"""simple docstring"""



from __future__ import annotations

import time

SCREAMING_SNAKE_CASE__:Union[str, Any]			=	list[tuple[int, int]]

SCREAMING_SNAKE_CASE__:str			=	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

SCREAMING_SNAKE_CASE__:List[str]			=	[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  pos_x
        __a     				=  pos_y
        __a     				=  (pos_y, pos_x)
        __a     				=  goal_x
        __a     				=  goal_y
        __a     				=  parent






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  Node(start[1]  ,						start[0]  ,						goal[1]  ,						goal[0]  ,						lowerCamelCase		)
        __a     				=  Node(goal[1]  ,						goal[0]  ,						goal[1]  ,						goal[0]  ,						lowerCamelCase		)

        __a     				=  [self.start]
        __a     				=  False


 def 		a__ (				self		):
        while self.node_queue:
               __a     				=  self.node_queue.pop(0		)

               if current_node.pos == self.target.pos:
                      __a     				=  True
                      return self.retrace_path(lowerCamelCase		)

               __a     				=  self.get_successors(lowerCamelCase		)

               for node in successors:
                      self.node_queue.append(lowerCamelCase		)

        if not self.reached:
               return [self.start.pos]
        return None


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for action in delta:
               __a     				=  parent.pos_x + action[1]
               __a     				=  parent.pos_y + action[0]
               if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(lowerCamelCase		) - 1):
                      continue

               if grid[pos_y][pos_x] != 0:
                      continue

               successors.append(
                   Node(lowerCamelCase  ,						lowerCamelCase  ,						self.target.pos_y  ,						self.target.pos_x  ,						lowerCamelCase		)		)
        return successors






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  node
        __a     				=  []
        while current_node is not None:
               path.append((current_node.pos_y, current_node.pos_x)		)
               __a     				=  current_node.parent
        path.reverse()
        return path






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  BreadthFirstSearch(lowerCamelCase  ,						lowerCamelCase		)
        __a     				=  BreadthFirstSearch(lowerCamelCase  ,						lowerCamelCase		)
        __a     				=  False


 def 		a__ (				self		):
        while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
               __a     				=  self.fwd_bfs.node_queue.pop(0		)
               __a     				=  self.bwd_bfs.node_queue.pop(0		)

               if current_bwd_node.pos == current_fwd_node.pos:
                      __a     				=  True
                      return self.retrace_bidirectional_path(
                          lowerCamelCase  ,						lowerCamelCase		)

               __a     				=  current_bwd_node
               __a     				=  current_fwd_node

               __a     				=  {
                   self.fwd_bfs: self.fwd_bfs.get_successors(lowerCamelCase		),
                   self.bwd_bfs: self.bwd_bfs.get_successors(lowerCamelCase		),
               }

               for bfs in [self.fwd_bfs, self.bwd_bfs]:
                      for node in successors[bfs]:
                             bfs.node_queue.append(lowerCamelCase		)

        if not self.reached:
               return [self.fwd_bfs.start.pos]
        return None






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.fwd_bfs.retrace_path(lowerCamelCase		)
        __a     				=  self.bwd_bfs.retrace_path(lowerCamelCase		)
        bwd_path.pop()
        bwd_path.reverse()
        __a     				=  fwd_path + bwd_path
        return path


if __name__ == "__main__":
   # all coordinates are given in format [y,x]
   import doctest

   doctest.testmod()
   SCREAMING_SNAKE_CASE__:Dict			=	(0, 0)
   SCREAMING_SNAKE_CASE__:Dict			=	(len(grid) - 1, len(grid[0]) - 1)
   for elem in grid:
      print(elem)

   SCREAMING_SNAKE_CASE__:List[str]			=	time.time()
   SCREAMING_SNAKE_CASE__:Dict			=	BreadthFirstSearch(init, goal)
   SCREAMING_SNAKE_CASE__:int			=	bfs.search()
   SCREAMING_SNAKE_CASE__:Tuple			=	time.time() - start_bfs_time

   print("""Unidirectional BFS computation time : """, bfs_time)

   SCREAMING_SNAKE_CASE__:Tuple			=	time.time()
   SCREAMING_SNAKE_CASE__:Dict			=	BidirectionalBreadthFirstSearch(init, goal)
   SCREAMING_SNAKE_CASE__:List[Any]			=	bd_bfs.search()
   SCREAMING_SNAKE_CASE__:Tuple			=	time.time() - start_bd_bfs_time

   print("""Bidirectional BFS computation time : """, bd_bfs_time)

"""simple docstring"""



import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Dict			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["projector.weight"]
       __a     				=  downstream_dict["projector.bias"]
       __a     				=  downstream_dict["model.post_net.linear.weight"]
       __a     				=  downstream_dict["model.post_net.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["connector.weight"]
       __a     				=  downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
              __a     				=  downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              __a     				=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       __a     				=  downstream_dict["objective.W"]
       return model



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

       __a     				=  WavaVecaConfig.from_pretrained(a							)
       __a     				=  WavaVecaFeatureExtractor.from_pretrained(
           a ,  return_attention_mask=a ,  do_normalize=a							)

       __a     				=  hf_config.architectures[0]
       if arch.endswith("ForSequenceClassification"							):
              __a     				=  convert_classification(a ,  a ,  a							)
       elif arch.endswith("ForAudioFrameClassification"							):
              __a     				=  convert_diarization(a ,  a ,  a							)
       elif arch.endswith("ForXVector"							):
              __a     				=  convert_xvector(a ,  a ,  a							)
       else:
              raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"							)

       if hf_config.use_weighted_layer_sum:
              __a     				=  checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(a							)
       hf_model.save_pretrained(a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, Any]			=	argparse.ArgumentParser()
   parser.add_argument(
       """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
   )
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
   parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
   parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
   SCREAMING_SNAKE_CASE__:Any			=	parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
