code: import inspect
  import unittest
  
  from transformers import SwinaSRConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
   import torch
   from torch import nn
  
   from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
   from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import SwinaSRImageProcessor
  
  class 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=13					,	lowerCamelCase__=32					,	lowerCamelCase__=1					,	lowerCamelCase__=3					,	lowerCamelCase__=16					,	lowerCamelCase__=[1, 2, 1]					,	lowerCamelCase__=[2, 2, 4]					,	lowerCamelCase__=2					,	lowerCamelCase__=2.0					,	lowerCamelCase__=True					,	lowerCamelCase__=0.0					,	lowerCamelCase__=0.0					,	lowerCamelCase__=0.1					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=0.0_2					,	lowerCamelCase__=1E-5					,	lowerCamelCase__=True					,	lowerCamelCase__=None					,	lowerCamelCase__=False					,	lowerCamelCase__=2					,	):
         lowerCAmelCase_:       Any         =  parent
         lowerCAmelCase_:       Tuple         =  batch_size
         lowerCAmelCase_:       Dict         =  image_size
         lowerCAmelCase_:       int         =  patch_size
         lowerCAmelCase_:       Any         =  num_channels
         lowerCAmelCase_:       List[str]         =  embed_dim
         lowerCAmelCase_:       Union[str, Any]         =  depths
         lowerCAmelCase_:       int         =  num_heads
         lowerCAmelCase_:       List[str]         =  window_size
         lowerCAmelCase_:       int         =  mlp_ratio
         lowerCAmelCase_:       Union[str, Any]         =  qkv_bias
         lowerCAmelCase_:       Dict         =  hidden_dropout_prob
         lowerCAmelCase_:       List[Any]         =  attention_probs_dropout_prob
         lowerCAmelCase_:       Optional[Any]         =  drop_path_rate
         lowerCAmelCase_:       Dict         =  hidden_act
         lowerCAmelCase_:       Tuple         =  use_absolute_embeddings
         lowerCAmelCase_:       str         =  patch_norm
         lowerCAmelCase_:       int         =  layer_norm_eps
         lowerCAmelCase_:       Union[str, Any]         =  initializer_range
         lowerCAmelCase_:       Optional[Any]         =  is_training
         lowerCAmelCase_:       Union[str, Any]         =  scope
         lowerCAmelCase_:       Dict         =  use_labels
         lowerCAmelCase_:       Optional[Any]         =  upscale
  
         # here we set some attributes to make tests pass
         lowerCAmelCase_:       List[str]         =  len(lowerCamelCase__   )
         lowerCAmelCase_:       List[str]         =  embed_dim
         lowerCAmelCase_:       Optional[int]         =  (image_size // patch_size) ** 2
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
  
         lowerCAmelCase_:       Optional[int]         =  None
         if self.use_labels:
            lowerCAmelCase_:       Union[str, Any]         =  ids_tensor([self.batch_size]					,	self.type_sequence_label_size   )
  
         lowerCAmelCase_:       Union[str, Any]         =  self.get_config()
  
         return config, pixel_values, labels
  
  
  
  
      def     _a						(						self   ):
         return SwinaSRConfig(
             image_size=self.image_size					,	patch_size=self.patch_size					,	num_channels=self.num_channels					,	embed_dim=self.embed_dim					,	depths=self.depths					,	num_heads=self.num_heads					,	window_size=self.window_size					,	mlp_ratio=self.mlp_ratio					,	qkv_bias=self.qkv_bias					,	hidden_dropout_prob=self.hidden_dropout_prob					,	attention_probs_dropout_prob=self.attention_probs_dropout_prob					,	drop_path_rate=self.drop_path_rate					,	hidden_act=self.hidden_act					,	use_absolute_embeddings=self.use_absolute_embeddings					,	path_norm=self.patch_norm					,	layer_norm_eps=self.layer_norm_eps					,	initializer_range=self.initializer_range					,	upscale=self.upscale					,	)
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       List[Any]         =  SwinaSRModel(config=lowerCamelCase__   )
         model.to(lowerCamelCase__   )
         model.eval()
         lowerCAmelCase_:       Tuple         =  model(lowerCamelCase__   )
  
