






import math
import sys
class 							__snake_case:



 def __init__(						self : Any						):

    """simple docstring"""






    lowercase__    :						List[str]       =			0
    lowercase__    :						Union[str, Any]       =			[]
    lowercase__    :						str       =			{}  # To store the pos of node in array



 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    return self.cur_size == 0



 def     UpperCAmelCase     (						self : Any			,		UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			self.left(UpperCAmelCase__						)
    lowercase__    :						List[str]       =			self.right(UpperCAmelCase__						)
    if lc < self.cur_size and self.array(UpperCAmelCase__						)[0] < self.array(UpperCAmelCase__						)[0]:
       lowercase__    :						int       =			lc
    else:
       lowercase__    :						Union[str, Any]       =			idx
    if rc < self.cur_size and self.array(UpperCAmelCase__						)[0] < self.array(UpperCAmelCase__						)[0]:
       lowercase__    :						Optional[Any]       =			rc
    if smallest != idx:
       self.swap(UpperCAmelCase__			,		UpperCAmelCase__						)
       self.min_heapify(UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						Tuple       =			self.cur_size
    self.cur_size += 1
    self.array.append((sys.maxsize, tup[1])						)
    self.decrease_key((sys.maxsize, tup[1])			,		tup[0]						)



 def     UpperCAmelCase     (						self : List[str]						):

    """simple docstring"""






    lowercase__    :						Optional[int]       =			self.array[0][1]
    lowercase__    :						Tuple       =			self.array[self.cur_size - 1]
    self.cur_size -= 1
    self.min_heapify(1						)
    del self.pos[min_node]
    return min_node



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Optional[int]						):

    """simple docstring"""






    return 2 * i + 1



 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : Dict						):

    """simple docstring"""






    return 2 * i + 2



 def     UpperCAmelCase     (						self : Optional[int]			,		UpperCAmelCase__ : Any						):

    """simple docstring"""






    return math.floor(i / 2						)



 def     UpperCAmelCase     (						self : Any			,		UpperCAmelCase__ : List[str]			,		UpperCAmelCase__ : str						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			j
    lowercase__    :						Dict       =			i
    lowercase__    :						List[str]       =			self.array[i]
    lowercase__    :						Dict       =			self.array[j]
    lowercase__    :						List[Any]       =			temp







 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Union[str, Any]			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			self.pos[tup[1]]
    # assuming the new_d is atmost old_d
    lowercase__    :						Optional[Any]       =			(new_d, tup[1])
    while idx > 0 and self.array[self.par(UpperCAmelCase__						)][0] > self.array[idx][0]:
       self.swap(UpperCAmelCase__			,		self.par(UpperCAmelCase__						)						)
       lowercase__    :						List[str]       =			self.par(UpperCAmelCase__						)
class 							__snake_case:



 def __init__(						self : Tuple			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__    :						Any       =			{}  # To store graph: u -> (v,w)
    lowercase__    :						List[Any]       =			num  # Number of nodes in graph
    # To store the distance from source vertex
    lowercase__    :						List[Any]       =			[0] * self.num_nodes
    lowercase__    :						Optional[int]       =			[-1] * self.num_nodes  # To store the path



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : Any			,		UpperCAmelCase__ : Union[str, Any]			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    if u in self.adjList:
       self.adjList[u].append((v, w)						)
    else:
       lowercase__    :						List[str]       =			[(v, w)]

    # Assuming undirected graph
    if v in self.adjList:
       self.adjList[v].append((u, w)						)
    else:
       lowercase__    :						Dict       =			[(u, w)]



