code: 
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)
  
  @dataclass
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)    ,      indent=2					) + "\n"
  
  
  
  
  
  
  @dataclass(frozen=snake_case__			)
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)					) + "\n"
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           if len(self.get_labels()					) > 1:
                snake_case					=       self.get_labels()[int(example.label					)]
           return example
  
  
  
  
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None					):
           with open(__snake_case    ,      '''r'''    ,      encoding='''utf-8-sig'''					) as f:
                return list(csv.reader(__snake_case    ,      delimiter='''\t'''    ,      quotechar=__snake_case					)					)
  
  
  
  
  
  
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case="classification"    ,      __snake_case=False					):
           snake_case					=       [] if labels is None else labels
           snake_case					=       [] if examples is None else examples
           snake_case					=       mode
           snake_case					=       verbose
      def __len__( self					):
           return len(self.examples					)
      def __getitem__( self    ,      __snake_case					):
           if isinstance(__snake_case    ,      __snake_case					):
                return SingleSentenceClassificationProcessor(labels=self.labels    ,      examples=self.examples[idx]					)
           return self.examples[idx]
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples_from_csv(
               __snake_case    ,      split_name=__snake_case    ,      column_label=__snake_case    ,      column_text=__snake_case    ,      column_id=__snake_case    ,      skip_first_row=__snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case    ,      )
           return processor
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples(__snake_case    ,      labels=__snake_case					)
           return processor
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False    ,      __snake_case=False    ,      ):
           snake_case					=       self._read_tsv(__snake_case					)
           if skip_first_row:
                snake_case					=       lines[1:]
           snake_case					=       []
           snake_case					=       []
           snake_case					=       []
           for i, line in enumerate(__snake_case					):
                texts.append(line[column_text]					)
                labels.append(line[column_label]					)
                if column_id is not None:
                     ids.append(line[column_id]					)
                else:
                     snake_case					=       F'''{split_name}-{i}''' if split_name else str(__snake_case					)
                     ids.append(__snake_case					)
  
           return self.add_examples(
               __snake_case    ,      __snake_case    ,      __snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False					):
           if labels is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(
                    F'''Text and labels have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(F'''Text and ids have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is None:
                snake_case					=       [None] * len(__snake_case					)
           if labels is None:
                snake_case					=       [None] * len(__snake_case					)
           snake_case					=       []
           snake_case					=       set()
           for text_or_text_and_label, label, guid in zip(__snake_case    ,      __snake_case    ,      __snake_case					):
                if isinstance(__snake_case    ,      (tuple, list)					) and label is None:
                     snake_case				,						snake_case					=       text_or_text_and_label
                else:
                     snake_case					=       text_or_text_and_label
                added_labels.add(__snake_case					)
                examples.append(InputExample(guid=__snake_case    ,      text_a=__snake_case    ,      text_b=__snake_case    ,      label=__snake_case					)					)
  
           # Update examples
           if overwrite_examples:
                snake_case					=       examples
           else:
                self.examples.extend(__snake_case					)
  
           # Update labels
           if overwrite_labels:
                snake_case					=       list(__snake_case					)
           else:
                snake_case					=       list(set(self.labels					).union(__snake_case					)					)
  
           return self.examples
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=0    ,      __snake_case=True    ,      __snake_case=None    ,      ):
           if max_length is None:
                snake_case					=       tokenizer.max_len
  
           snake_case					=       {label: i for i, label in enumerate(self.labels					)}
  
           snake_case					=       []
           for ex_index, example in enumerate(self.examples					):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info(F'''Tokenizing example {ex_index}'''					)
  
                snake_case					=       tokenizer.encode(
                    example.text_a    ,      add_special_tokens=__snake_case    ,      max_length=min(__snake_case    ,      tokenizer.max_len					)    ,      )
                all_input_ids.append(__snake_case					)
  
           snake_case					=       max(len(__snake_case					) for input_ids in all_input_ids					)
  
           snake_case					=       []
           for ex_index, (input_ids, example) in enumerate(zip(__snake_case    ,      self.examples					)					):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info(F'''Writing example {ex_index}/{len(self.examples					)}'''					)
                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                snake_case					=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)
  
