


'''simple docstring'''







import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

   from transformers import ViTMSNForImageClassification, ViTMSNModel
   from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=13				,							SCREAMING_SNAKE_CASE      :							Tuple=30				,							SCREAMING_SNAKE_CASE      :							Dict=2				,							SCREAMING_SNAKE_CASE      :							str=3				,							SCREAMING_SNAKE_CASE      :							str=True				,							SCREAMING_SNAKE_CASE      :							Dict=True				,							SCREAMING_SNAKE_CASE      :							List[Any]=32				,							SCREAMING_SNAKE_CASE      :							int=5				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=4				,							SCREAMING_SNAKE_CASE      :							Dict=37				,							SCREAMING_SNAKE_CASE      :							Dict="gelu"				,							SCREAMING_SNAKE_CASE      :							str=0.1				,							SCREAMING_SNAKE_CASE      :							List[Any]=0.1				,							SCREAMING_SNAKE_CASE      :							Tuple=10				,							SCREAMING_SNAKE_CASE      :							Dict=0.02				,							SCREAMING_SNAKE_CASE      :							int=None				,							):
           _A					:     int     = parent
           _A					:     Tuple     = batch_size
           _A					:     Union[str, Any]     = image_size
           _A					:     List[str]     = patch_size
           _A					:     Optional[Any]     = num_channels
           _A					:     Dict     = is_training
           _A					:     Union[str, Any]     = use_labels
           _A					:     str     = hidden_size
           _A					:     Tuple     = num_hidden_layers
           _A					:     Tuple     = num_attention_heads
           _A					:     Tuple     = intermediate_size
           _A					:     Union[str, Any]     = hidden_act
           _A					:     Dict     = hidden_dropout_prob
           _A					:     int     = attention_probs_dropout_prob
           _A					:     str     = type_sequence_label_size
           _A					:     List[str]     = initializer_range
           _A					:     int     = scope

           # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           _A					:     List[str]     = (image_size // patch_size) ** 2
           _A					:     int     = num_patches + 1


    def        A   (  self      :							List[str]):
           _A					:     Tuple     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _A					:     List[Any]     = None
           if self.use_labels:
                  _A					:     List[str]     = ids_tensor([self.batch_size]				,							self.type_sequence_label_size)

           _A					:     Optional[Any]     = self.get_config()

           return config, pixel_values, labels


    def        A   (  self      :							List[str]):
           return ViTMSNConfig(
               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				,							initializer_range=self.initializer_range				,							)


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     str     = ViTMSNModel(config=SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A					:     Union[str, Any]     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size))


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           _A					:     str     = self.type_sequence_label_size
           _A					:     Union[str, Any]     = ViTMSNForImageClassification(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A					:     int     = model(SCREAMING_SNAKE_CASE				,							labels=SCREAMING_SNAKE_CASE)
           print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
           print('Labels: {labels}')
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           _A					:     int     = 1
           _A					:     List[Any]     = ViTMSNForImageClassification(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           _A					:     Optional[Any]     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _A					:     str     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size))


    def        A   (  self      :							int):
           _A					:     Dict     = self.prepare_config_and_inputs()
           _A  ,			_A  ,			_A					:     int     = config_and_inputs
           _A					:     Dict     = {'pixel_values': pixel_values}
           return config, inputs_dict




@require_torch
class 			__lowerCamelCase			(	a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    a													=   (
        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    a													=   False
    a													=   False
    a													=   False
    a													=   False


    def        A   (  self      :							Union[str, Any]):
           _A					:     Optional[Any]     = ViTMSNModelTester(self)
           _A					:     List[Any]     = ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE				,							has_text_modality=SCREAMING_SNAKE_CASE				,							hidden_size=37)


    def        A   (  self      :							Optional[Any]):
           self.config_tester.run_common_tests()


    @unittest.skip(reason='ViTMSN does not use inputs_embeds')
    def        A   (  self      :							Tuple):
           pass


    def        A   (  self      :							str):
           _A  ,			_A					:     Optional[Any]     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A					:     List[Any]     = model_class(SCREAMING_SNAKE_CASE)
                  self.assertIsInstance(model.get_input_embeddings()				,							(nn.Module))
                  _A					:     str     = model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE				,							nn.Linear))


    def        A   (  self      :							int):
           _A  ,			_A					:     Union[str, Any]     = self.model_tester.prepare_config_and_inputs_for_common()

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

                  _A					:     Optional[Any]     = ['pixel_values']
                  self.assertListEqual(arg_names[:1]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]):
           _A					:     int     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]):
           _A					:     Dict     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)


    @slow
    def        A   (  self      :							str):
           for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A					:     Tuple     = ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    ):
       _A					:     Union[str, Any]     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image


@require_torch
@require_vision
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @cached_property
    def        A   (  self      :							int):
           return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None


    @slow
    def        A   (  self      :							Optional[Any]):
           torch.manual_seed(2)
           _A					:     Any     = ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(SCREAMING_SNAKE_CASE)

           _A					:     Dict     = self.default_image_processor
           _A					:     Any     = prepare_img()
           _A					:     Union[str, Any]     = image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors='pt').to(SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
                  _A					:     int     = model(**SCREAMING_SNAKE_CASE)

           # verify the logits
           _A					:     int     = torch.Size((1, 1000))
           self.assertEqual(outputs.logits.shape				,							SCREAMING_SNAKE_CASE)

           _A					:     str     = torch.tensor([-0.0803, -0.4454, -0.2375]).to(SCREAMING_SNAKE_CASE)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]				,							SCREAMING_SNAKE_CASE				,							atol=1e-4))










'''simple docstring'''







A				:					Dict    						=			'''Alexander Joslin'''

import operator as op

from .stack import Stack






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     Dict     = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

       _A					:     Stack[int]     = Stack()
       _A					:     Stack[str]     = Stack()

       for i in equation:
              if i.isdigit():
                     # RULE 1
                     operand_stack.push(int(lowerCamelCase       )       )
              elif i in operators:
                     # RULE 2
                     operator_stack.push(lowerCamelCase       )
              elif i == ")":
                     # RULE 4
                     _A					:     List[str]     = operator_stack.peek()
                     operator_stack.pop()
                     _A					:     Dict     = operand_stack.peek()
                     operand_stack.pop()
                     _A					:     Optional[int]     = operand_stack.peek()
                     operand_stack.pop()

                     _A					:     Dict     = operators[opr](lowerCamelCase  ,lowerCamelCase       )
                     operand_stack.push(lowerCamelCase       )

    # RULE 5
       return operand_stack.peek()


if __name__ == "__main__":
   A				:					Dict    						=			'''(5 + ((4 * 2) * (2 + 3)))'''
   # answer = 45
   print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")






