



'''simple docstring'''



import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase	 =       logging.getLogger()

lowerCamelCase	 =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class _UpperCamelCase		(     UpperCamelCase__		):





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  Optional[int]  ,       _lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											os.makedirs(__A  ,       exist_ok=__A)
											__lowercase       	=    {'source': 'What is love ?', 'target': 'life'}
											__lowercase       	=    {'train': 1_2, 'val': 2, 'test': 2}
											for split in ["train", "test", "val"]:
																		for field in ["source", "target"]:
																									__lowercase       	=    '\n'.join([contents[field]] * n_lines[split])
																									with open(os.path.join(__A  ,       f"""{split}.{field}""")  ,       'w') as f:
																																f.write(__A)

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  str = "pytorch"):







											'''simple docstring'''

											__lowercase       	=    self.get_auto_remove_tmp_dir()
											__lowercase       	=    os.path.join(__A  ,       'output')
											__lowercase       	=    os.path.join(__A  ,       'data')
											self._create_dummy_data(data_dir=__A)

											__lowercase       	=    f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

											if gpus > 0:
																		testargs.append(f"""--gpus={gpus}""")
																		if is_apex_available():
																									testargs.append('--fp16')
											else:
																		testargs.append('--gpus=0')
																		testargs.append('--distributed_backend=ddp_cpu')
																		testargs.append('--num_processes=2')

											__lowercase       	=    [sys.executable, str(Path(finetune_rag.__file__).resolve())] + testargs
											execute_subprocess_async(__A  ,       env=self.get_env())

											__lowercase       	=    os.path.join(__A  ,       'metrics.json')
											with open(__A) as f:
																		__lowercase       	=    json.load(__A)
											return result

				@require_torch_gpu
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=    self._run_finetune(gpus=1)
											self.assertGreaterEqual(result['test'][0]['test_avg_em']  ,       0.2)

				@require_torch_multi_gpu
				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

											__lowercase       	=    self._run_finetune(gpus=2)
											self.assertGreaterEqual(result['test'][0]['test_avg_em']  ,       0.2)

				@require_torch_gpu
				@require_ray
				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__lowercase       	=    self._run_finetune(gpus=1  ,       distributed_retriever='ray')
											self.assertGreaterEqual(result['test'][0]['test_avg_em']  ,       0.2)

				@require_torch_multi_gpu
				@require_ray
				def 				__lowerCamelCase     (							self  :  int):







											'''simple docstring'''

											__lowercase       	=    self._run_finetune(gpus=1  ,       distributed_retriever='ray')
											self.assertGreaterEqual(result['test'][0]['test_avg_em']  ,       0.2)








'''simple docstring'''



import numpy as np





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=(0, 0)
											__lowercase       	=None
											__lowercase       	=0
											__lowercase       	=0
											__lowercase       	=0

				def __eq__(							self  :  List[str]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.position == cell.position

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											print(self.position)





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  List[str]=(5, 5)):







											'''simple docstring'''

											__lowercase       	=np.zeros(_lowerCAmelCase)
											__lowercase       	=world_size[0]
											__lowercase       	=world_size[1]

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											print(self.w)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[str]):







											'''simple docstring'''

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=[]
							__lowercase       	=[]
							_open.append(_lowerCAmelCase					)

							while _open:
														__lowercase       	=np.argmin([n.f for n in _open]					)
														__lowercase       	=_open[min_f]
														_closed.append(_open.pop(_lowerCAmelCase					)					)
														if current == goal:
																					break
														for n in world.get_neigbours(_lowerCAmelCase					):
																					for c in _closed:
																												if c == n:
																																			continue
																					__lowercase       	=current.g + 1
																					__lowercase	,				__lowercase       	=n.position
																					__lowercase	,				__lowercase       	=goal.position
																					__lowercase       	=(ya - ya) ** 2 + (xa - xa) ** 2
																					__lowercase       	=n.h + n.g

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



