




'''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()
UpperCAmelCase_				:     Dict     			=						logging.get_logger(__name__)

UpperCAmelCase_				:     List[Any]     			=						{
    '''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 	_UpperCamelCase       (_lowerCamelCase							:   str      ,		_lowerCamelCase							:   Dict      ,		_lowerCamelCase							:   int      ,		_lowerCamelCase							:   Dict      ,		_lowerCamelCase							:   List[Any]		)->      str:




     '''simple docstring'''





     for attribute in key.split('''.'''		):
          __snake_case			    =     getattr(_lowerCamelCase      ,		_lowerCamelCase		)

     if weight_type is not None:
          __snake_case			    =     getattr(_lowerCamelCase      ,		_lowerCamelCase		).shape
     else:
          __snake_case			    =     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":
          __snake_case			    =     value
     elif weight_type == "weight_g":
          __snake_case			    =     value
     elif weight_type == "weight_v":
          __snake_case			    =     value
     elif weight_type == "bias":
          __snake_case			    =     value
     else:
          __snake_case			    =     value

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







def 	_UpperCamelCase       (_lowerCamelCase							:   str      ,		_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   str		)->      Optional[Any]:




     '''simple docstring'''





     __snake_case			    =     []
     __snake_case			    =     fairseq_model.state_dict()

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

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

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







def 	_UpperCamelCase       (_lowerCamelCase							:   Optional[Any]      ,		_lowerCamelCase							:   str      ,		_lowerCamelCase							:   Optional[int]      ,		_lowerCamelCase							:   List[str]      ,		_lowerCamelCase							:   List[Any]		)->      List[Any]:




     '''simple docstring'''





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







def 	_UpperCamelCase       (_lowerCamelCase							:   List[Any]      ,		_lowerCamelCase							:   Optional[Any]		)->      Any:




     '''simple docstring'''





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

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

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

     __snake_case			    =     '''Wav2Vec2FeatureExtractor'''
     __snake_case			    =     '''Wav2Vec2CTCTokenizer'''

     return config







@torch.no_grad()
def 	_UpperCamelCase       (_lowerCamelCase							:   int      ,		_lowerCamelCase							:   str      ,		_lowerCamelCase							:   Optional[Any]=None      ,		_lowerCamelCase							:   Optional[int]=None      ,		_lowerCamelCase							:   Optional[Any]=True		)->      Tuple:




     '''simple docstring'''





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

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

     __snake_case			    =     True if config.feat_extract_norm == '''layer''' else False
     __snake_case			    =     WavaVecaFeatureExtractor(
         feature_size=1      ,		sampling_rate=1_60_00      ,		padding_value=0      ,		do_normalize=_lowerCamelCase      ,		return_attention_mask=_lowerCamelCase      ,		)

     if is_finetuned:
          if dict_path:
               __snake_case			    =     Dictionary.load(_lowerCamelCase		)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __snake_case			    =     target_dict.pad_index
               __snake_case			    =     target_dict.bos_index
               __snake_case			    =     target_dict.pad_index
               __snake_case			    =     target_dict.bos_index
               __snake_case			    =     target_dict.eos_index
               __snake_case			    =     len(target_dict.symbols		)
               __snake_case			    =     os.path.join(_lowerCamelCase      ,		'''vocab.json'''		)
               if not os.path.isdir(_lowerCamelCase		):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCamelCase		)		)
                    return
               os.makedirs(_lowerCamelCase      ,		exist_ok=_lowerCamelCase		)
               with open(_lowerCamelCase      ,		'''w'''      ,		encoding='''utf-8'''		) as vocab_handle:
                    json.dump(target_dict.indices      ,		_lowerCamelCase		)
               __snake_case			    =     WavaVecaCTCTokenizer(
                   _lowerCamelCase      ,		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=_lowerCamelCase      ,		)
               __snake_case			    =     WavaVecaProcessor(feature_extractor=_lowerCamelCase      ,		tokenizer=_lowerCamelCase		)
               processor.save_pretrained(_lowerCamelCase		)

          __snake_case			    =     SEWDForCTC(_lowerCamelCase		)
     else:
          __snake_case			    =     SEWDModel(_lowerCamelCase		)
          feature_extractor.save_pretrained(_lowerCamelCase		)

     recursively_load_weights(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase		)

     hf_model.save_pretrained(_lowerCamelCase		)


if __name__ == "__main__":
     UpperCAmelCase_				:     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(
         '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     UpperCAmelCase_				:     Dict     			=						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 sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase_				:     List[Any]     			=						'''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase_				:     List[Any]     			=						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase_				:     Union[str, Any]     			=						'''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,      _KWARGS_DESCRIPTION)
class 			lowerCAmelCase      (       datasets.Metric):


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
                    {
                        '''predictions''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                        '''references''': datasets.Sequence(datasets.Value('''int32'''     )     ),
                    }
                    if self.config_name == '''multilabel'''
                    else {
                        '''predictions''': datasets.Value('''int32'''     ),
                        '''references''': datasets.Value('''int32'''     ),
                    }     )			,     reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']			,     )




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=None     )					->     Any:



            '''simple docstring'''




            return {
                "accuracy": float(
                    accuracy_score(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     normalize=__SCREAMING_SNAKE_CASE			,     sample_weight=__SCREAMING_SNAKE_CASE     )     )
            }






