





import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
 from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=30				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     snake_case_=True				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=37				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=10				,     snake_case_=0.02				,     snake_case_=None				,     snake_case_=2				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							image_size
         _A     =							patch_size
         _A     =							num_channels
         _A     =							is_training
         _A     =							use_labels
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							intermediate_size
         _A     =							hidden_act
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							type_sequence_label_size
         _A     =							initializer_range
         _A     =							scope
         _A     =							encoder_stride

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




     def        lowerCAmelCase__		(    self							):
         _A     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

         _A     =							self.get_config()

         return config, pixel_values, labels




     def        lowerCAmelCase__		(    self							):
         return ViTConfig(
             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=snake_case_				,     initializer_range=self.initializer_range				,     encoder_stride=self.encoder_stride				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							ViTModel(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							ViTForMaskedImageModeling(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_							)
         self.parent.assertEqual(
             result.reconstruction.shape				,     (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

         # test greyscale images
         _A     =							1
         _A     =							ViTForMaskedImageModeling(snake_case_							)
         model.to(snake_case_							)
         model.eval()

         _A     =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.reconstruction.shape				,     (self.batch_size, 1, self.image_size, self.image_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.type_sequence_label_size
         _A     =							ViTForImageClassification(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_				,     labels=snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         _A     =							1
         _A     =							ViTForImageClassification(snake_case_							)
         model.to(snake_case_							)
         model.eval()

         _A     =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)							)




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







@require_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             ViTModel,
             ViTForImageClassification,
             ViTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__   =			(
         {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
         if is_torch_available()
         else {}
     )
     __magic_name__   =			True

     __magic_name__   =			False
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							ViTModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     has_text_modality=snake_case_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.config_tester.run_common_tests()




     @unittest.skip(reason='ViT does not use inputs_embeds'							)
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A     =							model_class(snake_case_							)
             self.assertIsInstance(model.get_input_embeddings()				,     (nn.Module)							)
             _A     =							model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case_				,     nn.Linear							)							)




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

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

             _A     =							['pixel_values']
             self.assertListEqual(arg_names[:1]				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							ViTModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)





def 				__lowerCAmelCase(       )							-> Optional[Any]:




    """simple docstring"""




    _A     =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image







@require_torch
@require_vision
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @cached_property
     def        lowerCAmelCase__		(    self							):
         return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'							) if is_vision_available() else None




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'							).to(snake_case_							)

         _A     =							self.default_image_processor
         _A     =							prepare_img()
         _A     =							image_processor(images=snake_case_				,     return_tensors='pt'							).to(snake_case_							)

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

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

         _A     =							torch.tensor([-0.2744, 0.8215, -0.0836]							).to(snake_case_							)

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




     @slow
     def        lowerCAmelCase__		(    self							):
         # ViT models have an `interpolate_pos_encoding` argument in their forward method,
         # allowing to interpolate the pre-trained position embeddings in order to use
         # the model on higher resolutions. The DINO model by Facebook AI leverages this
         # to visualize self-attention on higher resolution images.
         _A     =							ViTModel.from_pretrained('facebook/dino-vits8'							).to(snake_case_							)

         _A     =							ViTImageProcessor.from_pretrained('facebook/dino-vits8'				,     size=480							)
         _A     =							prepare_img()
         _A     =							image_processor(images=snake_case_				,     return_tensors='pt'							)
         _A     =							inputs.pixel_values.to(snake_case_							)

         # forward pass
         with torch.no_grad():
             _A     =							model(snake_case_				,     interpolate_pos_encoding=snake_case_							)

         # verify the logits
         _A     =							torch.Size((1, 3601, 384)							)
         self.assertEqual(outputs.last_hidden_state.shape				,     snake_case_							)

         _A     =							torch.tensor(
             [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]							).to(snake_case_							)

         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]				,     snake_case_				,     atol=1E-4							)							)




     @slow
     @require_accelerate
     @require_torch_gpu
     def        lowerCAmelCase__		(    self							):
         _A     =							ViTModel.from_pretrained('facebook/dino-vits8'				,     torch_dtype=torch.floataa				,     device_map='auto'							)
         _A     =							self.default_image_processor

         _A     =							prepare_img()
         _A     =							image_processor(images=snake_case_				,     return_tensors='pt'							)
         _A     =							inputs.pixel_values.to(snake_case_							)

         # forward pass to make sure inference works in fp16
         with torch.no_grad():
             _A     =							model(snake_case_							)












__A							:			Dict         =			"Alexander Joslin"

import operator as op

from .stack import Stack





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							{'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    _A     =							Stack()
    _A     =							Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_SCREAMING_SNAKE_CASE				)				)
        elif i in operators:
            # RULE 2
            operator_stack.push(_SCREAMING_SNAKE_CASE				)
        elif i == ")":
            # RULE 4
            _A     =							operator_stack.peek()
            operator_stack.pop()
            _A     =							operand_stack.peek()
            operand_stack.pop()
            _A     =							operand_stack.peek()
            operand_stack.pop()

            _A     =							operators[opr](_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
            operand_stack.push(_SCREAMING_SNAKE_CASE				)

    # RULE 5
    return operand_stack.peek()


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





