


'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
A				:					List[str]    						=			logging.get_logger(__name__)

A				:					Any    						=			{
    '''post_extract_proj''': '''feature_projection.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.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
A				:					List[Any]    						=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[Any]       ):
       _A					:     Dict     = {}
       with open(lowerCamelCase  ,'r'       ) as file:
              for line_number, line in enumerate(lowerCamelCase       ):
                     _A					:     int     = line.strip()
                     if line:
                            _A					:     Any     = line.split()
                            _A					:     List[Any]     = line_number
                            _A					:     str     = words[0]
                            _A					:     Union[str, Any]     = value
       return result






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      List[Any]       ):
       for attribute in key.split('.'       ):
              _A					:     Tuple     = getattr(lowerCamelCase  ,lowerCamelCase       )

       _A					:     List[str]     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCamelCase       ):
                     _A					:     Optional[Any]     = PARAM_MAPPING[full_name.split('.'       )[-1]]
                     _A					:     Optional[Any]     = 'param'

       if weight_type is not None and weight_type != "param":
              _A					:     Dict     = getattr(lowerCamelCase  ,lowerCamelCase       ).shape
       elif weight_type is not None and weight_type == "param":
              _A					:     str     = hf_pointer
              for attribute in hf_param_name.split('.'       ):
                     _A					:     Union[str, Any]     = getattr(lowerCamelCase  ,lowerCamelCase       )
              _A					:     Dict     = shape_pointer.shape

              # let's reduce dimension
              _A					:     Optional[Any]     = value[0]
       else:
              _A					:     Dict     = 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":
              _A					:     List[str]     = value
       elif weight_type == "weight_g":
              _A					:     int     = value
       elif weight_type == "weight_v":
              _A					:     Optional[Any]     = value
       elif weight_type == "bias":
              _A					:     List[Any]     = value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'       ):
                     _A					:     Optional[Any]     = getattr(lowerCamelCase  ,lowerCamelCase       )
              _A					:     Union[str, Any]     = value
       else:
              _A					:     List[Any]     = value

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      List[Any]       ):
       _A					:     Dict     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCamelCase       ):
                     _A					:     List[Any]     = PARAM_MAPPING[full_name.split('.'       )[-1]]
                     _A					:     int     = 'param'

       if weight_type is not None and weight_type != "param":
              _A					:     Dict     = '.'.join([key, weight_type]       )
       elif weight_type is not None and weight_type == "param":
              _A					:     Optional[Any]     = '.'.join([key, hf_param_name]       )
       else:
              _A					:     int     = key

       _A					:     List[Any]     = value if 'lm_head' in full_key else value[0]


A				:					Tuple    						=			{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Optional[Any]=None  ,lowerCamelCase					:      Tuple=None       ):
       _A					:     List[str]     = False
       for key, mapped_key in MAPPING.items():
              _A					:     Union[str, Any]     = 'wav2vec2.' + 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]:
                     _A					:     str     = True
                     if "*" in mapped_key:
                            _A					:     Optional[int]     = name.split(lowerCamelCase       )[0].split('.'       )[-2]
                            _A					:     List[str]     = mapped_key.replace('*'  ,lowerCamelCase       )
                     if "weight_g" in name:
                            _A					:     Dict     = 'weight_g'
                     elif "weight_v" in name:
                            _A					:     int     = 'weight_v'
                     elif "bias" in name:
                            _A					:     Dict     = 'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            _A					:     int     = 'weight'
                     else:
                            _A					:     Union[str, Any]     = None
                     if hf_dict is not None:
                            rename_dict(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )
                     else:
                            set_recursively(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )
                     return is_used
       return is_used






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Union[str, Any]       ):
       _A					:     List[Any]     = []
       _A					:     str     = fairseq_model.state_dict()

       _A					:     List[str]     = hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              _A					:     List[str]     = False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase  ,hf_model.config.feat_extract_norm == 'group'  ,)
                     _A					:     Union[str, Any]     = True
              else:
                     _A					:     Optional[int]     = load_wavaveca_layer(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )
              if not is_used:
                     unused_weights.append(lowerCamelCase       )

