
"""simple docstring"""
from __future__ import annotations

lowercase_       :						Any     					=	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							list[list[int]],						lowerCamelCase__							:							list[int],						lowerCamelCase__							:							list[int],						lowerCamelCase__							:							int,						lowerCamelCase__							:							list[list[int]],						)  ->    tuple[list[list[int]], list[list[int]]]:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = [
      [0 for col in range(len(grid[0]				)				)] for row in range(len(lowerCamelCase__				)				)
  ]  # the reference grid
  _SCREAMING_SNAKE_CASE   :				str							    = 1
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [
      [0 for col in range(len(grid[0]				)				)] for row in range(len(lowerCamelCase__				)				)
  ]  # the action grid

  _SCREAMING_SNAKE_CASE   :				Any							    = init[0]
  _SCREAMING_SNAKE_CASE   :				str							    = init[1]
  _SCREAMING_SNAKE_CASE   :				List[str]							    = 0
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = g + heuristic[x][y]  # cost from starting cell to destination cell
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [[f, g, x, y]]

  _SCREAMING_SNAKE_CASE   :				List[str]							    = False  # flag that is set when search is complete
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = False  # flag set if we can't find expand

  while not found and not resign:
    if len(lowerCamelCase__				) == 0:
      raise ValueError("Algorithm is unable to find solution"				)
    else:  # to choose the least costliest action so as to move closer to the goal
      cell.sort()
      cell.reverse()
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = cell.pop()
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = next_cell[2]
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = next_cell[3]
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = next_cell[1]

      if x == goal[0] and y == goal[1]:
        _SCREAMING_SNAKE_CASE   :				List[str]							    = True
      else:
        for i in range(len(lowerCamelCase__				)				):  # to try out different valid actions
          _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = x + DIRECTIONS[i][0]
          _SCREAMING_SNAKE_CASE   :				Tuple							    = y + DIRECTIONS[i][1]
          if xa >= 0 and xa < len(lowerCamelCase__				) and ya >= 0 and ya < len(grid[0]				):
            if closed[xa][ya] == 0 and grid[xa][ya] == 0:
              _SCREAMING_SNAKE_CASE   :				List[str]							    = g + cost
              _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = ga + heuristic[xa][ya]
              cell.append([fa, ga, xa, ya]				)
              _SCREAMING_SNAKE_CASE   :				Dict							    = 1
              _SCREAMING_SNAKE_CASE   :				List[str]							    = i
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = []
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = goal[0]
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = goal[1]
  invpath.append([x, y]				)  # we get the reverse path from here
  while x != init[0] or y != init[1]:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = x - DIRECTIONS[action[x][y]][0]
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = y - DIRECTIONS[action[x][y]][1]
    _SCREAMING_SNAKE_CASE   :				Any							    = xa
    _SCREAMING_SNAKE_CASE   :				List[str]							    = ya
    invpath.append([x, y]				)

  _SCREAMING_SNAKE_CASE   :				str							    = []
  for i in range(len(lowerCamelCase__				)				):
    path.append(invpath[len(lowerCamelCase__				) - 1 - i]				)
  return path, action


if __name__ == "__main__":
      lowercase_       :						str     					=	[
          [0, 1, 0, 0, 0, 0],
          [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
          [0, 1, 0, 0, 0, 0],
          [0, 1, 0, 0, 1, 0],
          [0, 0, 0, 0, 1, 0],
      ]

      lowercase_       :						List[str]     					=	[0, 0]
      # all coordinates are given in format [y,x]
      lowercase_       :						Dict     					=	[len(grid) - 1, len(grid[0]) - 1]
      lowercase_       :						Union[str, Any]     					=	1

      # the cost map which pushes the path closer to the goal
      lowercase_       :						Union[str, Any]     					=	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
      for i in range(len(grid)):
            for j in range(len(grid[0])):
                  lowercase_       :						int     					=	abs(i - goal[0]) + abs(j - goal[1])
                  if grid[i][j] == 1:
                        # added extra penalty in the heuristic map
                        lowercase_       :						str     					=	99

      lowercase_					,	lowercase_       :						List[str]     					=	search(grid, init, goal, cost, heuristic)

      print('''ACTION MAP''')
      for i in range(len(action)):
            print(action[i])

      for i in range(len(path)):
            print(path[i])





