



"""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)
_UpperCAmelCase     =			logging.getLogger()

_UpperCAmelCase     =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 					a	(  lowercase__       ):



		def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					Tuple		)     ->					Dict:


					'''simple docstring'''




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



		def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					str = "pytorch"		)     ->					str:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_: Optional[int]     			=os.path.join(__lowercase       ,		"""output"""		)
					SCREAMING_SNAKE_CASE_: List[Any]     			=os.path.join(__lowercase       ,		"""data"""		)
					self._create_dummy_data(data_dir=__lowercase		)

					SCREAMING_SNAKE_CASE_: Optional[int]     			=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"""		)

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

					SCREAMING_SNAKE_CASE_: Optional[int]     			=os.path.join(__lowercase       ,		"""metrics.json"""		)
					with open(__lowercase		) as f:
								SCREAMING_SNAKE_CASE_: Union[str, Any]     			=json.load(__lowercase		)
					return result



		@require_torch_gpu
		def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					int:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Optional[int]     			=self._run_finetune(gpus=1		)
					self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,		0.2		)



		@require_torch_multi_gpu
		def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[int]:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Dict     			=self._run_finetune(gpus=2		)
					self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,		0.2		)



		@require_torch_gpu
		@require_ray
		def 	lowerCamelCase__       (						self :					Tuple		)     ->					Dict:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: str     			=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 :					Tuple		)     ->					Tuple:


					'''simple docstring'''




					SCREAMING_SNAKE_CASE_: Tuple     			=self._run_finetune(gpus=1       ,		distributed_retriever="""ray"""		)
					self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,		0.2		)








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      SCREAMING_SNAKE_CASE_: Tuple     			=True
      for i in range(0							,			len(lowercase ) - 1							,			2 ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: Tuple     			=False

      for i in range(1							,			len(lowercase ) - 1							,			2 ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: str     			=False
   return input_list


if __name__ == "__main__":
   print("""Enter list to be sorted""")
   _UpperCAmelCase     =			[int(x) for x in input().split()]
   # inputing elements of the list in one line
   _UpperCAmelCase     =			odd_even_sort(input_list)
   print("""The sorted list is""")
   print(sorted_list)



