


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			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''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			=							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    str:
       for attribute in key.split('.'					):
              lowercase__:       Tuple        =       getattr(snake_case      ,							snake_case					)

       if weight_type is not None:
              lowercase__:       Tuple        =       getattr(snake_case      ,							snake_case					).shape
       else:
              lowercase__:       str        =       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":
              lowercase__:       Optional[int]        =       value
       elif weight_type == "weight_g":
              lowercase__:       Dict        =       value
       elif weight_type == "weight_v":
              lowercase__:       int        =       value
       elif weight_type == "bias":
              lowercase__:       Dict        =       value
       elif weight_type == "running_mean":
              lowercase__:       Optional[Any]        =       value
       elif weight_type == "running_var":
              lowercase__:       int        =       value
       elif weight_type == "num_batches_tracked":
              lowercase__:       Optional[int]        =       value
       elif weight_type == "inv_freq":
              lowercase__:       Dict        =       value
       else:
              lowercase__:       Optional[Any]        =       value

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




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Dict:
       lowercase__:       List[str]        =       []
       lowercase__:       Optional[int]        =       fairseq_model.state_dict()

       lowercase__:       str        =       hf_model.wavaveca_conformer.feature_extractor

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

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




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Dict:
       lowercase__:       str        =       full_name.split('conv_layers.'					)[-1]
       lowercase__:       Any        =       name.split('.'					)
       lowercase__:       List[str]        =       int(items[0]					)
       lowercase__:       Union[str, 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.'					)
                     lowercase__:       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.'					)
                     lowercase__:       Optional[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.'					)
                     lowercase__:       Union[str, Any]        =       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.'					)
                     lowercase__:       Optional[int]        =       value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
       else:
              unused_weights.append(snake_case					)




@torch.no_grad()
def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case=None      ,							snake_case=None      ,							snake_case=True					)  ->    Any:
       if config_path is not None:
              lowercase__:       Union[str, Any]        =       WavaVecaConformerConfig.from_pretrained(snake_case      ,							hidden_act='swish'					)
       else:
              lowercase__:       Union[str, Any]        =       WavaVecaConformerConfig()

       if "rope" in checkpoint_path:
              lowercase__:       Optional[Any]        =       'rotary'

       if is_finetuned:
              if dict_path:
                     lowercase__:       int        =       Dictionary.load(snake_case					)

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

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

              lowercase__:       Dict        =       WavaVecaConformerForCTC(snake_case					)
       else:
              lowercase__:       Dict        =       WavaVecaConformerForPreTraining(snake_case					)

       if is_finetuned:
              lowercase__      ,						lowercase__      ,						lowercase__:       Union[str, Any]        =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]      ,							arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
       else:
              lowercase__:       List[Any]        =       argparse.Namespace(task='audio_pretraining'					)
              lowercase__:       Dict        =       fairseq.tasks.setup_task(snake_case					)

              lowercase__      ,						lowercase__      ,						lowercase__:       Union[str, Any]        =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,							task=snake_case					)

       lowercase__:       int        =       model[0].eval()

       recursively_load_weights(snake_case      ,							snake_case      ,							not is_finetuned					)

       hf_wavavec.save_pretrained(snake_case					)


if __name__ == "__main__":
 __lowerCAmelCase			=							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			=							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
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class   __a      (   tf.keras.layers.Layer				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None			) ->    int:




        '''simple docstring'''
        super().__init__()
        lowercase__:       Union[str, Any]        =       pad_token_id
        lowercase__:       List[str]        =       max_length
        lowercase__:       int        =       vocab
        lowercase__:       List[Any]        =       merges
        lowercase__:       str        =       BytePairTokenizer(lowerCAmelCase__    ,			lowerCAmelCase__    ,			sequence_length=lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Tuple        =       [' '.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
        lowercase__:       List[Any]        =       tokenizer.get_vocab()
        return cls(lowerCAmelCase__    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       GPTaTokenizer.from_pretrained(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
        return cls.from_tokenizer(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        return cls(**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return {
            "vocab": self.vocab,
            "merges": self.merges,
            "max_length": self.max_length,
            "pad_token_id": self.pad_token_id,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.tf_tokenizer(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       tf.ones_like(lowerCAmelCase__			)

        if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__:       int        =       max_length if max_length is not None else self.max_length

               if max_length is not None:
                      lowercase__      ,						lowercase__:       List[Any]        =       pad_model_inputs(
                          lowerCAmelCase__    ,			max_seq_length=lowerCAmelCase__    ,			pad_value=self.pad_token_id			)

        return {"attention_mask": attention_mask, "input_ids": input_ids}


