
"""simple docstring"""

import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

    # Embeddings
    # Position IDs
    UpperCAmelCase       =   fnet_pretraining_model.state_dict()

    UpperCAmelCase       =   state_dict['fnet.embeddings.position_ids']
    UpperCAmelCase       =   {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']	)

    # Pooler Layers
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

    # Masked LM Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']	)
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

    # Seq Relationship Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_kernel']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_bias']	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(lowerCAmelCase	)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--fnet_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained FNet model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)





"""simple docstring"""

import math
import sys
class        _UpperCAmelCase							:




      def __init__(							self							)  ->   List[str]:
          UpperCAmelCase       =   0
          UpperCAmelCase       =   []
          UpperCAmelCase       =   {}  # To store the pos of node in array




      def       a_ (							self							)  ->   Dict:
          return self.cur_size == 0




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   self.left(lowercase_							)
          UpperCAmelCase       =   self.right(lowercase_							)
          if lc < self.cur_size and self.array(lowercase_							)[0] < self.array(lowercase_							)[0]:
              UpperCAmelCase       =   lc
          else:
              UpperCAmelCase       =   idx
          if rc < self.cur_size and self.array(lowercase_							)[0] < self.array(lowercase_							)[0]:
              UpperCAmelCase       =   rc
          if smallest != idx:
              self.swap(lowercase_				,    lowercase_							)
              self.min_heapify(lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          # 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       a_ (							self							)  ->   int:
          # 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       a_ (							self				,    lowercase_							)  ->   Optional[int]:
          # returns the index of left child
          return 2 * i + 1




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:
          # returns the index of right child
          return 2 * i + 2




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:
          # returns the index of parent
          return math.floor(i / 2							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[Any]:
          # 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       a_ (							self				,    lowercase_				,    lowercase_							)  ->   int:
          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(lowercase_							)][0] > self.array[idx][0]:
              self.swap(lowercase_				,    self.par(lowercase_							)							)
              UpperCAmelCase       =   self.par(lowercase_							)

class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_							)  ->   Union[str, 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       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   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       a_ (							self							)  ->   Optional[Any]:
          # u -> v(w)
          for u in self.adjList:
              print(lowercase_				,    '->'				,    ' -> '.join(str(F"{v}({w})"							) for v, w in self.adjList[u]							)							)




      def       a_ (							self				,    lowercase_							)  ->   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)				,    lowercase_							)
                      UpperCAmelCase       =   new_dist
                      UpperCAmelCase       =   u

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




      def       a_ (							self				,    lowercase_							)  ->   Any:
          print(F"Distance from node: {src}"							)
          for u in range(self.num_nodes							):
              print(F"Node {u} has distance: {self.dist[u]}"							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[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(lowercase_							)
              if temp != src:
                  for v, w in self.adjList[temp]:
                      if v == self.par[temp]:
                          cost += w
                          break
              UpperCAmelCase       =   self.par[temp]
          path.append(lowercase_							)
          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: '				,    lowercase_							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  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



