

_lowerCamelCase							=[
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "bh-e12", "cd-e2", "ce-e4",
     "de-e1", "df-e8", "dg-e5", "dh-e10", "ef-e3", "eg-e2", "fg-e6", "gh-e6", "hi-e3"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "cd-e2", "de-e1", "df-e8",
     "ef-e3", "eg-e2", "fg-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "de-e1", "df-e8", "dg-e5", "ef-e3", "eg-e2",
     "eh-e12", "fg-e6", "fh-e10", "gh-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "bh-e12", "cd-e2", "df-e8", "dh-e10"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "cd-e2", "ce-e4", "de-e1", "df-e8",
     "dg-e5", "ef-e3", "eg-e2", "fg-e6"]
]







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=set()
     for row in edge_array:
          for item in row:
               distinct_edge.add(item[0]     )
     return list(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=['0'] * len(lowerCAmelCase_     )
     for i, row in enumerate(lowerCAmelCase_     ):
          for item in row:
               if distinct_edge in item[0]:
                    SCREAMING_SNAKE_CASE 		='1'
                    break
     return "".join(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=get_distinct_edge(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		={}

     for item in distinct_edge:
          SCREAMING_SNAKE_CASE 		=get_bitcode(lowerCAmelCase_,       lowerCAmelCase_     )
          # print('bit',bit)
          # bt=''.join(bit)
          SCREAMING_SNAKE_CASE 		=bit.count('1'     )
          SCREAMING_SNAKE_CASE 		=[s, bit]
     # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
     SCREAMING_SNAKE_CASE 		=[
         [k, v[0], v[1]]
         for k, v in sorted(frequency_table.items(),       key=lambda lowerCAmelCase_     : v[1][0],       reverse=lowerCAmelCase_     )
     ]
     return sorted_frequency_table







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		={}
     for _, item in enumerate(lowerCAmelCase_     ):
          nodes.setdefault(item[2],       []     ).append(item[0]     )
     return nodes







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		={}
     for key, value in nodes.items():
          SCREAMING_SNAKE_CASE 		=value
     return cluster







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     return [i * 100 / len(lowerCAmelCase_     ) for i in cluster]







def      snake_case__							(  ):





     """simple docstring"""







     print('\nNodes\n'     )
     for key, value in nodes.items():
          print(lowerCAmelCase_,       lowerCAmelCase_     )
     print('\nSupport\n'     )
     print(lowerCAmelCase_     )
     print('\n Cluster \n'     )
     for key, value in sorted(cluster.items(),       reverse=lowerCAmelCase_     ):
          print(lowerCAmelCase_,       lowerCAmelCase_     )
     print('\n Graph\n'     )
     for key, value in graph.items():
          print(lowerCAmelCase_,       lowerCAmelCase_     )
     print('\n Edge List of Frequent subgraphs \n'     )
     for edge_list in freq_subgraph_edge_list:
          print(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     for i in cluster[ca]:
          SCREAMING_SNAKE_CASE 		=0
          SCREAMING_SNAKE_CASE 		=ca + 1
          while ca < max(cluster.keys()     ):
               for j in cluster[ca]:
                    if int(lowerCAmelCase_,       2     ) & int(lowerCAmelCase_,       2     ) == int(lowerCAmelCase_,       2     ):
                         if tuple(nodes[i]     ) in graph:
                              graph[tuple(nodes[i]     )].append(nodes[j]     )
                         else:
                              SCREAMING_SNAKE_CASE 		=[nodes[j]]
                         count += 1
               if count == 0:
                    SCREAMING_SNAKE_CASE 		=ca + 1
               else:
                    break







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=cluster[max(cluster.keys()     )]
     SCREAMING_SNAKE_CASE 		='Header'
     SCREAMING_SNAKE_CASE 		={}
     for i in x:
          if (["Header"],) in graph:
               graph[(["Header"],)].append(x[i]     )
          else:
               SCREAMING_SNAKE_CASE 		=[x[i]]
     for i in x:
          SCREAMING_SNAKE_CASE 		=[['Header']]
     SCREAMING_SNAKE_CASE 		=1
     while i < max(lowerCAmelCase_     ) - 1:
          create_edge(lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     )
          SCREAMING_SNAKE_CASE 		=i + 1
     return graph







