  lowerCamelCase             =		True if config.feat_extract_norm == "layer" else False
                  lowerCamelCase             =		WavaVecaFeatureExtractor(
                      feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , )
                  lowerCamelCase             =		WavaVecaProcessor(feature_extractor=UpperCAmelCase__ , tokenizer=UpperCAmelCase__			)
                  processor.save_pretrained(UpperCAmelCase__			)

            lowerCamelCase             =		WavaVecaConformerForCTC(UpperCAmelCase__			)
      else:
            lowerCamelCase             =		WavaVecaConformerForPreTraining(UpperCAmelCase__			)

      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(UpperCAmelCase__			)

            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=UpperCAmelCase__			)

      lowerCamelCase             =		model[0].eval()

      recursively_load_weights(UpperCAmelCase__ , UpperCAmelCase__ , not is_finetuned			)

      hf_wavavec.save_pretrained(UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  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'
     )
     a_    :  Optional[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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    :  Tuple						  =		{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
         'UniSpeechForCTC',
         'UniSpeechForPreTraining',
         'UniSpeechForSequenceClassification',
         'UniSpeechModel',
         'UniSpeechPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech import (
              UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechForCTC,
              UniSpeechForPreTraining,
              UniSpeechForSequenceClassification,
              UniSpeechModel,
              UniSpeechPreTrainedModel,
          )

else:
     import sys

     a_    :  Optional[Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)