


'''simple docstring'''






import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor






class      SCREAMING_SNAKE_CASE_	:




			def __init__(      self					,      lowercase					,      lowercase = 1_3					,      lowercase = 6_4					,      lowercase = 2					,      lowercase = 3					,      lowercase = 3					,      lowercase = True					,      lowercase = True					,      lowercase = 1_2_8					,      lowercase=[1_6, 3_2, 6_4, 1_2_8]					,      lowercase = 7					,      lowercase = 4					,      lowercase = 3_7					,      lowercase = "gelu"					,      lowercase = 0.1					,      lowercase = 0.1					,      lowercase = 1_0					,      lowercase = 0.0_2					,      lowercase = 2					,      lowercase = 1					,      lowercase = 1_2_8					,      lowercase = [2, 2, 2, 2]					,      lowercase = 2					,      lowercase = 2					,      )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							int			   = parent
								__SCREAMING_SNAKE_CASE					:							int			   = batch_size
								__SCREAMING_SNAKE_CASE					:							List[str]			   = image_size
								__SCREAMING_SNAKE_CASE					:							int			   = patch_size
								__SCREAMING_SNAKE_CASE					:							Dict			   = num_channels
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = is_training
								__SCREAMING_SNAKE_CASE					:							Tuple			   = use_labels
								__SCREAMING_SNAKE_CASE					:							str			   = hidden_size
								__SCREAMING_SNAKE_CASE					:							str			   = num_hidden_layers
								__SCREAMING_SNAKE_CASE					:							Tuple			   = num_attention_heads
								__SCREAMING_SNAKE_CASE					:							int			   = intermediate_size
								__SCREAMING_SNAKE_CASE					:							List[str]			   = hidden_act
								__SCREAMING_SNAKE_CASE					:							Dict			   = hidden_dropout_prob
								__SCREAMING_SNAKE_CASE					:							int			   = attention_probs_dropout_prob
								__SCREAMING_SNAKE_CASE					:							Dict			   = type_sequence_label_size
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = initializer_range
								__SCREAMING_SNAKE_CASE					:							int			   = encoder_stride
								__SCREAMING_SNAKE_CASE					:							str			   = num_attention_outputs
								__SCREAMING_SNAKE_CASE					:							List[str]			   = embed_dim
								__SCREAMING_SNAKE_CASE					:							Tuple			   = embed_dim + 1
								__SCREAMING_SNAKE_CASE					:							Dict			   = resolution
								__SCREAMING_SNAKE_CASE					:							str			   = depths
								__SCREAMING_SNAKE_CASE					:							str			   = hidden_sizes
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = dim
								__SCREAMING_SNAKE_CASE					:							Tuple			   = mlp_expansion_ratio




			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[Any]			   = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

								__SCREAMING_SNAKE_CASE					:							Dict			   = None
								if self.use_labels:
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = ids_tensor([self.batch_size]					,      self.type_sequence_label_size       )

								__SCREAMING_SNAKE_CASE					:							Dict			   = self.get_config()
								return config, pixel_values, labels




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								return EfficientFormerConfig(
								    image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      is_decoder=lowercase					,      initializer_range=self.initializer_range					,      encoder_stride=self.encoder_stride					,      resolution=self.resolution					,      depths=self.depths					,      hidden_sizes=self.hidden_sizes					,      dim=self.dim					,      mlp_expansion_ratio=self.mlp_expansion_ratio					,      )




			def 			_snake_case   (      self					,      lowercase					,      lowercase					,      lowercase       )			->						Tuple:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[Any]			   = EfficientFormerModel(config=lowercase       )
								model.to(lowercase       )
								model.eval()
								__SCREAMING_SNAKE_CASE					:							Any			   = model(lowercase       )
								self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)       )




			def 			_snake_case   (      self					,      lowercase					,      lowercase					,      lowercase       )			->						List[str]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Any			   = self.type_sequence_label_size
								__SCREAMING_SNAKE_CASE					:							Tuple			   = EfficientFormerForImageClassification(lowercase       )
								model.to(lowercase       )
								model.eval()
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = model(lowercase					,      labels=lowercase       )
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)       )

								# test greyscale images
								__SCREAMING_SNAKE_CASE					:							List[str]			   = 1
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = EfficientFormerForImageClassification(lowercase       )
								model.to(lowercase       )
								model.eval()

