


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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    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''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''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        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Any:
       for attribute in key.split('.'					):
              lowercase__:       Optional[Any]        =       getattr(snake_case      ,							snake_case					)

       if weight_type is not None:
              lowercase__:       int        =       getattr(snake_case      ,							snake_case					).shape
       else:
              lowercase__:       List[Any]        =       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":
              lowercase__:       Union[str, Any]        =       value
       elif weight_type == "weight_g":
              lowercase__:       Union[str, Any]        =       value
       elif weight_type == "weight_v":
              lowercase__:       Dict        =       value
       elif weight_type == "bias":
              lowercase__:       List[Any]        =       value
       else:
              lowercase__:       str        =       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					)  ->    Optional[int]:
       lowercase__:       Any        =       []
       lowercase__:       Optional[int]        =       fairseq_model.state_dict()

       lowercase__:       Tuple        =       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__:       Union[str, Any]        =       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__:       int        =       True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__:       Any        =       'sew.' + 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]:
                                   lowercase__:       Optional[int]        =       True
                                   if "*" in mapped_key:
                                          lowercase__:       Any        =       name.split(snake_case					)[0].split('.'					)[-2]
                                          lowercase__:       List[Any]        =       mapped_key.replace('*'      ,							snake_case					)
                                   if "weight_g" in name:
                                          lowercase__:       List[str]        =       'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__:       List[str]        =       'weight_v'
                                   elif "weight" in name:
                                          lowercase__:       List[Any]        =       'weight'
                                   elif "bias" in name:
                                          lowercase__:       Any        =       'bias'
                                   else:
                                          lowercase__:       int        =       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					)  ->    Optional[int]:
       lowercase__:       Any        =       full_name.split('conv_layers.'					)[-1]
       lowercase__:       Tuple        =       name.split('.'					)
       lowercase__:       Any        =       int(items[0]					)
       lowercase__:       Union[str, Any]        =       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.'
                     )
                     lowercase__:       Tuple        =       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.'
                     )
                     lowercase__:       Union[str, 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:
                     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."
                     )
                     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:
                     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.'
                     )
                     lowercase__:       List[str]        =       value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
       else:
              unused_weights.append(snake_case					)




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       lowercase__:       Any        =       SEWConfig()
       if is_finetuned:
              lowercase__:       Tuple        =       model.wav_encoder.wav_model.cfg
       else:
              lowercase__:       List[str]        =       model.cfg

       lowercase__:       Tuple        =       fs_config.conv_bias
       lowercase__:       int        =       eval(fs_config.conv_feature_layers					)
       lowercase__:       Dict        =       [x[0] for x in conv_layers]
       lowercase__:       str        =       [x[1] for x in conv_layers]
       lowercase__:       Dict        =       [x[2] for x in conv_layers]
       lowercase__:       Optional[Any]        =       'gelu'
       lowercase__:       Any        =       'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       lowercase__:       Optional[Any]        =       0.0
       lowercase__:       Tuple        =       fs_config.activation_fn.name
       lowercase__:       str        =       fs_config.encoder_embed_dim
       lowercase__:       Dict        =       0.0_2
       lowercase__:       Optional[Any]        =       fs_config.encoder_ffn_embed_dim
       lowercase__:       Dict        =       1e-5
       lowercase__:       Optional[int]        =       fs_config.encoder_layerdrop
       lowercase__:       str        =       fs_config.encoder_attention_heads
       lowercase__:       Optional[int]        =       fs_config.conv_pos_groups
       lowercase__:       Tuple        =       fs_config.conv_pos
       lowercase__:       Dict        =       len(snake_case					)
       lowercase__:       Dict        =       fs_config.encoder_layers
       lowercase__:       int        =       fs_config.squeeze_factor