         self.parent.assertEqual(
             result.last_hidden_state.shape					,	(self.batch_size, self.embed_dim, self.image_size, self.image_size)   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
         lowerCAmelCase_:       List[Any]         =  SwinaSRForImageSuperResolution(lowerCamelCase__   )
         model.to(lowerCamelCase__   )
         model.eval()
         lowerCAmelCase_:       str         =  model(lowerCamelCase__   )
  
         lowerCAmelCase_:       List[str]         =  self.image_size * self.upscale
         self.parent.assertEqual(
             result.reconstruction.shape					,	(self.batch_size, self.num_channels, expected_image_size, expected_image_size)   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
         lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  config_and_inputs
         lowerCAmelCase_:       Union[str, Any]         =  {"pixel_values": pixel_values}
         return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							int        =						(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      SCREAMING_SNAKE_CASE:							List[str]        =						{'feature-extraction': SwinaSRModel} if is_torch_available() else {}
  
      SCREAMING_SNAKE_CASE:							Tuple        =						False
      SCREAMING_SNAKE_CASE:							Any        =						False
      SCREAMING_SNAKE_CASE:							List[str]        =						False
      SCREAMING_SNAKE_CASE:							Tuple        =						False
      SCREAMING_SNAKE_CASE:							Any        =						False
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       List[Any]         =  SwinaSRModelTester(self   )
         lowerCAmelCase_:       Tuple         =  ConfigTester(self					,	config_class=lowerCamelCase__					,	embed_dim=37   )
  
  
  
  
      def     _a						(						self   ):
         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     _a						(						self   ):
         lowerCAmelCase_:       List[str]         =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_super_resolution(*lowerCamelCase__   )
  
  
  
  
      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."   )
      def     _a						(						self   ):
         pass
  
  
  
  
      @unittest.skip(reason="Swin2SR does not use inputs_embeds"   )
      def     _a						(						self   ):
         pass
  
  
  
  
      @unittest.skip(reason="Swin2SR does not support training yet"   )
      def     _a						(						self   ):
         pass
  
  
  
  
      @unittest.skip(reason="Swin2SR does not support training yet"   )
      def     _a						(						self   ):
         pass
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       Union[str, Any]         =  self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
            lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__   )
            self.assertIsInstance(model.get_input_embeddings()					,	(nn.Module)   )
            lowerCAmelCase_:       List[Any]         =  model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowerCamelCase__					,	nn.Linear   )   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       Any         =  self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
            lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__   )
            lowerCAmelCase_:       Any         =  inspect.signature(model.forward   )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            lowerCAmelCase_:       Tuple         =  [*signature.parameters.keys()]
  
            lowerCAmelCase_:       Optional[int]         =  ["pixel_values"]
            self.assertListEqual(arg_names[:1]					,	lowerCamelCase__   )
  
  
  
  
      @slow
      def     _a						(						self   ):
         for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowerCAmelCase_:       str         =  SwinaSRModel.from_pretrained(lowerCamelCase__   )
            self.assertIsNotNone(lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  self.model_tester.prepare_config_and_inputs_for_common()
  
         lowerCAmelCase_:       List[Any]         =  _config_zero_init(lowerCamelCase__   )
         for model_class in self.all_model_classes:
            lowerCAmelCase_:       List[str]         =  model_class(config=lowerCamelCase__   )
            for name, param in model.named_parameters():
               if "logit_scale" in name:
                  continue
               if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1E9).round() / 1E9).item()					,	[0.0, 1.0]					,	msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''					,	)
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase_:       Tuple         =  True
  
         for model_class in self.all_model_classes:
            lowerCAmelCase_:       str         =  True
            lowerCAmelCase_:       Any         =  False
            lowerCAmelCase_:       Union[str, Any]         =  True
            lowerCAmelCase_:       Any         =  model_class(lowerCamelCase__   )
            model.to(lowerCamelCase__   )
            model.eval()
            with torch.no_grad():
               lowerCAmelCase_:       int         =  model(**self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )   )
            lowerCAmelCase_:       str         =  outputs.attentions
            lowerCAmelCase_:       Any         =  len(self.model_tester.depths   )
            self.assertEqual(len(lowerCamelCase__   )					,	lowerCamelCase__   )
  
