

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      = logging.get_logger(__name__)

A      = {
    "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      = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A ,						__A			)						->					Any:



       '''simple docstring'''





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

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

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





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     []
       UpperCAmelCase__										=     fairseq_model.state_dict()

       UpperCAmelCase__										=     hf_model.wavaveca_conformer.feature_extractor

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

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





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A ,						__A			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     full_name.split("conv_layers."			)[-1]
       UpperCAmelCase__										=     name.split("."			)
       UpperCAmelCase__										=     int(items[0]			)
       UpperCAmelCase__										=     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__										=     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__										=     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__										=     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__										=     value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
       else:
              unused_weights.append(__A			)





@torch.no_grad()
def __UpperCAmelCase       (		__A ,						__A ,						__A=None ,						__A=None ,						__A=True			)						->					Optional[int]:



       '''simple docstring'''





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

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

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

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     UpperCAmelCase__										=     target_dict.pad_index
                     UpperCAmelCase__										=     target_dict.bos_index
                     UpperCAmelCase__										=     target_dict.eos_index
                     UpperCAmelCase__										=     len(target_dict.symbols			)
                     UpperCAmelCase__										=     os.path.join(__A ,						"vocab.json"			)
                     if not os.path.isdir(__A			):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__A			)			)
                            return
                     os.makedirs(__A ,						exist_ok=__A			)
                     UpperCAmelCase__										=     target_dict.indices

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

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

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

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

       UpperCAmelCase__										=     model[0].eval()

       recursively_load_weights(__A ,						__A ,						not is_finetuned			)

       hf_wavavec.save_pretrained(__A			)


if __name__ == "__main__":
    A      = 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      = 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
    )









import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     "ylacombe/bark-small"
         UpperCAmelCase__										=     tempfile.mkdtemp()
         UpperCAmelCase__										=     "en_speaker_1"
         UpperCAmelCase__										=     "This is a test string"
         UpperCAmelCase__										=     "speaker_embeddings_path.json"
         UpperCAmelCase__										=     "speaker_embeddings"






  def 			_UpperCAmelCase  (					self      :					List[str]		,						**_lowercase      :					str ):




         """simple docstring"""







         return AutoTokenizer.from_pretrained(self.checkpoint		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         shutil.rmtree(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         processor.save_pretrained(self.tmpdirname )
         UpperCAmelCase__										=     BarkProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer.get_vocab() )






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)
         processor.save_pretrained(
             self.tmpdirname		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						speaker_embeddings_directory=self.speaker_embeddings_directory		,						)

         UpperCAmelCase__										=     self.get_tokenizer(bos_token="(BOS)"		,						eos_token="(EOS)" )

         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             self.tmpdirname		,						self.speaker_embeddings_dict_path		,						bos_token="(BOS)"		,						eos_token="(EOS)"		,						)

         self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer_add_kwargs.get_vocab() )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)

         UpperCAmelCase__										=     35
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     8

         UpperCAmelCase__										=     {
             "semantic_prompt": np.ones(_lowercase ),
             "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
             "fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
         }

         # test providing already loaded voice_preset
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )

         UpperCAmelCase__										=     inputs["history_prompt"]
         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from npz file
         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						"file.npz" )
         np.savez(_lowercase		,						**_lowercase )
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )
         UpperCAmelCase__										=     inputs["history_prompt"]

         for key in voice_preset:
                self.assertListEqual(voice_preset[key].tolist()		,						processed_voice_preset.get(_lowercase		,						np.array([] ) ).tolist() )

         # test loading voice preset from the hub
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=self.voice_preset )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         UpperCAmelCase__										=     processor(text=self.input_string )

         UpperCAmelCase__										=     tokenizer(
             self.input_string		,						padding="max_length"		,						max_length=2_56		,						add_special_tokens=_lowercase		,						return_attention_mask=_lowercase		,						return_token_type_ids=_lowercase		,						)

         for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]		,						encoded_processor[key].squeeze().tolist() )