                # Zero-pad up to the sequence length.
                snake_case					=       batch_length - len(__snake_case					)
                if pad_on_left:
                     snake_case					=       ([pad_token] * padding_length) + input_ids
                     snake_case					=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                else:
                     snake_case					=       input_ids + ([pad_token] * padding_length)
                     snake_case					=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
  
                if self.mode == "classification":
                     snake_case					=       label_map[example.label]
                elif self.mode == "regression":
                     snake_case					=       float(example.label					)
                else:
                     raise ValueError(self.mode					)
  
                if ex_index < 5 and self.verbose:
                     logger.info('''*** Example ***'''					)
                     logger.info(F'''guid: {example.guid}'''					)
                     logger.info(F'''input_ids: {" ".join([str(__snake_case					) for x in input_ids]					)}'''					)
                     logger.info(F'''attention_mask: {" ".join([str(__snake_case					) for x in attention_mask]					)}'''					)
                     logger.info(F'''label: {example.label} (id = {label})'''					)
  
                features.append(InputFeatures(input_ids=__snake_case    ,      attention_mask=__snake_case    ,      label=__snake_case					)					)
  
           if return_tensors is None:
                return features
           elif return_tensors == "tf":
                if not is_tf_available():
                     raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''					)
                import tensorflow as tf
  
                def gen():
                     for ex in features:
                          yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
                snake_case					=       tf.data.Dataset.from_generator(
                    __snake_case    ,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,      ({'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)}, tf.TensorShape([]					))    ,      )
                return dataset
           elif return_tensors == "pt":
                if not is_torch_available():
                     raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''					)
                import torch
                from torch.utils.data import TensorDataset
  
                snake_case					=       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long					)
                snake_case					=       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long					)
                if self.mode == "classification":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.long					)
                elif self.mode == "regression":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.float					)
  
                snake_case					=       TensorDataset(__snake_case    ,      __snake_case    ,      __snake_case					)
                return dataset
           else:
                raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)
  
  
  
  
  
style_context: 
  
  
  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()
  _SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "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__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for attribute in key.split('''.'''       ):
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       )
  
       if weight_type is not None:
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       ).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__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """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(
                     UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,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(UpperCamelCase_       )[0].split('''.'''       )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                snake_case					=       mapped_key.replace('''*'''							,UpperCamelCase_       )
                           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(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
                      continue
            if not is_used:
                 unused_weights.append(UpperCamelCase_       )
  
       logger.warning(F'''Unused weights: {unused_weights}'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """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(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """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(UpperCamelCase_       )
       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__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):
  
  
  
       """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(UpperCamelCase_       )
       else:
            snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
       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=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)
  
       if is_finetuned:
            if dict_path:
                 snake_case					=       Dictionary.load(UpperCamelCase_       )
  
                 # 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(UpperCamelCase_							,'''vocab.json'''       )
                 if not os.path.isdir(UpperCamelCase_       ):
                      logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_       )       )
                      return
                 os.makedirs(UpperCamelCase_							,exist_ok=UpperCamelCase_       )
                 with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''       ) as vocab_handle:
                      json.dump(target_dict.indices							,UpperCamelCase_       )
                 snake_case					=       WavaVecaCTCTokenizer(
                     UpperCamelCase_							,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=UpperCamelCase_							,)
                 snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
                 processor.save_pretrained(UpperCamelCase_       )
  
            snake_case					=       SEWDForCTC(UpperCamelCase_       )
       else:
            snake_case					=       SEWDModel(UpperCamelCase_       )
            feature_extractor.save_pretrained(UpperCamelCase_       )
  
       recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       hf_model.save_pretrained(UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				List[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(
       "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
   )
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )
  
  
  
  
  