



import numpy as np



class 		_snake_case   :





 """simple docstring"""



 def __init__(    self       )						->				Optional[int]:




   """simple docstring"""







   _A							     =     (0, 0)
   _A							     =     None
   _A							     =     0
   _A							     =     0
   _A							     =     0
 def __eq__(    self						,     a       )						->				Optional[int]:




   """simple docstring"""







   return self.position == cell.position



 def lowercase_		(    self       )						->				List[str]:




   """simple docstring"""







   print(self.position       )







class 		_snake_case   :





 """simple docstring"""



 def __init__(    self						,     a=(5, 5)       )						->				List[Any]:




   """simple docstring"""







   _A							     =     np.zeros(a       )
   _A							     =     world_size[0]
   _A							     =     world_size[1]
 def lowercase_		(    self       )						->				Optional[int]:




   """simple docstring"""







   print(self.w       )



 def lowercase_		(    self						,     a       )						->				int:




   """simple docstring"""







   _A							     =     [
       (-1, -1),
       (-1, 0),
       (-1, 1),
       (0, -1),
       (0, 1),
       (1, -1),
       (1, 0),
       (1, 1),
   ]
   _A							     =     cell.position[0]
   _A							     =     cell.position[1]
   _A							     =     []
   for n in neughbour_cord:
     _A							     =     current_x + n[0]
     _A							     =     current_y + n[1]
     if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
       _A							     =     Cell()
       _A							     =     (x, y)
       _A							     =     cell
       neighbours.append(a       )
   return neighbours







def      UpperCAmelCase__			(				__snake_case    ,					__snake_case    ,					__snake_case				) ->			Any:
  _A							     =     []
  _A							     =     []
  _open.append(__UpperCAmelCase				)

  while _open:
    _A							     =     np.argmin([n.f for n in _open]				)
    _A							     =     _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
      _A							     =     current.g + 1
      _A     , _A							     =     n.position
      _A     , _A							     =     goal.position
      _A							     =     (ya - ya) ** 2 + (xa - xa) ** 2
      _A							     =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(__UpperCAmelCase				)
  _A							     =     []
  while current.parent is not None:
    path.append(current.position				)
    _A							     =     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)






'''simple docstring'''

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase=None				,						__UpperCAmelCase=None   )    ->       Dict:

   '''simple docstring'''







   return field(default_factory=lambda: default				,						metadata=__UpperCAmelCase   )







@dataclass
class __a			:
   __UpperCamelCase :			List[str]						   =						list_field(
       default=[], metadata={
           'help': (
               'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
               ' of all available models'
           )
       }, )

   __UpperCamelCase :			List[int]						   =						list_field(
       default=[8], metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}      )

   __UpperCamelCase :			List[int]						   =						list_field(
       default=[8, 32, 128, 512], metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}, )

   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Use FP16 to accelerate inference.'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Benchmark training of model'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Verbose memory tracing'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={
           'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
       }, )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Trace memory line by line'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Save result to a CSV file'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Save all print statements in a log file'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Whether to print environment information'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={
           'help': (
               'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
               ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
               ' for debugging / testing and on TPU.'
           )
       }, )
   __UpperCamelCase :			str						   =						field(
       default=F'inference_time_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving time results to csv.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'inference_memory_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving memory results to csv.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'train_time_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving time results to csv for training.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'train_memory_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving memory results to csv for training.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'env_info_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving environment information.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'log_{round(time()      )}.csv', metadata={'help': 'Log filename used if print statements are saved in log.'}, )
   __UpperCamelCase :			int						   =						field(default=3, metadata={'help': 'Times an experiment will be run.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={
           'help': (
               'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
               ' model weights.'
           )
       }, )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      warnings.warn(
          f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
          """ are deprecated in general and it is advised to use external Benchmarking libraries """
          """ to benchmark Transformer models."""					,lowerCamelCase					,)





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return json.dumps(dataclasses.asdict(self     )					,indent=2     )





   @property
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if len(self.models     ) <= 0:
         raise ValueError(
             """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
             """ bert-base-cased` or `args.models = ['bert-base-cased']."""     )
      return self.models





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      if not self.multi_process:
         return False
      elif self.is_tpu:
         logger.info("""Multiprocessing is currently not possible on TPU."""     )
         return False
      else:
         return True





