





"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[str]:






       """simple docstring"""




       for attribute in key.split('.'				):
              _UpperCAmelCase			     =  getattr(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

       if weight_type is not None:
              _UpperCAmelCase			     =  getattr(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				).shape
       else:
              _UpperCAmelCase			     =  hf_pointer.shape

       assert hf_shape == value.shape, (
           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
       else:
              _UpperCAmelCase			     =  value

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Any:






       """simple docstring"""




       _UpperCAmelCase			     =  []
       _UpperCAmelCase			     =  fairseq_model.state_dict()

       _UpperCAmelCase			     =  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _UpperCAmelCase			     =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,hf_model.config.feat_extract_norm == 'group',)
                     _UpperCAmelCase			     =  True
              else:
                     for key, mapped_key in MAPPING.items():
                            _UpperCAmelCase			     =  'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') 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(SCREAMING_SNAKE_CASE				)[0].split('.'				)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          _UpperCAmelCase			     =  mapped_key.replace('*',SCREAMING_SNAKE_CASE				)
                                   if "weight_g" in name:
                                          _UpperCAmelCase			     =  'weight_g'
                                   elif "weight_v" in name:
                                          _UpperCAmelCase			     =  'weight_v'
                                   elif "weight" in name:
                                          _UpperCAmelCase			     =  'weight'
                                   elif "bias" in name:
                                          _UpperCAmelCase			     =  'bias'
                                   else:
                                          _UpperCAmelCase			     =  None
                                   set_recursively(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE				)

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[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:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         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:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         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:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F"""{full_name} has size {value.shape}, but {feature_extractor[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:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F"""{full_name} has size {value.shape}, but"""
                         F""" {feature_extractor[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(SCREAMING_SNAKE_CASE				)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[Any]:






       """simple docstring"""




       _UpperCAmelCase			     =  SEWDConfig()
       if is_finetuned:
              _UpperCAmelCase			     =  model.wav_encoder.wav_model.cfg
       else:
              _UpperCAmelCase			     =  model.cfg

       _UpperCAmelCase			     =  fs_config.conv_bias
       _UpperCAmelCase			     =  eval(fs_config.conv_feature_layers				)
       _UpperCAmelCase			     =  [x[0] for x in conv_layers]
       _UpperCAmelCase			     =  [x[1] for x in conv_layers]
       _UpperCAmelCase			     =  [x[2] for x in conv_layers]
       _UpperCAmelCase			     =  'gelu'
       _UpperCAmelCase			     =  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       _UpperCAmelCase			     =  0.0
       _UpperCAmelCase			     =  fs_config.activation_fn.name
       _UpperCAmelCase			     =  fs_config.encoder_embed_dim
       _UpperCAmelCase			     =  0.02
       _UpperCAmelCase			     =  fs_config.encoder_ffn_embed_dim
       _UpperCAmelCase			     =  1E-5
       _UpperCAmelCase			     =  fs_config.encoder_layerdrop
       _UpperCAmelCase			     =  fs_config.encoder_attention_heads
       _UpperCAmelCase			     =  fs_config.conv_pos_groups
       _UpperCAmelCase			     =  fs_config.conv_pos
       _UpperCAmelCase			     =  len(SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  fs_config.encoder_layers
       _UpperCAmelCase			     =  fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       _UpperCAmelCase			     =  fs_config.max_position_embeddings
       _UpperCAmelCase			     =  fs_config.position_buckets
       _UpperCAmelCase			     =  fs_config.share_att_key
       _UpperCAmelCase			     =  fs_config.relative_attention
       _UpperCAmelCase			     =  fs_config.position_biased_input
       _UpperCAmelCase			     =  tuple(fs_config.pos_att_type.split('|'				)				)
       _UpperCAmelCase			     =  fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              _UpperCAmelCase			     =  model.cfg
              _UpperCAmelCase			     =  fs_config.final_dropout
              _UpperCAmelCase			     =  fs_config.layerdrop
       _UpperCAmelCase			     =  fs_config.activation_dropout
       _UpperCAmelCase			     =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       _UpperCAmelCase			     =  fs_config.attention_dropout
       _UpperCAmelCase			     =  fs_config.dropout_input
       _UpperCAmelCase			     =  fs_config.dropout
       _UpperCAmelCase			     =  fs_config.mask_channel_length
       _UpperCAmelCase			     =  fs_config.mask_channel_prob
       _UpperCAmelCase			     =  fs_config.mask_length
       _UpperCAmelCase			     =  fs_config.mask_prob