            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            lowerCAmelCase_:       Dict         =  True
            lowerCAmelCase_:       Tuple         =  config.window_size**2
            lowerCAmelCase_:       List[Any]         =  model_class(lowerCamelCase__   )
            model.to(lowerCamelCase__   )
            model.eval()
            with torch.no_grad():
               lowerCAmelCase_:       Union[str, Any]         =  model(**self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )   )
            lowerCAmelCase_:       int         =  outputs.attentions
            self.assertEqual(len(lowerCamelCase__   )					,	lowerCamelCase__   )
  
            self.assertListEqual(
                list(attentions[0].shape[-3:]   )					,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,	)
            lowerCAmelCase_:       Optional[Any]         =  len(lowerCamelCase__   )
  
            # Check attention is always last and order is fine
            lowerCAmelCase_:       Tuple         =  True
            lowerCAmelCase_:       Optional[Any]         =  True
            lowerCAmelCase_:       Union[str, Any]         =  model_class(lowerCamelCase__   )
            model.to(lowerCamelCase__   )
            model.eval()
            with torch.no_grad():
               lowerCAmelCase_:       List[Any]         =  model(**self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )   )
  
            self.assertEqual(out_len + 1					,	len(lowerCamelCase__   )   )
  
            lowerCAmelCase_:       Optional[Any]         =  outputs.attentions
  
            self.assertEqual(len(lowerCamelCase__   )					,	lowerCamelCase__   )
  
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]   )					,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,	)
  
  
  
  
  
  @require_vision
  @require_torch
  @slow
  class 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Any         =  SwinaSRImageProcessor()
         lowerCAmelCase_:       Union[str, Any]         =  SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"   ).to(lowerCamelCase__   )
  
         lowerCAmelCase_:       List[Any]         =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
         lowerCAmelCase_:       Union[str, Any]         =  processor(images=lowerCamelCase__					,	return_tensors="pt"   ).to(lowerCamelCase__   )
  
         # forward pass
         with torch.no_grad():
            lowerCAmelCase_:       int         =  model(**lowerCamelCase__   )
  
         # verify the logits
         lowerCAmelCase_:       str         =  torch.Size([1, 3, 976, 1_296]   )
         self.assertEqual(outputs.reconstruction.shape					,	lowerCamelCase__   )
         lowerCAmelCase_:       List[Any]         =  torch.tensor(
             [[0.5_4_5_8, 0.5_5_4_6, 0.5_6_3_8], [0.5_5_2_6, 0.5_5_6_5, 0.5_6_5_1], [0.5_3_9_6, 0.5_4_2_6, 0.5_6_2_1]]   ).to(lowerCamelCase__   )
         self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]					,	lowerCamelCase__					,	atol=1E-4   )   )
style_context: from .balanced_parentheses import balanced_parentheses
  from .stack import Stack
  def 		snake_case__  (		lowercase				):
     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(lowercase       ,							-1				)
  def 		snake_case__  (		lowercase				):
     if not balanced_parentheses(lowercase				):
        raise ValueError("Mismatched parentheses"				)
     lowerCAmelCase_:       Stack[str]         =  Stack()
     lowerCAmelCase_:       Union[str, Any]         =  []
     for char in expression_str:
        if char.isalpha() or char.isdigit():
           postfix.append(lowercase				)
        elif char == "(":
           stack.push(lowercase				)
        elif char == ")":
           while not stack.is_empty() and stack.peek() != "(":
              postfix.append(stack.pop()				)
           stack.pop()
        else:
           while not stack.is_empty() and precedence(lowercase				) <= precedence(stack.peek()				):
              postfix.append(stack.pop()				)
           stack.push(lowercase				)
     while not stack.is_empty():
        postfix.append(stack.pop()				)
     return " ".join(lowercase				)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   a     :			int					    =						"""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))