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              lowercase__:       int        =       model.cfg
              lowercase__:       Union[str, Any]        =       fs_config.final_dropout
              lowercase__:       Dict        =       fs_config.layerdrop
       lowercase__:       Any        =       fs_config.activation_dropout
       lowercase__:       int        =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowercase__:       List[str]        =       fs_config.attention_dropout
       lowercase__:       Union[str, Any]        =       fs_config.dropout_input
       lowercase__:       int        =       fs_config.dropout
       lowercase__:       Tuple        =       fs_config.mask_channel_length
       lowercase__:       List[str]        =       fs_config.mask_channel_prob
       lowercase__:       Any        =       fs_config.mask_length
       lowercase__:       Any        =       fs_config.mask_prob

       lowercase__:       Optional[int]        =       'Wav2Vec2FeatureExtractor'
       lowercase__:       List[Any]        =       'Wav2Vec2CTCTokenizer'

       return config




@torch.no_grad()
def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case=None      ,							snake_case=None      ,							snake_case=True					)  ->    List[str]:

       if is_finetuned:
              lowercase__      ,						lowercase__      ,						lowercase__:       Any        =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]      ,							arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
       else:
              lowercase__      ,						lowercase__      ,						lowercase__:       Union[str, Any]        =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

       if config_path is not None:
              lowercase__:       List[Any]        =       SEWConfig.from_pretrained(snake_case					)
       else:
              lowercase__:       Dict        =       convert_config(model[0]      ,							snake_case					)
       lowercase__:       Dict        =       model[0].eval()

       lowercase__:       Optional[Any]        =       True if config.feat_extract_norm == 'layer' else False
       lowercase__:       List[str]        =       WavaVecaFeatureExtractor(
           feature_size=1      ,							sampling_rate=1_60_00      ,							padding_value=0      ,							do_normalize=snake_case      ,							return_attention_mask=snake_case      ,							)

       if is_finetuned:
              if dict_path:
                     lowercase__:       List[Any]        =       Dictionary.load(snake_case					)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowercase__:       Any        =       target_dict.pad_index
                     lowercase__:       List[str]        =       target_dict.bos_index
                     lowercase__:       Union[str, Any]        =       target_dict.pad_index
                     lowercase__:       Union[str, Any]        =       target_dict.bos_index
                     lowercase__:       Tuple        =       target_dict.eos_index
                     lowercase__:       Any        =       len(target_dict.symbols					)
                     lowercase__:       Any        =       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					)
                     with open(snake_case      ,							'w'      ,							encoding='utf-8'					) as vocab_handle:
                            json.dump(target_dict.indices      ,							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[Any]        =       WavaVecaProcessor(feature_extractor=snake_case      ,							tokenizer=snake_case					)
                     processor.save_pretrained(snake_case					)

              lowercase__:       Optional[Any]        =       SEWForCTC(snake_case					)
       else:
              lowercase__:       Union[str, Any]        =       SEWModel(snake_case					)
              feature_extractor.save_pretrained(snake_case					)

       recursively_load_weights(snake_case      ,							snake_case      ,							snake_case					)

       hf_model.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(
     '''--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
 )






def        snake_case_					(  snake_case					)  ->    list[int]:
       lowercase__:       Dict        =       [0 for i in range(len(snake_case					)					)]

       # initialize interval's left pointer and right pointer
       lowercase__      ,						lowercase__:       Union[str, Any]        =       0, 0

       for i in range(1      ,							len(snake_case					)					):
              # case when current index is inside the interval
              if i <= right_pointer:
                     lowercase__:       List[Any]        =       min(right_pointer - i + 1      ,							z_result[i - left_pointer]					)
                     lowercase__:       List[str]        =       min_edge

              while go_next(snake_case      ,							snake_case      ,							snake_case					):
                     z_result[i] += 1

              # if new index's result gives us more right interval,
              # we've to update left_pointer and right_pointer
              if i + z_result[i] - 1 > right_pointer:
                     lowercase__      ,						lowercase__:       List[Any]        =       i, i + z_result[i] - 1

       return z_result




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    bool:
       return i + z_result[i] < len(snake_case					) and s[z_result[i]] == s[i + z_result[i]]




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       lowercase__:       Tuple        =       0
       # concatenate 'pattern' and 'input_str' and call z_function
       # with concatenated string
       lowercase__:       Any        =       z_function(pattern + input_str					)

       for val in z_result:
              # if value is greater then length of the pattern string
              # that means this index is starting position of substring
              # which is equal to pattern string
              if val >= len(snake_case					):
                     answer += 1

       return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