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      List[str]       ):
       _A					:     str     = full_name.split('conv_layers.'       )[-1]
       _A					:     str     = name.split('.'       )
       _A					:     Any     = int(items[0]       )
       _A					:     List[str]     = 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.'       )
                     _A					:     Optional[Any]     = 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.'       )
                     _A					:     int     = 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.'       )
                     _A					:     Optional[int]     = 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.'       )
                     _A					:     List[Any]     = value
                     logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
       else:
              unused_weights.append(lowerCamelCase       )






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict=None  ,lowerCamelCase					:      List[Any]=None  ,lowerCamelCase					:      int=True  ,lowerCamelCase					:      Any=False       ):
       if config_path is not None:
              _A					:     List[str]     = WavaVecaConfig.from_pretrained(lowerCamelCase       )
       else:
              _A					:     str     = WavaVecaConfig()

       if is_seq_class:
              _A					:     List[Any]     = read_txt_into_dict(lowerCamelCase       )
              _A					:     Optional[Any]     = idalabel
              _A					:     Union[str, Any]     = WavaVecaForSequenceClassification(lowerCamelCase       )
              _A					:     List[Any]     = WavaVecaFeatureExtractor(
                  feature_size=1  ,sampling_rate=16000  ,padding_value=0  ,do_normalize=lowerCamelCase  ,return_attention_mask=lowerCamelCase  ,)
              feature_extractor.save_pretrained(lowerCamelCase       )

       elif is_finetuned:
              if dict_path:
                     _A					:     Any     = Dictionary.load(lowerCamelCase       )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _A					:     str     = target_dict.pad_index
                     _A					:     Union[str, Any]     = target_dict.bos_index
                     _A					:     Optional[Any]     = target_dict.eos_index
                     _A					:     Optional[int]     = len(target_dict.symbols       )
                     _A					:     Any     = 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       )
                     _A					:     List[Any]     = target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     _A					:     Any     = 0
                     _A					:     int     = 1
                     with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as vocab_handle:
                            json.dump(lowerCamelCase  ,lowerCamelCase       )
                     _A					:     Dict     = 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  ,)
                     _A					:     int     = True if config.feat_extract_norm == 'layer' else False
                     _A					:     Any     = WavaVecaFeatureExtractor(
                         feature_size=1  ,sampling_rate=16000  ,padding_value=0  ,do_normalize=lowerCamelCase  ,return_attention_mask=lowerCamelCase  ,)
                     _A					:     str     = WavaVecaProcessor(feature_extractor=lowerCamelCase  ,tokenizer=lowerCamelCase       )
                     processor.save_pretrained(lowerCamelCase       )

              _A					:     Tuple     = WavaVecaForCTC(lowerCamelCase       )
       else:
              _A					:     List[str]     = WavaVecaForPreTraining(lowerCamelCase       )

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

              _A  ,			_A  ,			_A					:     List[str]     = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,task=lowerCamelCase       )

       _A					:     Optional[int]     = model[0].eval()

       recursively_load_weights(lowerCamelCase  ,lowerCamelCase  ,not is_finetuned       )

       hf_wavavec.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
   A				:					Union[str, Any]    						=			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'''
   )
   parser.add_argument(
       '''--is_seq_class''',
       action='''store_true''',
       help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
   )
   A				:					int    						=			parser.parse_args()

   A				:					str    						=			not args.not_finetuned and not args.is_seq_class
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.dict_path,
       is_finetuned,
       args.is_seq_class,
   )










'''simple docstring'''







class 			__lowerCamelCase			:  # Public class to implement a graph
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           _A					:     List[Any]     = row
           _A					:     Union[str, Any]     = col
           _A					:     List[str]     = graph


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           # Checking all 8 elements surrounding nth element
           _A					:     Tuple     = [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           _A					:     Dict     = [-1, 0, 1, -1, 1, -1, 0, 1]
           _A					:     List[Any]     = True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE):
                         self.diffs(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):  # And finally, count all islands.
           _A					:     Dict     = [[False for j in range(self.COL)] for i in range(self.ROW)]
           _A					:     Union[str, Any]     = 0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                                count += 1
           return count






