


'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a_					:    List[Any]          =    logging.get_logger(__name__)

a_					:    Optional[int]          =    {
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
a_					:    Dict          =    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :List[str]		)   ->  List[str]:




     '''simple docstring'''



     for attribute in key.split('.'		):
          _a			      =							getattr(lowerCAmelCase__				,						lowerCAmelCase__		)

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

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






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Tuple		)   ->  Dict:




     '''simple docstring'''



     _a			      =							[]
     _a			      =							fairseq_model.state_dict()

     _a			      =							hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          _a			      =							False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						hf_model.config.feat_extract_norm == 'group'				,						)
               _a			      =							True
          else:
               for key, mapped_key in MAPPING.items():
                    _a			      =							'unispeech_sat.' + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'		)[:-1]		) != key):
                              # special case since naming is very similar
                              continue
                         _a			      =							True
                         if "*" in mapped_key:
                              _a			      =							name.split(lowerCAmelCase__		)[0].split('.'		)[-2]
                              _a			      =							mapped_key.replace('*'				,						lowerCAmelCase__		)
                         if "weight_g" in name:
                              _a			      =							'weight_g'
                         elif "weight_v" in name:
                              _a			      =							'weight_v'
                         elif "bias" in name:
                              _a			      =							'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _a			      =							'weight'
                         else:
                              _a			      =							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 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :str		)   ->  Optional[Any]:




     '''simple docstring'''



     _a			      =							full_name.split('conv_layers.'		)[-1]
     _a			      =							name.split('.'		)
     _a			      =							int(items[0]		)
     _a			      =							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			      =							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			      =							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[layer_id].layer_norm.bias.data.shape} was found.'		)
               _a			      =							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[layer_id].layer_norm.weight.data.shape} was found.'		)
               _a			      =							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 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :str=None				,						lowerCAmelCase__  :Dict=None				,						lowerCAmelCase__  :List[Any]=True		)   ->  int:




     '''simple docstring'''



     if config_path is not None:
          _a			      =							UniSpeechSatConfig.from_pretrained(lowerCAmelCase__		)
     else:
          _a			      =							UniSpeechSatConfig()

     _a			      =							''

     if is_finetuned:
          _a			      =							UniSpeechSatForCTC(lowerCAmelCase__		)
     else:
          _a			      =							UniSpeechSatForPreTraining(lowerCAmelCase__		)

     _a      ,   _a      ,   _a			      =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]				,						arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
     _a			      =							model[0].eval()

     recursively_load_weights(lowerCAmelCase__				,						lowerCAmelCase__		)

     hf_wavavec.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
 a_					:    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"
 )
 a_					:    str          =    parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Optional[Any]		)   ->  Optional[int]:




     '''simple docstring'''



     print('\nThe shortest path matrix using Floyd Warshall algorithm\n'		)
     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               if dist[i][j] != float('inf'		):
                    print(int(dist[i][j]		)				,						end='\t'		)
               else:
                    print('INF'				,						end='\t'		)
          print()






def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :List[str]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							[[float('inf'		) for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]

     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               _a			      =							graph[i][j]

               # check vertex k against all other vertices (i, j)
     for k in range(lowerCAmelCase__		):
          # looping through rows of graph array
          for i in range(lowerCAmelCase__		):
               # looping through columns of graph array
               for j in range(lowerCAmelCase__		):
                    if (
                        dist[i][k] != float('inf'		)
                        and dist[k][j] != float('inf'		)
                        and dist[i][k] + dist[k][j] < dist[i][j]
                    ):
                         _a			      =							dist[i][k] + dist[k][j]

     _print_dist(lowerCAmelCase__				,						lowerCAmelCase__		)
     return dist, v


if __name__ == "__main__":
 a_					:    Optional[Any]          =    int(input("Enter number of vertices: "))
 a_					:    List[Any]          =    int(input("Enter number of edges: "))

 a_					:    Dict          =    [[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  a_					:    Union[str, Any]          =    0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  a_					:    Union[str, Any]          =    int(input("Enter source:"))
  a_					:    int          =    int(input("Enter destination:"))
  a_					:    Tuple          =    float(input("Enter weight:"))
  a_					:    Dict          =    weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0

