






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 						SCREAMING_SNAKE_CASE				( snake_case					):





       """simple docstring"""

       def       __A (  self:   Any		,     __A:   Union[str, Any]   )    ->				Optional[Any]:
            os.makedirs(__A		,     exist_ok=__A   )
            _A								= {'''source''': '''What is love ?''', '''target''': '''life'''}
            _A								= {'''train''': 12, '''val''': 2, '''test''': 2}
            for split in ["train", "test", "val"]:
                 for field in ["source", "target"]:
                      _A								= '''\n'''.join([contents[field]] * n_lines[split]   )
                      with open(os.path.join(__A		,     f"""{split}.{field}"""   )		,     '''w'''   ) as f:
                           f.write(__A   )

       def       __A (  self:   Dict		,     __A:   int		,     __A:   str = "pytorch"   )    ->				Union[str, Any]:
            _A								= self.get_auto_remove_tmp_dir()
            _A								= os.path.join(__A		,     '''output'''   )
            _A								= os.path.join(__A		,     '''data'''   )
            self._create_dummy_data(data_dir=__A   )

            _A								= 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'''   )

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

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

       @require_torch_gpu
       def       __A (  self:   Any   )    ->				Any:
            _A								= self._run_finetune(gpus=1   )
            self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,     0.2   )

       @require_torch_multi_gpu
       def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
            _A								= self._run_finetune(gpus=2   )
            self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,     0.2   )

       @require_torch_gpu
       @require_ray
       def       __A (  self:   str   )    ->				Tuple:
            _A								= 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       __A (  self:   List[Any]   )    ->				Optional[int]:
            _A								= self._run_finetune(gpus=1		,     distributed_retriever='''ray'''   )
            self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,     0.2   )











def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [0] * len(_lowercase )
					_A								= []
					_A								= []
					_A								= 0

					for values in graph.values():
										for i in values:
															indegree[i] += 1

					for i in range(len(_lowercase ) ):
										if indegree[i] == 0:
															queue.append(_lowercase )

					while queue:
										_A								= queue.pop(0 )
										cnt += 1
										topo.append(_lowercase )
										for x in graph[vertex]:
															indegree[x] -= 1
															if indegree[x] == 0:
																				queue.append(_lowercase )

					if cnt != len(_lowercase ):
										print('''Cycle exists''' )
					else:
										print(_lowercase )


# Adjacency List of Graph
__A      =				{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}


topological_sort(graph)



