




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

from accelerate.utils.dataclasses import DistributedType






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=3      ,				__UpperCAmelCase=64      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__				:  List[str]		       =     np.random.default_rng(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     length
        lowerCAmelCase__				:  int		       =     rng.normal(size=(length,)		).astype(np.floataa		)
        lowerCAmelCase__				:  List[str]		       =     a * self.x + b + rng.normal(scale=0.1      ,				size=(length,)		).astype(np.floataa		)

      def __len__(   self		):
        return self.length







      def __getitem__(   self      ,				__UpperCAmelCase		):
        return {"x": self.x[i], "y": self.y[i]}






class  _lowerCAmelCase     (  torch.nn.Module	):

      def __init__(   self      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=False		):
        super().__init__()
        lowerCAmelCase__				:  Union[str, Any]		       =     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
        lowerCAmelCase__				:  Optional[int]		       =     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
        lowerCAmelCase__				:  Dict		       =     True







      def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
        if self.first_batch:
          print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
          lowerCAmelCase__				:  int		       =     False
        return x * self.a[0] + self.b[0]






class  _lowerCAmelCase     (  torch.nn.Module	):

      def __init__(   self      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=False		):
        super().__init__()
        lowerCAmelCase__				:  List[str]		       =     torch.nn.Parameter(torch.tensor(__UpperCAmelCase		).float()		)
        lowerCAmelCase__				:  Dict		       =     torch.nn.Parameter(torch.tensor(__UpperCAmelCase		).float()		)
        lowerCAmelCase__				:  Union[str, Any]		       =     True







      def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
        if self.first_batch:
          print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
          lowerCAmelCase__				:  Dict		       =     False
        return x * self.a + self.b



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = 16      )     ->	Tuple:
  from datasets import load_dataset
  from transformers import AutoTokenizer

  lowerCAmelCase__				:  Any		       =     AutoTokenizer.from_pretrained('''bert-base-cased'''      )
  lowerCAmelCase__				:  int		       =     {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
  lowerCAmelCase__				:  Dict		       =     load_dataset('''csv'''	,      data_files=UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     datasets['''train'''].unique('''label'''      )

  lowerCAmelCase__				:  Union[str, Any]		       =     {v: i for i, v in enumerate(UpperCamelCase      )}

  def tokenize_function(UpperCamelCase      ):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase__				:  str		       =     tokenizer(
        examples['''sentence1''']	,      examples['''sentence2''']	,      truncation=UpperCamelCase	,      max_length=UpperCamelCase	,      padding='''max_length'''      )
    if "label" in examples:
      lowerCAmelCase__				:  Union[str, Any]		       =     [label_to_id[l] for l in examples['''label''']]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  lowerCAmelCase__				:  str		       =     datasets.map(
      UpperCamelCase	,      batched=UpperCamelCase	,      remove_columns=['''sentence1''', '''sentence2''', '''label''']	,      )

  def collate_fn(UpperCamelCase      ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(UpperCamelCase	,      padding='''max_length'''	,      max_length=128	,      return_tensors='''pt'''      )
    return tokenizer.pad(UpperCamelCase	,      padding='''longest'''	,      return_tensors='''pt'''      )

  # Instantiate dataloaders.
  lowerCAmelCase__				:  Optional[Any]		       =     DataLoader(tokenized_datasets['''train''']	,      shuffle=UpperCamelCase	,      collate_fn=UpperCamelCase	,      batch_size=2      )
  lowerCAmelCase__				:  Any		       =     DataLoader(tokenized_datasets['''validation''']	,      shuffle=UpperCamelCase	,      collate_fn=UpperCamelCase	,      batch_size=1      )

  return train_dataloader, eval_dataloader







import numpy as np






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     (0, 0)
        lowerCAmelCase__				:  Optional[int]		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     0
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Tuple		       =     0

      def __eq__(   self      ,				__UpperCAmelCase		):
        return self.position == cell.position







      def 	__magic_name__(   self		):
        print(self.position		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=(5, 5)		):
        lowerCAmelCase__				:  List[str]		       =     np.zeros(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     world_size[0]
        lowerCAmelCase__				:  str		       =     world_size[1]

      def 	__magic_name__(   self		):
        print(self.w		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        lowerCAmelCase__				:  Tuple		       =     cell.position[0]
        lowerCAmelCase__				:  Optional[int]		       =     cell.position[1]
        lowerCAmelCase__				:  Any		       =     []
        for n in neughbour_cord:
          lowerCAmelCase__				:  int		       =     current_x + n[0]
          lowerCAmelCase__				:  Dict		       =     current_y + n[1]
          if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            lowerCAmelCase__				:  Optional[int]		       =     Cell()
            lowerCAmelCase__				:  Optional[int]		       =     (x, y)
            lowerCAmelCase__				:  Any		       =     cell
            neighbours.append(__UpperCAmelCase		)
        return neighbours



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  int		       =     []
  lowerCAmelCase__				:  int		       =     []
  _open.append(UpperCamelCase      )

  while _open:
    lowerCAmelCase__				:  Dict		       =     np.argmin([n.f for n in _open]      )
    lowerCAmelCase__				:  Dict		       =     _open[min_f]
    _closed.append(_open.pop(UpperCamelCase      )      )
    if current == goal:
      break
    for n in world.get_neigbours(UpperCamelCase      ):
      for c in _closed:
        if c == n:
          continue
      lowerCAmelCase__				:  Any		       =     current.g + 1
      lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     n.position
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     goal.position
      lowerCAmelCase__				:  Tuple		       =     (ya - ya) ** 2 + (xa - xa) ** 2
      lowerCAmelCase__				:  Optional[Any]		       =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     []
  while current.parent is not None:
    path.append(current.position      )
    lowerCAmelCase__				:  List[Any]		       =     current.parent
  path.append(current.position      )
  return path[::-1]


if __name__ == "__main__":
     lowerCAmelCase_					    = Gridworld()
     # Start position and goal
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (0, 0)
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (4, 4)
     print(F"""path from {start.position} to {goal.position}""")
     lowerCAmelCase_					    = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCAmelCase_					    = 1
     print(world.w)