       _UpperCAmelCase			     =  'Wav2Vec2FeatureExtractor'
       _UpperCAmelCase			     =  'Wav2Vec2CTCTokenizer'

       return config

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






       """simple docstring"""




       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     ,				_UpperCAmelCase     ,				_UpperCAmelCase			     =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

       if config_path is not None:
              _UpperCAmelCase			     =  SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
       else:
              _UpperCAmelCase			     =  convert_config(model[0],SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  model[0].eval()

       _UpperCAmelCase			     =  True if config.feat_extract_norm == 'layer' else False
       _UpperCAmelCase			     =  WavaVecaFeatureExtractor(
           feature_size=1,sampling_rate=16000,padding_value=0,do_normalize=SCREAMING_SNAKE_CASE,return_attention_mask=SCREAMING_SNAKE_CASE,)

       if is_finetuned:
              if dict_path:
                     _UpperCAmelCase			     =  Dictionary.load(SCREAMING_SNAKE_CASE				)

                     # 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.pad_index
                     _UpperCAmelCase			     =  target_dict.bos_index
                     _UpperCAmelCase			     =  target_dict.eos_index
                     _UpperCAmelCase			     =  len(target_dict.symbols				)
                     _UpperCAmelCase			     =  os.path.join(SCREAMING_SNAKE_CASE,'vocab.json'				)
                     if not os.path.isdir(SCREAMING_SNAKE_CASE				):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE				)				)
                            return
                     os.makedirs(SCREAMING_SNAKE_CASE,exist_ok=SCREAMING_SNAKE_CASE				)
                     with open(SCREAMING_SNAKE_CASE,'w',encoding='utf-8'				) as vocab_handle:
                            json.dump(target_dict.indices,SCREAMING_SNAKE_CASE				)
                     _UpperCAmelCase			     =  WavaVecaCTCTokenizer(
                         SCREAMING_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=SCREAMING_SNAKE_CASE,)
                     _UpperCAmelCase			     =  WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE,tokenizer=SCREAMING_SNAKE_CASE				)
                     processor.save_pretrained(SCREAMING_SNAKE_CASE				)

              _UpperCAmelCase			     =  SEWDForCTC(SCREAMING_SNAKE_CASE				)
       else:
              _UpperCAmelCase			     =  SEWDModel(SCREAMING_SNAKE_CASE				)
              feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE				)

       recursively_load_weights(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

       hf_model.save_pretrained(SCREAMING_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(
        '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    lowerCAmelCase_      =				parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )













"""simple docstring"""






from __future__ import annotations

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       _UpperCAmelCase			     =  [None] * (len(SCREAMING_SNAKE_CASE				) + len(SCREAMING_SNAKE_CASE				))

       _UpperCAmelCase			     =  0  # pointer to current index for left Half
       _UpperCAmelCase			     =  0  # pointer to current index for the right Half
       _UpperCAmelCase			     =  0  # pointer to current index for the sorted array Half

       while pointera < len(SCREAMING_SNAKE_CASE				) and pointera < len(SCREAMING_SNAKE_CASE				):
              if left_half[pointera] < right_half[pointera]:
                     _UpperCAmelCase			     =  left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     _UpperCAmelCase			     =  right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(SCREAMING_SNAKE_CASE				):
              _UpperCAmelCase			     =  right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							list:






       """simple docstring"""




       if len(SCREAMING_SNAKE_CASE				) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       _UpperCAmelCase			     =  0 + (len(SCREAMING_SNAKE_CASE				) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       _UpperCAmelCase			     =  array[:middle]
       _UpperCAmelCase			     =  array[middle:]

       return merge(merge_sort(SCREAMING_SNAKE_CASE				),merge_sort(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






