




import math
import sys

class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    0
          __UpperCamelCase			=    []
          __UpperCamelCase			=    {}  # To store the pos of node in array







      def    snake_case						(  self       :			Any					):
          return self.cur_size == 0







      def    snake_case						(  self       :			Any			,							snake_case       :			Tuple					):
          __UpperCamelCase			=    self.left(snake_case					)
          __UpperCamelCase			=    self.right(snake_case					)
          if lc < self.cur_size and self.array(snake_case					)[0] < self.array(snake_case					)[0]:
              __UpperCamelCase			=    lc
          else:
              __UpperCamelCase			=    idx
          if rc < self.cur_size and self.array(snake_case					)[0] < self.array(snake_case					)[0]:
              __UpperCamelCase			=    rc
          if smallest != idx:
              self.swap(snake_case			,							snake_case					)
              self.min_heapify(snake_case					)







      def    snake_case						(  self       :			Dict			,							snake_case       :			Any					):
          # Inserts a node into the Priority Queue
          __UpperCamelCase			=    self.cur_size
          self.cur_size += 1
          self.array.append((sys.maxsize, tup[1])					)
          self.decrease_key((sys.maxsize, tup[1])			,							tup[0]					)







      def    snake_case						(  self       :			Dict					):
          # Removes and returns the min element at top of priority queue
          __UpperCamelCase			=    self.array[0][1]
          __UpperCamelCase			=    self.array[self.cur_size - 1]
          self.cur_size -= 1
          self.min_heapify(1					)
          del self.pos[min_node]
          return min_node







      def    snake_case						(  self       :			Any			,							snake_case       :			int					):
          # returns the index of left child
          return 2 * i + 1







      def    snake_case						(  self       :			str			,							snake_case       :			Dict					):
          # returns the index of right child
          return 2 * i + 2







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Union[str, Any]					):
          # returns the index of parent
          return math.floor(i / 2					)







      def    snake_case						(  self       :			Any			,							snake_case       :			Optional[Any]			,							snake_case       :			str					):
          # swaps array elements at indices i and j
          # update the pos{}
          __UpperCamelCase			=    j
          __UpperCamelCase			=    i
          __UpperCamelCase			=    self.array[i]
          __UpperCamelCase			=    self.array[j]
          __UpperCamelCase			=    temp







      def    snake_case						(  self       :			Dict			,							snake_case       :			Optional[int]			,							snake_case       :			List[Any]					):
          __UpperCamelCase			=    self.pos[tup[1]]
          # assuming the new_d is atmost old_d
          __UpperCamelCase			=    (new_d, tup[1])
          while idx > 0 and self.array[self.par(snake_case					)][0] > self.array[idx][0]:
              self.swap(snake_case			,							self.par(snake_case					)					)
              __UpperCamelCase			=    self.par(snake_case					)







class 			_lowerCamelCase    :

      """simple docstring"""

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







      def    snake_case						(  self       :			str			,							snake_case       :			Optional[int]			,							snake_case       :			Union[str, Any]			,							snake_case       :			Optional[Any]					):
          #  Edge going from node u to v and v to u with weight w
          # u (w)-> v, v (w) -> u
          # Check if u already in graph
          if u in self.adjList:
              self.adjList[u].append((v, w)					)
          else:
              __UpperCamelCase			=    [(v, w)]

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







      def    snake_case						(  self       :			Optional[int]					):
          # u -> v(w)
          for u in self.adjList:
              print(snake_case			,							'''->'''			,							''' -> '''.join(str(F"{v}({w})"					) for v, w in self.adjList[u]					)					)







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[int]					):
          # Flush old junk values in par[]
          __UpperCamelCase			=    [-1] * self.num_nodes
          # src is the source node
          __UpperCamelCase			=    0
          __UpperCamelCase			=    PriorityQueue()
          q.insert((0, src)					)  # (dist from src, node)
          for u in self.adjList:
              if u != src:
                  __UpperCamelCase			=    sys.maxsize  # Infinity
                  __UpperCamelCase			=    -1

          while not q.is_empty():
              __UpperCamelCase			=    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]:
                  __UpperCamelCase			=    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)			,							snake_case					)
                      __UpperCamelCase			=    new_dist
                      __UpperCamelCase			=    u

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







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			List[str]					):
          print(F"Distance from node: {src}"					)
          for u in range(self.num_nodes					):
              print(F"Node {u} has distance: {self.dist[u]}"					)







      def    snake_case						(  self       :			Dict			,							snake_case       :			Optional[int]			,							snake_case       :			Optional[Any]					):
          # To show the shortest path from src to dest
          # WARNING: Use it *after* calling dijkstra
          __UpperCamelCase			=    []
          __UpperCamelCase			=    0
          __UpperCamelCase			=    dest
          # Backtracking from dest to src
          while self.par[temp] != -1:
              path.append(snake_case					)
              if temp != src:
                  for v, w in self.adjList[temp]:
                      if v == self.par[temp]:
                          cost += w
                          break
              __UpperCamelCase			=    self.par[temp]
          path.append(snake_case					)
          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: '''			,							snake_case					)


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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


    for attribute in key.split('''.'''   ):
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   )

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

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    []
    __UpperCamelCase			=    fairseq_model.state_dict()

    __UpperCamelCase			=    hf_model.feature_extractor

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

                if key in name:
                    __UpperCamelCase			=    True
                    if "*" in mapped_key:
                        __UpperCamelCase			=    name.split(lowercase_   )[0].split('''.'''   )[-2]
                        __UpperCamelCase			=    mapped_key.replace('''*'''       ,  lowercase_   )
                    if "weight_g" in name:
                        __UpperCamelCase			=    '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase			=    '''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase			=    '''weight'''
                    elif "bias" in name:
                        __UpperCamelCase			=    '''bias'''
                    else:
                        __UpperCamelCase			=    None
                    set_recursively(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )
                continue
        if not is_used:
            unused_weights.append(lowercase_   )

    logger.warning(F"Unused weights: {unused_weights}"   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    __UpperCamelCase			=    full_name.split('''conv_layers.'''   )[-1]
    __UpperCamelCase			=    name.split('''.'''   )
    __UpperCamelCase			=    int(items[0]   )
    __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
    else:
        unused_weights.append(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    HubertConfig()
    __UpperCamelCase			=    model.config

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

    return config




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=None   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    distilhubert().model.model

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

    __UpperCamelCase			=    WavaVecaFeatureExtractor(
        feature_size=1       ,  sampling_rate=1_60_00       ,  padding_value=0       ,  do_normalize=lowercase_       ,  return_attention_mask=lowercase_       ,  )
    __UpperCamelCase			=    HubertModel(lowercase_   )

    recursively_load_weights(lowercase_       ,  lowercase_   )

    feature_extractor.save_pretrained(lowercase_   )
    hf_model.save_pretrained(lowercase_   )


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)