"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be trained."""}  )
     A__      =			field(
         default="""./"""      ,       metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path of training dataset."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for training."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size for evaluation."""}  )
     A__      =			field(default=0.1      ,       metadata={"""help""": """Value of weight decay."""}  )
     A__      =			field(
         default=10000      ,       metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}  )
     A__      =			field(default=2e-4      ,       metadata={"""help""": """Learning rate fo training."""}  )
     A__      =			field(default="""cosine"""      ,       metadata={"""help""": """Learning rate."""}  )
     A__      =			field(
         default=750      ,       metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}  )
     A__      =			field(
         default=16      ,       metadata={"""help""": """Number of gradient accumulation steps."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}  )
     A__      =			field(default=50000      ,       metadata={"""help""": """Maximum number of training steps."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Sequence lengths used for training."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Training seed."""}  )
     A__      =			field(
         default=1024      ,       metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True the data is pretokenized."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-valid"""      ,       metadata={"""help""": """Name or path of validation dataset."""}  )
     A__      =			field(default=2      ,       metadata={"""help""": """Batch size used for evaluation."""}  )
     A__      =			field(
         default=-1      ,       metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}  )
     A__      =			field(default=1024      ,       metadata={"""help""": """Length of sequences to be evaluated."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Model name or path of model to be evaluated."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Sample from the language model's output distribution."""}  )
     A__      =			field(default=0.2      ,       metadata={"""help""": """Sampling temperature used for generation."""}  )
     A__      =			field(default=256      ,       metadata={"""help""": """Maximum number of newly generated tokens."""}  )
     A__      =			field(default=0      ,       metadata={"""help""": """Top-k parameter used for generation."""}  )
     A__      =			field(default=0.95      ,       metadata={"""help""": """Top-p parameter used for nucleus sampling."""}  )
     A__      =			field(default=10      ,       metadata={"""help""": """Number of generations to run in parallel."""}  )
     A__      =			field(
         default=200      ,       metadata={"""help""": """Number of completions to generate for each sample."""}  )
     A__      =			field(default=1      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""eval_results.json"""      ,       metadata={"""help""": """Random seed used for evaluation."""}  )
     A__      =			field(
         default="""0"""      ,       metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}  )
     A__      =			field(
         default=-1      ,       metadata={
             """help""": (
                 """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                 """ number corresponds to which GPU device id to run on."""
             )
         }      ,       )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={
             """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
         }      ,       )
     A__      =			field(
         default="""transformersbook/codeparrot"""      ,       metadata={"""help""": """Folder or name of dataset to process."""}  )
     A__      =			field(
         default="""codeparrot-clean"""      ,       metadata={"""help""": """Folder to save processed processed dataset."""}  )
     A__      =			field(
         default=100000      ,       metadata={"""help""": """Number of files to save per JSON output file."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(
         default=1000      ,       metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=100      ,       metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.25      ,       metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}  )
     A__      =			field(
         default=1.5      ,       metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}  )
     A__      =			field(
         default=0.7      ,       metadata={"""help""": """Probability for filtering config, test and uncommon files."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}      ,       )
     A__      =			field(
         default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """If True, near-duplicate samples are removed."""}  )
     A__      =			field(
         default=0.85      ,       metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2"""      ,       metadata={"""help""": """Base tokenizer to build new tokenizer from."""}  )
     A__      =			field(
         default="""transformersbook/codeparrot-train"""      ,       metadata={"""help""": """Dataset to train tokenizer on."""}  )
     A__      =			field(default="""content"""      ,       metadata={"""help""": """Column containing text data to process."""}  )
     A__      =			field(default=200000      ,       metadata={"""help""": """Number of examples to train tokenizer on."""}  )
     A__      =			field(
         default=32768      ,       metadata={"""help""": """Number of examples to train the tokenizer on."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of new tokenizer."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Name or path to the tokenizer."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot-clean-train"""      ,       metadata={"""help""": """Name or path to the dataset to pretokenize."""}  )
     A__      =			field(
         default="""tokenized-codeparrot-train"""      ,       metadata={"""help""": """Repo name of the pretokenized data."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Number of workers used for code evaluation."""}  )
@dataclass
class 			UpperCamelCase      :
     A__      =			field(
         default="""gpt2-large"""      ,       metadata={"""help""": """Configuration to use for model initialization."""}  )
     A__      =			field(
         default="""codeparrot/codeparrot"""      ,       metadata={"""help""": """Tokenizer attached to model."""}  )
     A__      =			field(default="""codeparrot"""      ,       metadata={"""help""": """Name of the created model."""}  )
     A__      =			field(default=__SCREAMING_SNAKE_CASE      ,       metadata={"""help""": """Push saved tokenizer to the hub."""}  )