 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    for u in self.adjList:
       print(UpperCAmelCase__			,		"""->"""			,		""" -> """.join(str(f'''{v}({w})'''						) for v, w in self.adjList[u]						)						)



 def     UpperCAmelCase     (						self : Optional[int]			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__    :						str       =			[-1] * self.num_nodes
    # src is the source node
    lowercase__    :						Optional[Any]       =			0
    lowercase__    :						Any       =			PriorityQueue()
    q.insert((0, src)						)  # (dist from src, node)
    for u in self.adjList:
       if u != src:
          lowercase__    :						List[Any]       =			sys.maxsize  # Infinity
          lowercase__    :						Optional[Any]       =			-1

    while not q.is_empty():
       lowercase__    :						Optional[int]       =			q.extract_min()  # Returns node with the min dist from source
       # Update the distance of all the neighbours of u and
       # if their prev dist was INFINITY then push them in Q
       for v, w in self.adjList[u]:
          lowercase__    :						Any       =			self.dist[u] + w
          if self.dist[v] > new_dist:
             if self.dist[v] == sys.maxsize:
                q.insert((new_dist, v)						)
             else:
                q.decrease_key((self.dist[v], v)			,		UpperCAmelCase__						)
             lowercase__    :						Any       =			new_dist
             lowercase__    :						List[Any]       =			u

        # Show the shortest distances from src
    self.show_distances(UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : Tuple						):

    """simple docstring"""






    print(f'''Distance from node: {src}'''						)
    for u in range(self.num_nodes						):
       print(f'''Node {u} has distance: {self.dist[u]}'''						)







 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : str			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__    :						Any       =			[]
    lowercase__    :						Dict       =			0
    lowercase__    :						Dict       =			dest
    # Backtracking from dest to src
    while self.par[temp] != -1:
       path.append(UpperCAmelCase__						)
       if temp != src:
          for v, w in self.adjList[temp]:
             if v == self.par[temp]:
                cost += w
                break
       lowercase__    :						List[Any]       =			self.par[temp]
    path.append(UpperCAmelCase__						)
    path.reverse()

    print(f'''----Path to reach {dest} from {src}----'''						)
    for u in path:
       print(f'''{u}'''			,		end=""" """						)
       if u != dest:
          print("""-> """			,		end=""""""						)

    print("""\nTotal cost of path: """			,		UpperCAmelCase__						)


if __name__ == "__main__":
 a 			=							Graph(9)
 graph.add_edge(0, 1, 4)
 graph.add_edge(0, 7, 8)
 graph.add_edge(1, 2, 8)
 graph.add_edge(1, 7, 11)
 graph.add_edge(2, 3, 7)
 graph.add_edge(2, 8, 2)
 graph.add_edge(2, 5, 4)
 graph.add_edge(3, 4, 9)
 graph.add_edge(3, 5, 14)
 graph.add_edge(4, 5, 10)
 graph.add_edge(5, 6, 2)
 graph.add_edge(6, 7, 1)
 graph.add_edge(6, 8, 6)
 graph.add_edge(7, 8, 7)
 graph.show_graph()
 graph.dijkstra(0)
 graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21










import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

a 			=							{
    '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    a__    (	A		,					A		,					A		,					A		,					A       ):
   for attribute in key.split("""."""       ):
      lowercase__    :						Dict       =			getattr(A		,					A       )

   if weight_type is not None:
      lowercase__    :						List[Any]       =			getattr(A		,					A       ).shape
   else:
      lowercase__    :						Tuple       =			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":
      lowercase__    :						Dict       =			value
   elif weight_type == "weight_g":
      lowercase__    :						Any       =			value
   elif weight_type == "weight_v":
      lowercase__    :						Dict       =			value
   elif weight_type == "bias":
      lowercase__    :						Any       =			value
   else:
      lowercase__    :						Tuple       =			value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
def    a__    (	A		,					A       ):
   lowercase__    :						str       =			[]
   lowercase__    :						int       =			fairseq_model.state_dict()

   lowercase__    :						Tuple       =			hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase__    :						Optional[int]       =			False
      if "conv_layers" in name:
         load_conv_layer(
             A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == """group"""		,					)
         lowercase__    :						Optional[int]       =			True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase__    :						Dict       =			mapped_key