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=(path or []) + [start]
     if start == end:
          paths.append(lowerCAmelCase_     )
     for node in graph[start]:
          if tuple(lowerCAmelCase_     ) not in path:
               my_dfs(lowerCAmelCase_,       tuple(lowerCAmelCase_     ),       lowerCAmelCase_,       lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=int(s / 100 * (len(lowerCAmelCase_     ) - 1)     )
     for i in cluster[k]:
          my_dfs(lowerCAmelCase_,       tuple(cluster[k][i]     ),       (['Header'],)     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[]
     for edges in paths:
          SCREAMING_SNAKE_CASE 		=[]
          for j in range(len(lowerCAmelCase_     ) - 1     ):
               SCREAMING_SNAKE_CASE 		=list(edges[j]     )
               for e in temp:
                    SCREAMING_SNAKE_CASE 		=(e[0], e[1])
                    el.append(lowerCAmelCase_     )
          freq_sub_el.append(lowerCAmelCase_     )
     return freq_sub_el







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     for i in range(len(lowerCAmelCase_     )     ):
          for j in range(len(edge_array[i]     )     ):
               SCREAMING_SNAKE_CASE 		=edge_array[i][j].split('-'     )
               SCREAMING_SNAKE_CASE 		=t


if __name__ == "__main__":
 preprocess(edge_array)
 _lowerCamelCase							=get_frequency_table(edge_array)
 _lowerCamelCase							=get_nodes(frequency_table)
 _lowerCamelCase							=get_cluster(nodes)
 _lowerCamelCase							=get_support(cluster)
 _lowerCamelCase							=construct_graph(cluster, nodes)
 find_freq_subgraph_given_support(60, cluster, graph)
 _lowerCamelCase							=[]
 _lowerCamelCase							=freq_subgraphs_edge_list(paths)
 print_all()




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase							={
    "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",
    "mask_emb": "masked_spec_embed",
}







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







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

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

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[]
     SCREAMING_SNAKE_CASE 		=fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE 		=hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          SCREAMING_SNAKE_CASE 		=False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       hf_model.config.feat_extract_norm == 'group',       )
               SCREAMING_SNAKE_CASE 		=True
          else:
               for key, mapped_key in MAPPING.items():
                    SCREAMING_SNAKE_CASE 		=mapped_key

                    if key in name:
                         SCREAMING_SNAKE_CASE 		=True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE 		=name.split(lowerCAmelCase_     )[0].split('.'     )[-2]
                              SCREAMING_SNAKE_CASE 		=mapped_key.replace('*',       lowerCAmelCase_     )
                         if "weight_g" in name:
                              SCREAMING_SNAKE_CASE 		='weight_g'
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE 		='weight_v'
                         elif "weight" in name:
                              SCREAMING_SNAKE_CASE 		='weight'
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE 		='bias'
                         else:
                              SCREAMING_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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=full_name.split('conv_layers.'     )[-1]
     SCREAMING_SNAKE_CASE 		=name.split('.'     )
     SCREAMING_SNAKE_CASE 		=int(items[0]     )
     SCREAMING_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.'
               )
               SCREAMING_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.'
               )
               SCREAMING_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."
               )
               SCREAMING_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.'
               )
               SCREAMING_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      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=HubertConfig()
     SCREAMING_SNAKE_CASE 		=model.config

     SCREAMING_SNAKE_CASE 		=fs_config.activation_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=fs_config.attention_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=eval(fs_config.extractor_conv_feature_layers     )
     SCREAMING_SNAKE_CASE 		=[x[0] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[1] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[2] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		='gelu'
     SCREAMING_SNAKE_CASE 		='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.activation_fn
     SCREAMING_SNAKE_CASE 		=fs_config.dropout
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_embed_dim
     SCREAMING_SNAKE_CASE 		=0.02
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_ffn_embed_dim
     SCREAMING_SNAKE_CASE 		=1e-5
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_attention_heads
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos_groups
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos
     SCREAMING_SNAKE_CASE 		=len(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_layers

     return config







@torch.no_grad()
def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=distilhubert().model.model

     if config_path is not None:
          SCREAMING_SNAKE_CASE 		=HubertConfig.from_pretrained(lowerCAmelCase_     )
     else:
          SCREAMING_SNAKE_CASE 		=convert_config(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=model.eval()

     SCREAMING_SNAKE_CASE 		=WavaVecaFeatureExtractor(
         feature_size=1,       sampling_rate=16000,       padding_value=0,       do_normalize=lowerCAmelCase_,       return_attention_mask=lowerCAmelCase_,       )
     SCREAMING_SNAKE_CASE 		=HubertModel(lowerCAmelCase_     )

     recursively_load_weights(lowerCAmelCase_,       lowerCAmelCase_     )

     feature_extractor.save_pretrained(lowerCAmelCase_     )
     hf_model.save_pretrained(lowerCAmelCase_     )


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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 _lowerCamelCase							=parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)