								__SCREAMING_SNAKE_CASE					:							str			   = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = model(lowercase       )
								self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)       )




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = self.prepare_config_and_inputs()
								(
								    (
								    __SCREAMING_SNAKE_CASE
								)   ,   (
								    __SCREAMING_SNAKE_CASE
								)   ,   (
								    __SCREAMING_SNAKE_CASE
								)   ,   
								)					:							List[Any]			   = config_and_inputs
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case					,					unittest.TestCase		):
			__a					:      List[Any]					=  (
			    (
			        EfficientFormerModel,
			        EfficientFormerForImageClassificationWithTeacher,
			        EfficientFormerForImageClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__a					:      Any					=  (
			    {
			        '''feature-extraction''': EfficientFormerModel,
			        '''image-classification''': (
			            EfficientFormerForImageClassification,
			            EfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_torch_available()
			    else {}
			)
			__a					:      Optional[Any]					=  False

			__a					:      Optional[int]					=  False
			__a					:      List[Any]					=  False
			__a					:      Optional[int]					=  False




			def 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = EfficientFormerModelTester(self       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = ConfigTester(
								    self					,      config_class=lowercase					,      has_text_modality=lowercase					,      hidden_size=3_7       )




			def 			_snake_case   (      self       )			->						str:


								'''simple docstring'''






								self.config_tester.run_common_tests()




			@unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''       )
			def 			_snake_case   (      self       )			->						str:


								'''simple docstring'''






								pass




			@unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''       )
			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								pass




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Tuple			   = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__SCREAMING_SNAKE_CASE					:							int			   = model_class(lowercase       )
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = inspect.signature(model.forward       )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__SCREAMING_SNAKE_CASE					:							Dict			   = [*signature.parameters.keys()]

													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = ['''pixel_values''']
													self.assertListEqual(arg_names[:1]					,      lowercase       )




			def 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								def check_hidden_states_output(lowercase					,      lowercase					,      lowercase       ):
													__SCREAMING_SNAKE_CASE					:							Dict			   = model_class(lowercase       )
													model.to(lowercase       )
													model.eval()

													with torch.no_grad():
																		__SCREAMING_SNAKE_CASE					:							Tuple			   = model(**self._prepare_for_class(lowercase					,      lowercase       )       )

													__SCREAMING_SNAKE_CASE					:							List[str]			   = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													__SCREAMING_SNAKE_CASE					:							str			   = getattr(
													    self.model_tester					,      '''expected_num_hidden_layers'''					,      self.model_tester.num_hidden_layers + 1       )
													self.assertEqual(len(lowercase       )					,      lowercase       )

													if hasattr(self.model_tester					,      '''encoder_seq_length'''       ):
																		__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.model_tester.encoder_seq_length
																		if hasattr(self.model_tester					,      '''chunk_length'''       ) and self.model_tester.chunk_length > 1:
																							__SCREAMING_SNAKE_CASE					:							str			   = seq_length * self.model_tester.chunk_length
													else:
																		__SCREAMING_SNAKE_CASE					:							str			   = self.model_tester.seq_length

													self.assertListEqual(
													    list(hidden_states[-1].shape[-2:]       )					,      [seq_length, self.model_tester.hidden_size]					,      )

													if config.is_encoder_decoder:
																		__SCREAMING_SNAKE_CASE					:							List[str]			   = outputs.decoder_hidden_states

																		self.assertIsInstance(lowercase					,      (list, tuple)       )
																		self.assertEqual(len(lowercase       )					,      lowercase       )
																		__SCREAMING_SNAKE_CASE					:							Dict			   = getattr(self.model_tester					,      '''seq_length'''					,      lowercase       )
																		__SCREAMING_SNAKE_CASE					:							Optional[int]			   = getattr(self.model_tester					,      '''decoder_seq_length'''					,      lowercase       )

																		self.assertListEqual(
																		    list(hidden_states[-1].shape[-2:]       )					,      [decoder_seq_length, self.model_tester.hidden_size]					,      )

