




"""simple docstring"""






from random import randint, random







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = False							,    lowercase__ = False							,    lowercase__ = 5							,    ):
				_lowerCamelCase     :			Tuple    =    [[-1] * number_of_cells]  # Create a highway without any car
				_lowerCamelCase     :			Any    =    0
				_lowerCamelCase     :			str    =    max(lowercase__							,    0		)
				while i < number_of_cells:
								_lowerCamelCase     :			Optional[int]    =    (
								    randint(0							,    lowercase__		) if random_speed else initial_speed
								)  # Place the cars
								i += (
								    randint(1							,    max_speed * 2		) if random_frequency else frequency
								)  # Arbitrary number, may need tuning
				return highway







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			Optional[int]    =    highway_now[car_index + 1 :]
				for cell in range(len(lowercase__		)		):  # May need a better name for this
								if cells[cell] != -1:  # If the cell is not empty then
												return distance  # we have the distance we wanted
								distance += 1
				# Here if the car is near the end of the highway
				return distance + get_distance(lowercase__							,    -1		)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			int    =    len(lowercase__		)
				# Beforce calculations, the highway is empty
				_lowerCamelCase     :			Any    =    [-1] * number_of_cells

				for car_index in range(lowercase__		):
								if highway_now[car_index] != -1:
												# Add 1 to the current speed of the car and cap the speed
												_lowerCamelCase     :			int    =    min(highway_now[car_index] + 1							,    lowercase__		)
												# Number of empty cell before the next car
												_lowerCamelCase     :			int    =    get_distance(lowercase__							,    lowercase__		) - 1
												# We can't have the car causing an accident
												_lowerCamelCase     :			Union[str, Any]    =    min(next_highway[car_index]							,    lowercase__		)
												if random() < probability:
																# Randomly, a driver will slow down
																_lowerCamelCase     :			Optional[int]    =    max(next_highway[car_index] - 1							,    0		)
				return next_highway







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    len(highway[0]		)

				for i in range(lowercase__		):
								_lowerCamelCase     :			Dict    =    update(highway[i]							,    lowercase__							,    lowercase__		)
								_lowerCamelCase     :			Any    =    [-1] * number_of_cells

								for car_index in range(lowercase__		):
												_lowerCamelCase     :			List[str]    =    next_speeds_calculated[car_index]
												if speed != -1:
																# Change the position based on the speed (with % to create the loop)
																_lowerCamelCase     :			str    =    (car_index + speed) % number_of_cells
																# Commit the change of position
																_lowerCamelCase     :			Optional[Any]    =    speed
								highway.append(lowercase__		)

				return highway


if __name__ == "__main__":
			import doctest

			doctest.testmod()







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)
_a      	=  logging.getLogger()

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





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							os.makedirs(UpperCAmelCase       ,    exist_ok=UpperCAmelCase    )
							_UpperCAmelCase									=			{'source': 'What is love ?', 'target': 'life'}
							_UpperCAmelCase									=			{'train': 12, 'val': 2, 'test': 2}
							for split in ["train", "test", "val"]:
													for field in ["source", "target"]:
																			_UpperCAmelCase									=			'\n'.join([contents[field]] * n_lines[split]    )
																			with open(os.path.join(UpperCAmelCase       ,    F"""{split}.{field}"""    )       ,    'w'    ) as f:
																									f.write(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = "pytorch"    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'output'    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'data'    )
							self._create_dummy_data(data_dir=UpperCAmelCase    )

							_UpperCAmelCase									=			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'    )

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

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

	@require_torch_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@require_torch_multi_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@require_torch_gpu
	@require_ray
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







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