





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)
_lowercase		:  Tuple			       =logging.getLogger()

_lowercase		:  Tuple			       =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 						UpperCamelCase_  (  snake_case__							):



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



					def   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     str = "pytorch"						):
							lowerCamelCase_	:     Optional[Any] 						=      self.get_auto_remove_tmp_dir()
							lowerCamelCase_	:     Optional[Any] 						=      os.path.join(lowerCamelCase       ,      'output'						)
							lowerCamelCase_	:     Dict 						=      os.path.join(lowerCamelCase       ,      'data'						)
							self._create_dummy_data(data_dir=lowerCamelCase						)

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

							lowerCamelCase_	:     Optional[Any] 						=      [sys.executable, str(Path(finetune_rag.__file__						).resolve()						)] + testargs
							execute_subprocess_async(lowerCamelCase       ,      env=self.get_env()						)

							lowerCamelCase_	:     Dict 						=      os.path.join(lowerCamelCase       ,      'metrics.json'						)
							with open(lowerCamelCase						) as f:
									lowerCamelCase_	:     int 						=      json.load(lowerCamelCase						)
							return result



					@require_torch_gpu
					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Optional[Any] 						=      self._run_finetune(gpus=1						)
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,      0.2						)



					@require_torch_multi_gpu
					def   __a     (		self     :     int						):
							lowerCamelCase_	:     List[Any] 						=      self._run_finetune(gpus=2						)
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,      0.2						)



					@require_torch_gpu
					@require_ray
					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     List[Any] 						=      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     :     Any						):
							lowerCamelCase_	:     str 						=      self._run_finetune(gpus=1       ,      distributed_retriever='ray'						)
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,      0.2						)







def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Union[str, Any] 						=      []
		lowerCamelCase_	:     Tuple 						=      []
		lowerCamelCase_	:     Dict 						=      {
		    '^': 3,
		    '*': 2,
		    '/': 2,
		    '%': 2,
		    '+': 1,
		    '-': 1,
		}  # Priority of each operator
		lowerCamelCase_	:     Optional[int] 						=      len(lowerCAmelCase__						) if (len(lowerCAmelCase__						) > 7) else 7

		# Print table header for output
		print(
		    'Symbol'.center(8						)							,'Stack'.center(lowerCAmelCase__						)							,'Postfix'.center(lowerCAmelCase__						)							,sep=' | '							,)
		print('-' * (print_width * 3 + 7)						)

		for x in infix:
				if x.isalpha() or x.isdigit():
						post_fix.append(lowerCAmelCase__						)  # if x is Alphabet / Digit, add it to Postfix
				elif x == "(":
						stack.append(lowerCAmelCase__						)  # if x is "(" push to Stack
				elif x == ")":  # if x is ")" pop stack until "(" is encountered
						while stack[-1] != "(":
								post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
						stack.pop()
				else:
						if len(lowerCAmelCase__						) == 0:
								stack.append(lowerCAmelCase__						)  # If stack is empty, push x to stack
						else:  # while priority of x is not > priority of element in the stack
								while len(lowerCAmelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
										post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								stack.append(lowerCAmelCase__						)  # push x to stack

				print(
				    x.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		while len(lowerCAmelCase__						) > 0:  # while stack is not empty
				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
				print(
				    ' '.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		return "".join(lowerCAmelCase__						)  # return Postfix as str


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      list(infix[::-1]						)  # reverse the infix equation

		for i in range(len(lowerCAmelCase__						)						):
				if infix[i] == "(":
						lowerCamelCase_	:     str 						=      ')'  # change "(" to ")"
				elif infix[i] == ")":
						lowerCamelCase_	:     Optional[Any] 						=      '('  # change ")" to "("

		return (infix_2_postfix(''.join(lowerCAmelCase__						)						))[
		    ::-1
		]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		_lowercase		:  int			       =input("""\nEnter an Infix Equation = """)  # Input an Infix equation
		_lowercase		:  Optional[Any]			       ="""""".join(Infix.split())  # Remove spaces from the input
		print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")