								__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Tuple			   = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													__SCREAMING_SNAKE_CASE					:							str			   = True
													check_hidden_states_output(lowercase					,      lowercase					,      lowercase       )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__SCREAMING_SNAKE_CASE					:							List[str]			   = True

													check_hidden_states_output(lowercase					,      lowercase					,      lowercase       )




			def 			_snake_case   (      self					,      lowercase					,      lowercase					,      lowercase=False       )			->						int:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Any			   = super()._prepare_for_class(lowercase					,      lowercase					,      return_labels=lowercase       )

								if return_labels:
													if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
																		del inputs_dict["labels"]

								return inputs_dict




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[str]			   = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase       )




			@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''       )
			def 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[Any]			   = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*lowercase       )




			def 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowercase       )




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								if not self.model_tester.is_training:
													return

								__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = self.model_tester.prepare_config_and_inputs_for_common()
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = True

								for model_class in self.all_model_classes:
													# EfficientFormerForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(lowercase       )
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																		continue
													__SCREAMING_SNAKE_CASE					:							Tuple			   = model_class(lowercase       )
													model.to(lowercase       )
													model.train()
													__SCREAMING_SNAKE_CASE					:							str			   = self._prepare_for_class(lowercase					,      lowercase					,      return_labels=lowercase       )
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = model(**lowercase       ).loss
													loss.backward()




			def 			_snake_case   (      self       )			->						Tuple:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							List[str]			   = self.model_tester.prepare_config_and_inputs_for_common()

								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = [
								    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
								    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
								    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
								]

								for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(lowercase       ),
													    ]
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																		continue

													for problem_type in problem_types:
																		with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""       ):
																							__SCREAMING_SNAKE_CASE					:							List[str]			   = problem_type['''title''']
																							__SCREAMING_SNAKE_CASE					:							List[str]			   = problem_type['''num_labels''']

																							__SCREAMING_SNAKE_CASE					:							Optional[int]			   = model_class(lowercase       )
																							model.to(lowercase       )
																							model.train()

																							__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self._prepare_for_class(lowercase					,      lowercase					,      return_labels=lowercase       )

																							if problem_type["num_labels"] > 1:
																												__SCREAMING_SNAKE_CASE					:							Tuple			   = inputs['''labels'''].unsqueeze(1       ).repeat(1					,      problem_type['''num_labels''']       )

																							__SCREAMING_SNAKE_CASE					:							Dict			   = inputs['''labels'''].to(problem_type['''dtype''']       )

																							# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																							# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																							# they have the same size." which is a symptom something in wrong for the regression problem.
																							# See https://github.com/huggingface/transformers/issues/11780
																							with warnings.catch_warnings(record=lowercase       ) as warning_list:
																												__SCREAMING_SNAKE_CASE					:							str			   = model(**lowercase       ).loss
																							for w in warning_list:
																												if "Using a target size that is different to the input size" in str(w.message       ):
																																	raise ValueError(
																																	    f"""Something is going wrong in the regression problem: intercepted {w.message}"""       )

																							loss.backward()




			@slow
			def 			_snake_case   (      self       )			->						Tuple:


								'''simple docstring'''






								for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__SCREAMING_SNAKE_CASE					:							int			   = EfficientFormerModel.from_pretrained(lowercase       )
													self.assertIsNotNone(lowercase       )




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Any			   = self.model_tester.prepare_config_and_inputs_for_common()
								__SCREAMING_SNAKE_CASE					:							Tuple			   = True

								__SCREAMING_SNAKE_CASE					:							List[str]			   = getattr(self.model_tester					,      '''seq_length'''					,      lowercase       )
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = getattr(self.model_tester					,      '''encoder_seq_length'''					,      lowercase       )
								__SCREAMING_SNAKE_CASE					:							List[str]			   = getattr(self.model_tester					,      '''key_length'''					,      lowercase       )
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = getattr(self.model_tester					,      '''chunk_length'''					,      lowercase       )

								if chunk_length is not None and hasattr(self.model_tester					,      '''num_hashes'''       ):
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = encoder_seq_length * self.model_tester.num_hashes

								for model_class in self.all_model_classes:
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = True
													__SCREAMING_SNAKE_CASE					:							Any			   = False
													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = True
													__SCREAMING_SNAKE_CASE					:							int			   = model_class(lowercase       )
													model.to(lowercase       )
													model.eval()
													with torch.no_grad():
																		__SCREAMING_SNAKE_CASE					:							List[Any]			   = model(**self._prepare_for_class(lowercase					,      lowercase       )       )
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(lowercase       )					,      self.model_tester.num_attention_outputs       )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = True
													__SCREAMING_SNAKE_CASE					:							int			   = model_class(lowercase       )
													model.to(lowercase       )
													model.eval()
													with torch.no_grad():
																		__SCREAMING_SNAKE_CASE					:							int			   = model(**self._prepare_for_class(lowercase					,      lowercase       )       )
													__SCREAMING_SNAKE_CASE					:							Tuple			   = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(lowercase       )					,      self.model_tester.num_attention_outputs       )