            if key in name:
               lowercase__    :						int       =			True
               if "*" in mapped_key:
                  lowercase__    :						Any       =			name.split(A       )[0].split("""."""       )[-2]
                  lowercase__    :						int       =			mapped_key.replace("""*"""		,					A       )
               if "weight_g" in name:
                  lowercase__    :						Dict       =			"""weight_g"""
               elif "weight_v" in name:
                  lowercase__    :						Dict       =			"""weight_v"""
               elif "weight" in name:
                  lowercase__    :						Union[str, Any]       =			"""weight"""
               elif "bias" in name:
                  lowercase__    :						Optional[int]       =			"""bias"""
               else:
                  lowercase__    :						List[str]       =			None
               set_recursively(A		,					A		,					A		,					A		,					A       )
            continue
      if not is_used:
         unused_weights.append(A       )

   logger.warning(F'''Unused weights: {unused_weights}'''       )
def    a__    (	A		,					A		,					A		,					A		,					A       ):
   lowercase__    :						Optional[int]       =			full_name.split("""conv_layers."""       )[-1]
   lowercase__    :						int       =			name.split("""."""       )
   lowercase__    :						Optional[Any]       =			int(items[0]       )
   lowercase__    :						Optional[int]       =			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.'''
         )
         lowercase__    :						int       =			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.'''
         )
         lowercase__    :						str       =			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."
         )
         lowercase__    :						Any       =			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.'''
         )
         lowercase__    :						int       =			value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
   else:
      unused_weights.append(A       )
def    a__    (	A       ):
   lowercase__    :						Optional[Any]       =			HubertConfig()
   lowercase__    :						List[str]       =			model.config

   lowercase__    :						int       =			fs_config.activation_dropout
   lowercase__    :						Tuple       =			False
   lowercase__    :						Optional[Any]       =			fs_config.attention_dropout
   lowercase__    :						str       =			False
   lowercase__    :						List[Any]       =			eval(fs_config.extractor_conv_feature_layers       )
   lowercase__    :						Union[str, Any]       =			[x[0] for x in conv_layers]
   lowercase__    :						str       =			[x[1] for x in conv_layers]
   lowercase__    :						str       =			[x[2] for x in conv_layers]
   lowercase__    :						Any       =			"""gelu"""
   lowercase__    :						Optional[int]       =			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   lowercase__    :						Optional[int]       =			False
   lowercase__    :						Tuple       =			0.0
   lowercase__    :						Optional[Any]       =			0.0
   lowercase__    :						Optional[Any]       =			fs_config.activation_fn
   lowercase__    :						Optional[Any]       =			fs_config.dropout
   lowercase__    :						Any       =			fs_config.encoder_embed_dim
   lowercase__    :						List[Any]       =			0.0_2
   lowercase__    :						List[Any]       =			fs_config.encoder_ffn_embed_dim
   lowercase__    :						List[Any]       =			1e-5
   lowercase__    :						Dict       =			0.0
   lowercase__    :						List[Any]       =			fs_config.encoder_attention_heads
   lowercase__    :						List[str]       =			fs_config.conv_pos_groups
   lowercase__    :						List[Any]       =			fs_config.conv_pos
   lowercase__    :						Union[str, Any]       =			len(A       )
   lowercase__    :						List[str]       =			fs_config.encoder_layers

   return config
@torch.no_grad()
def    a__    (	A		,					A=None       ):
   lowercase__    :						List[str]       =			distilhubert().model.model

   if config_path is not None:
      lowercase__    :						List[str]       =			HubertConfig.from_pretrained(A       )
   else:
      lowercase__    :						List[str]       =			convert_config(A       )
   lowercase__    :						List[Any]       =			model.eval()

   lowercase__    :						Optional[Any]       =			WavaVecaFeatureExtractor(
       feature_size=1		,					sampling_rate=16_000		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
   lowercase__    :						Union[str, Any]       =			HubertModel(A       )

   recursively_load_weights(A		,					A       )

   feature_extractor.save_pretrained(A       )
   hf_model.save_pretrained(A       )


if __name__ == "__main__":
 a 			=							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')
 a 			=							parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


