




import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__       :      List[Any]  =   '''us-east-1'''  # defaults region




@dataclass
class 		UpperCamelCase__	:

    '''simple docstring'''



    lowerCamelCase_      :	str
    lowerCamelCase_      :	Tuple	     =      '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
    lowerCamelCase_      :	str	     =      {
        '''task_name''': '''mnli''',
        '''per_device_train_batch_size''': 1_6,
        '''per_device_eval_batch_size''': 1_6,
        '''do_train''': True,
        '''do_eval''': True,
        '''do_predict''': True,
        '''output_dir''': '''/opt/ml/model''',
        '''overwrite_output_dir''': True,
        '''max_steps''': 5_0_0,
        '''save_steps''': 5_5_0_0,
    }
    lowerCamelCase_      :	Optional[int]	     =      {**hyperparameters, '''max_steps''': 1_0_0_0}







    @property
    def     _lowercase     (    self     )     ->      str:
           if self.framework == "pytorch":
                  return [
                      {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                      {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                      {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                  ]
           else:
                  return [
                      {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                      {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                      {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                  ]







    @property
    def     _lowercase     (    self     )     ->      List[Any]:
           return F'''{self.framework}-transfromers-test'''







    @property
    def     _lowercase     (    self     )     ->      Any:
           return F'''./tests/sagemaker/scripts/{self.framework}'''







    @property
    def     _lowercase     (    self     )     ->      Dict:
           if self.framework == "pytorch":
                  return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
           else:
                  return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope="class"    )
def      A      (      _SCREAMING_SNAKE_CASE    )	-> Dict:
       lowerCamelCase   :      Optional[int]			 =					SageMakerTestEnvironment(framework=request.cls.framework    )













from heapq import heappop, heappush

import numpy as np







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      tuple[float | int, list[tuple[int, int]]]:
					__A    ,				__A	:						int       =				grid.shape
					__A	:						Any       =				[-1, 1, 0, 0]
					__A	:						Optional[Any]       =				[0, 0, -1, 1]
					if allow_diagonal:
										dx += [-1, -1, 1, 1]
										dy += [-1, 1, -1, 1]

					__A    ,				__A	:						Optional[int]       =				[(0, source)], set()
					__A	:						Any       =				np.full((rows, cols)					,						np.inf  )
					__A	:						Any       =				0
					__A	:						Any       =				np.empty((rows, cols)					,						dtype=a  )
					__A	:						Optional[Any]       =				None

					while queue:
										((__A)    ,				(__A))	:						List[str]       =				heappop(a  )
										if (x, y) in visited:
															continue
										visited.add((x, y)  )

										if (x, y) == destination:
															__A	:						int       =				[]
															while (x, y) != source:
																				path.append((x, y)  )
																				__A    ,				__A	:						Optional[int]       =				predecessors[x, y]
															path.append(a  )  # add the source manually
															path.reverse()
															return matrix[destination], path

										for i in range(len(a  )  ):
															__A    ,				__A	:						Union[str, Any]       =				x + dx[i], y + dy[i]
															if 0 <= nx < rows and 0 <= ny < cols:
																				__A	:						Optional[int]       =				grid[nx][ny]
																				if next_node == 1 and matrix[nx, ny] > dist + 1:
																									heappush(a					,						(dist + 1, (nx, ny))  )
																									__A	:						List[Any]       =				dist + 1
																									__A	:						Union[str, Any]       =				(x, y)

					return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()