													if chunk_length is not None:
																		self.assertListEqual(
																		    list(attentions[0].shape[-4:]       )					,      [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]					,      )
													else:
																		self.assertListEqual(
																		    list(attentions[0].shape[-3:]       )					,      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]					,      )




def A_					(				)     ->  Optional[int]:
					__SCREAMING_SNAKE_CASE					:							Any			   = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
					return image






@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE_	( unittest.TestCase		):




			@cached_property
			def 			_snake_case   (      self       )			->						Any:


								'''simple docstring'''






								return (
								    EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''       )
								    if is_vision_available()
								    else None
								)




			@slow
			def 			_snake_case   (      self       )			->						List[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Tuple			   = EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''       ).to(
								    lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.default_image_processor
								__SCREAMING_SNAKE_CASE					:							List[str]			   = prepare_img()
								__SCREAMING_SNAKE_CASE					:							Any			   = image_processor(images=lowercase					,      return_tensors='''pt'''       ).to(lowercase       )

								# forward pass
								with torch.no_grad():
													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = model(**lowercase       )

								# verify the logits
								__SCREAMING_SNAKE_CASE					:							Dict			   = (1, 1_0_0_0)
								self.assertEqual(outputs.logits.shape					,      lowercase       )

								__SCREAMING_SNAKE_CASE					:							Any			   = torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]       ).to(lowercase       )
								self.assertTrue(torch.allclose(outputs.logits[0][:3]					,      lowercase					,      atol=1e-4       )       )




			@slow
			def 			_snake_case   (      self       )			->						Any:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = EfficientFormerForImageClassificationWithTeacher.from_pretrained(
								    '''snap-research/efficientformer-l1-300'''       ).to(lowercase       )

								__SCREAMING_SNAKE_CASE					:							Tuple			   = self.default_image_processor
								__SCREAMING_SNAKE_CASE					:							str			   = prepare_img()
								__SCREAMING_SNAKE_CASE					:							Tuple			   = image_processor(images=lowercase					,      return_tensors='''pt'''       ).to(lowercase       )

								# forward pass
								with torch.no_grad():
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = model(**lowercase       )

								# verify the logits
								__SCREAMING_SNAKE_CASE					:							Tuple			   = (1, 1_0_0_0)
								self.assertEqual(outputs.logits.shape					,      lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]       ).to(lowercase       )
								self.assertTrue(torch.allclose(outputs.logits[0][:3]					,      lowercase					,      atol=1e-4       )       )






'''simple docstring'''






from .balanced_parentheses import balanced_parentheses
from .stack import Stack


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  int:
					return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__SCREAMING_SNAKE_CASE	,   -1       )


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  str:
					if not balanced_parentheses(__SCREAMING_SNAKE_CASE       ):
										raise ValueError('''Mismatched parentheses'''       )
					__SCREAMING_SNAKE_CASE					:							Stack[str]			   = Stack()
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = []
					for char in expression_str:
										if char.isalpha() or char.isdigit():
															postfix.append(__SCREAMING_SNAKE_CASE       )
										elif char == "(":
															stack.push(__SCREAMING_SNAKE_CASE       )
										elif char == ")":
															while not stack.is_empty() and stack.peek() != "(":
																				postfix.append(stack.pop()       )
															stack.pop()
										else:
															while not stack.is_empty() and precedence(__SCREAMING_SNAKE_CASE       ) <= precedence(stack.peek()       ):
																				postfix.append(stack.pop()       )
															stack.push(__SCREAMING_SNAKE_CASE       )
					while not stack.is_empty():
										postfix.append(stack.pop()       )
					return " ".join(__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						_A      =     """a+b*(c^d-e)^(f+g*h)-i"""

						print("""Infix to Postfix Notation demonstration:\n""")
						print("""Infix notation: """ + expression)
						print("""Postfix notation: """ + infix_to_postfix(expression))


