



def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]						)	->    Tuple:
					__lowerCAmelCase						 =[]
					__lowerCAmelCase						 =[]
					__lowerCAmelCase						 ={
					    """^""": 3,
					    """*""": 2,
					    """/""": 2,
					    """%""": 2,
					    """+""": 1,
					    """-""": 1,
					}  # Priority of each operator
					__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Any						)	->    List[Any]:
					__lowerCAmelCase						 =list(infix[::-1]						)  # reverse the infix equation

					for i in range(len(__lowerCamelCase						)						):
										if infix[i] == "(":
															__lowerCAmelCase						 =""")"""  # change "(" to ")"
										elif infix[i] == ")":
															__lowerCAmelCase						 ="""("""  # change ")" to "("

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


if __name__ == "__main__":
				lowercase_													=      input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
				lowercase_													=      ''''''.join(Infix.split())  # Remove spaces from the input
				print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')









import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
				import torch

				from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class     __a			:



	def __init__(    self				:     Union[str, Any]					,						snake_case_				:     Dict					,						snake_case_				:     Tuple=None					,						snake_case_				:     List[str]=None					,						snake_case_				:     Union[str, Any]=None					,						snake_case_				:     str="resnet50"					,						snake_case_				:     List[Any]=3					,						snake_case_				:     Optional[int]=32					,						snake_case_				:     Union[str, Any]=3					,						snake_case_				:     Tuple=True					,						snake_case_				:     List[str]=True					,						)->				Optional[Any]:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =out_indices if out_indices is not None else [4]
						__lowerCAmelCase						 =stage_names
						__lowerCAmelCase						 =out_features
						__lowerCAmelCase						 =backbone
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =image_size
						__lowerCAmelCase						 =num_channels
						__lowerCAmelCase						 =use_pretrained_backbone
						__lowerCAmelCase						 =is_training



	def 		UpperCamelCase				(    self				:     int)->				Dict:
						__lowerCAmelCase						 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
						__lowerCAmelCase						 =self.get_config()

						return config, pixel_values



	def 		UpperCamelCase				(    self				:     Optional[int])->				Optional[int]:
						return TimmBackboneConfig(
						    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     int					,						snake_case_				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =TimmBackbone(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						with torch.no_grad():
											__lowerCAmelCase						 =model(snake_case_)
						self.parent.assertEqual(
						    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 14, 14)					,						)



	def 		UpperCamelCase				(    self				:     List[str])->				Union[str, Any]:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						__lowerCAmelCase ,						__lowerCAmelCase						 =config_and_inputs
						__lowerCAmelCase						 ={"""pixel_values""": pixel_values}
						return config, inputs_dict






@require_torch
@require_timm
class     __a			(  SCREAMING_SNAKE_CASE						,     SCREAMING_SNAKE_CASE						,     SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							(TimmBackbone,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE  	=							{"feature-extraction": TimmBackbone} if is_torch_available() else {}
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =TimmBackboneModelTester(self)
						__lowerCAmelCase						 =ConfigTester(self					,						config_class=snake_case_					,						has_text_modality=snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Optional[Any]:
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 ="""resnet18"""
						__lowerCAmelCase						 ="""microsoft/resnet-18"""

						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						use_timm_backbone=snake_case_)
						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_)

						self.assertEqual(len(timm_model.out_features)					,						len(transformers_model.out_features))
						self.assertEqual(len(timm_model.stage_names)					,						len(transformers_model.stage_names))
						self.assertEqual(timm_model.channels					,						transformers_model.channels)
						# Out indices are set to the last layer by default. For timm models, we don't know
						# the number of layers in advance, so we set it to (-1,), whereas for transformers
						# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
						self.assertEqual(timm_model.out_indices					,						(-1,))
						self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names) - 1])

						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						use_timm_backbone=snake_case_					,						out_indices=[1, 2, 3])
						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						out_indices=[1, 2, 3])

						self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices)
						self.assertEqual(len(timm_model.out_features)					,						len(transformers_model.out_features))
						self.assertEqual(timm_model.channels					,						transformers_model.channels)



	@unittest.skip("""TimmBackbone doesn't support feed forward chunking""")
	def 		UpperCamelCase				(    self				:     Dict)->				Any:
						pass



	@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""")
	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						pass



	@unittest.skip("""TimmBackbone initialization is managed on the timm side""")
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				List[str]:
						pass



	@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
	def 		UpperCamelCase				(    self				:     List[str])->				List[str]:
						pass



	@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
	def 		UpperCamelCase				(    self				:     int)->				int:
						pass



	@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""")
	def 		UpperCamelCase				(    self				:     Dict)->				int:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						pass



	@unittest.skip("""model weights aren't tied in TimmBackbone.""")
	def 		UpperCamelCase				(    self				:     int)->				Tuple:
						pass



	@unittest.skip("""model weights aren't tied in TimmBackbone.""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Union[str, Any]:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Optional[Any]:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Any)->				List[Any]:
						pass



	@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""")
	def 		UpperCamelCase				(    self				:     Any)->				Tuple:
						pass



	@unittest.skip("""TimmBackbone doesn't support output_attentions.""")
	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						pass



	@unittest.skip("""Safetensors is not supported by timm.""")
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[int]:
						pass



	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Tuple:
						pass



	def 		UpperCamelCase				(    self				:     int)->				Union[str, Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__lowerCAmelCase						 =model_class(snake_case_)
											__lowerCAmelCase						 =inspect.signature(model.forward)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowerCAmelCase						 =[*signature.parameters.keys()]

											__lowerCAmelCase						 =["""pixel_values"""]
											self.assertListEqual(arg_names[:1]					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase						 =True
						__lowerCAmelCase						 =self.has_attentions

						# no need to test all models as different heads yield the same functionality
						__lowerCAmelCase						 =self.all_model_classes[0]
						__lowerCAmelCase						 =model_class(snake_case_)
						model.to(snake_case_)

						__lowerCAmelCase						 =self._prepare_for_class(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =model(**snake_case_)
						__lowerCAmelCase						 =outputs[0][-1]

						# Encoder-/Decoder-only models
						__lowerCAmelCase						 =outputs.hidden_states[0]
						hidden_states.retain_grad()

						if self.has_attentions:
											__lowerCAmelCase						 =outputs.attentions[0]
											attentions.retain_grad()

						output.flatten()[0].backward(retain_graph=snake_case_)

						self.assertIsNotNone(hidden_states.grad)

						if self.has_attentions:
											self.assertIsNotNone(attentions.grad)



	def 		UpperCamelCase				(    self				:     Tuple)->				List[Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)

											self.assertEqual(len(result.feature_maps)					,						len(config.out_indices))
											self.assertEqual(len(model.channels)					,						len(config.out_indices))

											# Check output of last stage is taken if out_features=None, out_indices=None
											__lowerCAmelCase						 =copy.deepcopy(snake_case_)
											__lowerCAmelCase						 =None
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)

											self.assertEqual(len(result.feature_maps)					,						1)
											self.assertEqual(len(model.channels)					,						1)

											# Check backbone can be initialized with fresh weights
											__lowerCAmelCase						 =copy.deepcopy(snake_case_)
											__lowerCAmelCase						 =False
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)




