






import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class       __a  (   A__	):







 def __init__(		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  params

      UpperCamelCase__      :       Tuple	    =  np.array(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       int	    =  np.array([len(SCREAMING_SNAKE_CASE						) for t in data]						)

      self.check()
      self.remove_long_sequences()
      self.remove_empty_sequences()
      self.remove_unknown_sequences()
      self.check()
      self.print_statistics()







 def __getitem__(		self :       List[str]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]						):


      '''simple docstring'''






      return (self.token_ids[index], self.lengths[index])







 def __len__(		self :       Optional[Any]						):


      '''simple docstring'''






      return len(self.lengths						)







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      assert len(self.token_ids						) == len(self.lengths						)
      assert all(self.lengths[i] == len(self.token_ids[i]						) for i in range(len(self.lengths						)						)						)







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.params.max_model_input_size
      UpperCamelCase__      :       str	    =  self.lengths > max_len
      logger.info(F'Splitting {sum(SCREAMING_SNAKE_CASE						)} too long sequences.'						)

      def divide_chunks(SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Optional[int]						):
           return [l[i : i + n] for i in range(0							,       len(SCREAMING_SNAKE_CASE						)							,       SCREAMING_SNAKE_CASE						)]

      UpperCamelCase__      :       Optional[int]	    =  []
      UpperCamelCase__      :       Dict	    =  []
      if self.params.mlm:
           UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
      else:
           UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

      for seq_, len_ in zip(self.token_ids							,       self.lengths						):
           assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
           if len_ <= max_len:
                new_tok_ids.append(seq_						)
                new_lengths.append(len_						)
           else:
                UpperCamelCase__      :       Tuple	    =  []
                for sub_s in divide_chunks(seq_							,       max_len - 2						):
                     if sub_s[0] != cls_id:
                          UpperCamelCase__      :       Optional[int]	    =  np.insert(SCREAMING_SNAKE_CASE							,       0							,       SCREAMING_SNAKE_CASE						)
                     if sub_s[-1] != sep_id:
                          UpperCamelCase__      :       Optional[Any]	    =  np.insert(SCREAMING_SNAKE_CASE							,       len(SCREAMING_SNAKE_CASE						)							,       SCREAMING_SNAKE_CASE						)
                     assert len(SCREAMING_SNAKE_CASE						) <= max_len
                     assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                     sub_seqs.append(SCREAMING_SNAKE_CASE						)

                new_tok_ids.extend(SCREAMING_SNAKE_CASE						)
                new_lengths.extend([len(SCREAMING_SNAKE_CASE						) for l in sub_seqs]						)

      UpperCamelCase__      :       Union[str, Any]	    =  np.array(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       int	    =  np.array(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  len(self						)
      UpperCamelCase__      :       Optional[int]	    =  self.lengths > 11
      UpperCamelCase__      :       List[str]	    =  self.token_ids[indices]
      UpperCamelCase__      :       int	    =  self.lengths[indices]
      UpperCamelCase__      :       Any	    =  len(self						)
      logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.'						)







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      if "unk_token" not in self.params.special_tok_ids:
           return
      else:
           UpperCamelCase__      :       List[Any]	    =  self.params.special_tok_ids["unk_token"]
      UpperCamelCase__      :       Dict	    =  len(self						)
      UpperCamelCase__      :       Union[str, Any]	    =  np.array([np.count_nonzero(a == unk_token_id						) for a in self.token_ids]						)
      UpperCamelCase__      :       Dict	    =  (unk_occs / self.lengths) < 0.5
      UpperCamelCase__      :       Union[str, Any]	    =  self.token_ids[indices]
      UpperCamelCase__      :       Optional[Any]	    =  self.lengths[indices]
      UpperCamelCase__      :       Any	    =  len(self						)
      logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'						)







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      if not self.params.is_master:
           return
      logger.info(F'{len(self						)} sequences'						)
      # data_len = sum(self.lengths)
      # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
      # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

      # unk_idx = self.params.special_tok_ids['unk_token']
      # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
      # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  [t[0] for t in batch]
      UpperCamelCase__      :       Any	    =  [t[1] for t in batch]
      assert len(SCREAMING_SNAKE_CASE						) == len(SCREAMING_SNAKE_CASE						)

      # Max for paddings
      UpperCamelCase__      :       Union[str, Any]	    =  max(SCREAMING_SNAKE_CASE						)

      # Pad token ids
      if self.params.mlm:
           UpperCamelCase__      :       List[Any]	    =  self.params.special_tok_ids["pad_token"]
      else:
           UpperCamelCase__      :       Union[str, Any]	    =  self.params.special_tok_ids["unk_token"]
      UpperCamelCase__      :       Tuple	    =  [list(t.astype(SCREAMING_SNAKE_CASE						)						) + [pad_idx] * (max_seq_len_ - len(SCREAMING_SNAKE_CASE						)) for t in token_ids]
      assert len(tk_						) == len(SCREAMING_SNAKE_CASE						)
      assert all(len(SCREAMING_SNAKE_CASE						) == max_seq_len_ for t in tk_						)

      UpperCamelCase__      :       int	    =  torch.tensor(tk_						)  # (bs, max_seq_len_)
      UpperCamelCase__      :       List[Any]	    =  torch.tensor(SCREAMING_SNAKE_CASE						)  # (bs)
      return tk_t, lg_t







lowerCamelCase     :							Optional[int]    ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       Optional[Any]	    =  [[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

    # in case there's no path between the 2 nodes
     return []







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     UpperCamelCase__      :       Tuple	    =  [start]
     UpperCamelCase__      :       Optional[int]	    =  set(__lowerCAmelCase      )
     # Keep tab on distances from `start` node.
     UpperCamelCase__      :       str	    =  {start: 0, target: -1}
     while queue:
          UpperCamelCase__      :       Any	    =  queue.pop(0      )
          if node == target:
               UpperCamelCase__      :       Union[str, Any]	    =  (
                   dist[node] if dist[target] == -1 else min(dist[target] ,   dist[node]      )
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4